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1
Sharma, Girraj, and Ritu Sharma. "Performance improvement of CSS over imperfect reporting using diversity reception in cognitive radio networks." World Journal of Engineering 16, no. 1 (2019): 87–93. http://dx.doi.org/10.1108/wje-09-2017-0288.
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Purpose This paper aims to discuss over imperfect reporting channel the performance of cooperative spectrum sensing (CSS). It is investigated that imperfect reporting channel introduces some lower bound in false alarm probability (Pf). The lower bound of probability of false alarm linearly increases with the probability of reporting error. Design/methodology/approach To solve this problem, a transmit diversity-based CSS method is proposed, and to improve the detection performance, square law selection (SLS) diversity is used. Findings It is observed that detection probability increases up to 11.55 per cent when SLS diversity is applied, and lower bound Qf decreases up to 80 per cent when transmit diversity is applied. Originality/value No literature is available to the best of the authors’ knowledge that measures the performance of CSS with respect to parameters as reported in this paper.
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Bhat, Nayeem Ahmad, and Sheikh Umar Farooq. "Local modeling approach for cross-project defect prediction." Intelligent Decision Technologies 15, no. 4 (2022): 623–37. http://dx.doi.org/10.3233/idt-210130.
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Prediction approaches used for cross-project defect prediction (CPDP) are usually impractical because of high false alarms, or low detection rate. Instance based data filter techniques that improve the CPDP performance are time-consuming and each time a new test set arrives for prediction the entire filter procedure is repeated. We propose to use local modeling approach for the utilization of ever-increasing cross-project data for CPDP. We cluster the cross-project data, train per cluster prediction models and predict the target test instances using corresponding cluster models. Over 7 NASA Data sets performance comparison using statistical methods between within-project, cross-project, and our local modeling approach were performed. Compared to within-project prediction the cross-project prediction increased the probability of detection (PD) associated with an increase in the probability of false alarm (PF) and decreased overall performance Balance. The application of local modeling decreased the (PF) associated with a decrease in (PD) and an overall performance improvement in terms of Balance. Moreover, compared to one state of the art filter technique – Burak filter, our approach is simple, fast, performance comparable, and opens a new perspective for the utilization of ever-increasing cross-project data for defect prediction. Therefore, when insufficient within-project data is available we recommend training local cluster models than training a single global model on cross-project datasets.
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N., Armi, Gharibi W., and Z. Khan W. "Error rate detection due to primary user emulation attack in cognitive radio networks." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 5385–91. https://doi.org/10.11591/ijece.v10i5.pp5385-5391.
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Security threat is a crucial issue in cognitive radio network (CRN). These threats come from physical layer, data link layer, network layer, transport layer, and application layer. Hence, security system to all layers in CRN has a responsibility to protect the communication between among Secondary User (SU) or to maintain valid detection to the presence of Primary User (PU) signals. Primary User Emulation Attack (PUEA) is a threat on physical layer where malicious user emulates PU signal. This paper studies the effect of exclusive region of PUEA in CRN. We take two setting of exclusive distances, 30m and 50m, where this radius of area is free of malicious users. Probability of false alarm (Pf) and miss detection (Pm) are used to evaluate the performances. The result shows that increasing distance of exclusive region may decrease Pf and Pm.
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Armi, N., W. Gharibi, and W. Z. Khan. "Error rate detection due to primary user emulation attack in cognitive radio networks." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 5385. http://dx.doi.org/10.11591/ijece.v10i5.pp5385-5391.
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Security threat is a crucial issue in cognitive radio network (CRN). These threats come from physical layer, data link layer, network layer, transport layer, and application layer. Hence, security system to all layers in CRN has a responsibility to protect the communication between among Secondary User (SU) or to maintain valid detection to the presence of Primary User (PU) signals. Primary User Emulation Attack (PUEA) is a threat on physical layer where malicious user emulates PU signal. This paper studies the effect of exclusive region of PUEA in CRN. We take two setting of exclusive distances, 30m and 50m, where this radius of area is free of malicious users. Probability of false alarm (Pf) and miss detection (Pm) are used to evaluate the performances. The result shows that increasing distance of exclusive region may decrease Pf and Pm.
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Guo, Mengxi. "Cooperative Spectrum Sensing for IoT System." Journal of Physics: Conference Series 2547, no. 1 (2023): 012019. http://dx.doi.org/10.1088/1742-6596/2547/1/012019.
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Abstract This paper it mainly writes how to make better use of spectrum and propose different algorithms to reduce excess spectrum waste in the case of spectrum shortage. Using Randomly distribute PU and SU’s location in certain area, with the help of graphs mark the PU and SU’s, we calculate the SNR considering channel fading, using formula to find the path loss. To perform energy detection, we generated an observation period of H1 signals and run simulations and evaluated it based on the graph of probability of detection (Pd) vs probability of false alarm (Pf). We also made some improvement on the algorithm to deal with sudden impulsive change in the environment variables. We subsequently used OR rule to decide presence of PUs. To get good performance and affordable work, we divided PU sensors into groups to detect energy of different PUs. When grouping according to SNR between PU and SU pairs, we achieved stable performance and less burden.
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P. Herath, Sanjeewa, Nandana Rajatheva, Tho Le-Ngoc, and Chintha Tellambura. "Energy Detection with Diversity Reception." Journal of Science and Technology: Issue on Information and Communications Technology 3, no. 1 (2017): 20. http://dx.doi.org/10.31130/jst.2017.34.
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We characterize the performance of energy detector (ED) over square-law, square-law selection, and switch-and-stay diversity combining schemes. The exact average probabilities of a miss (Pm), and a false alarm (Pf) are derived in closed-form. To derive Pm for versatile Nakagami-m and Rician fading channels, a twofold approach, using the probability density function (PDF) and the moment generating function (MGF), is applied. Using the PDF method, the achievable diversity order over the Nakagami-m channel is derived. However, this method becomes intractable when analyzing Pm of the aforementioned combiners in Rician channels, but the MGF method can handle this case. Our analysis helps to quantify the performance gains of ED due to diversity reception. Theoretical derivations are verified through numerical Monte-Carlo simulation results.
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Khalil, Gaith, Ayoob Aziz, and Zozan Ayoub. "Spectrum Sensing Using Cooperative Matched Filter Detector in Cognitive Radio." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 15, no. 2 (2024): 6–29. http://dx.doi.org/10.61841/turcomat.v15i2.14632.
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The vast rise in the number of internet-connected devices necessitates a more accessible spectrum. As a result, Cognitive Radio was already proposed as a solution to the problem of restricted spectrum resources by utilizing available spectrum which is assigned to primary users. This method allows the secondary user to utilize the spectrum whenever the primary user is not using it, and it does so without intruding with the primary user. Whenever the secondary user detects the spectrum, it faces many issues, such as complexity in sensing, leading to a lack of noise value, and the primary user is hidden to all secondary users. In order to tackle these challenges, many spectrum sensing frameworks were introduced in the literature. In this paper, an adaptive threshold matched filter detector and a cooperative matched filter detector frameworks are utilized to detect the spectrum and resolve the issues above. The probability of detection (Pd), probability of miss detection (Pm), and probability of false alarm (Pf) are the metrics used to assess sensing accuracy. To simulate suggested detectors results and proficiency, the MATLAB R2020a software was utilized. In comparison to earlier studies, the simulation conclusions reveal that the detection process starts with lower SNR values compared to previous work.
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Al-Saggaf, Ubaid M., Jawwad Ahmad, Mohammed A. Alrefaei, and Muhammad Moinuddin. "Optimized Statistical Beamforming for Cooperative Spectrum Sensing in Cognitive Radio Networks." Mathematics 11, no. 16 (2023): 3533. http://dx.doi.org/10.3390/math11163533.
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In cognitive radio (CR), cooperative spectrum sensing (CSS) employs a fusion of multiple decisions from various secondary user (SU) nodes at a central fusion center (FC) to detect spectral holes not utilized by the primary user (PU). The energy detector (ED) is a well-established technique of spectrum sensing (SS). However, a major challenge in designing an energy detector-based SS is the requirement of correct knowledge for the distribution of decision statistics. Usually, the Gaussian assumption is employed for the received statistics, which is not true in real practice, particularly with a limited number of samples. Another big challenge in the CSS task is choosing an optimal fusion strategy. To tackle these issues, we have proposed a beamforming-assisted ED with a heuristic-optimized CSS technique that utilizes a more accurate distribution of decision statistics by employing the characterization of the indefinite quadratic form (IQF). Two heuristic algorithms, genetic algorithm with multi-parent crossover (GA-MPC) and constriction factor particle swarm-based optimization (CF-PSO), are developed to design optimum beamforming and optimum fusion weights that can maximize the global probability of detection pd while constraining the global probability of false alarm pf to below a required level. The simulation results are presented to validate the theoretical findings and to asses the performance of the proposed algorithm.
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Raza, Ahmad, Mohsin Ali, Muhammad Khurram Ehsan, and Ali Hassan Sodhro. "Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach." Sensors 23, no. 17 (2023): 7456. http://dx.doi.org/10.3390/s23177456.
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The rapid technological advancements in the current modern world bring the attention of researchers to fast and real-time healthcare and monitoring systems. Smart healthcare is one of the best choices for this purpose, in which different on-body and off-body sensors and devices monitor and share patient data with healthcare personnel and hospitals for quick and real-time decisions about patients’ health. Cognitive radio (CR) can be very useful for effective and smart healthcare systems to send and receive patient’s health data by exploiting the primary user’s (PU) spectrum. In this paper, tree-based algorithms (TBAs) of machine learning (ML) are investigated to evaluate spectrum sensing in CR-based smart healthcare systems. The required data sets for TBAs are created based on the probability of detection (Pd) and probability of false alarm (Pf). These data sets are used to train and test the system by using fine tree, coarse tree, ensemble boosted tree, medium tree, ensemble bagged tree, ensemble RUSBoosted tree, and optimizable tree. Training and testing accuracies of all TBAs are calculated for both simulated and theoretical data sets. The comparison of training and testing accuracies of all classifiers is presented for the different numbers of received signal samples. Results depict that optimizable tree gives the best accuracy results to evaluate the spectrum sensing with minimum classification error (MCE).
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Saad, Mohammed Ayad, Mustafa S. T, Mohammed Hussein Ali, M. M. Hashim, Mahamod Bin Ismail, and Adnan H. Ali. "Spectrum sensing and energy detection in cognitive networks." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 1 (2020): 464. http://dx.doi.org/10.11591/ijeecs.v17.i1.pp464-471.
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<span>It is worth mentioning that the use of wireless systems has been increased in recent years and supposed to highly increase in the few coming years because of the increasing demands of wireless applications such as mobile phones, Internet of Things (IoT), wireless sensors networks (WSNs), mobile applications and tablets. The scarcity of spectrum needs to be into consideration when designing a wireless system specially to answer the two following questions; how to use efficiently the spectrum available for the available networks in sharing process and how to increase the throughput delivered to the serving users. The spectrum sharing between several types of wireless networks where networks are called cognitive networks is used to let networks cooperate with each other by borrowing some spectrum bands between them especially when there is an extra band that is not used. In this project, the simulation of spectrum sensing and sharing in cognitive networks is performed between two cognitive networks. This project discusses the performance of probability of energy detected (Pd) with different values of false alarm (Pf) and Signal-To-Noise Ratio (SNR) values to evaluate the performance of the sensing and sharing process in cognitive networks. The results show that when the request of sharing spectrum increased, the full sharing process occurs for a long time and the error rate decreases for small values of SNR.</span>
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Mohammed, Ayad Saad, S. T. Mustafa, Hussein Ali Mohammed, M. Hashim M., Bin Ismail Mahamod, and H. Ali Adnan. "Spectrum sensing and energy detection in cognitive networks." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 17, no. 1 (2021): 465–72. https://doi.org/10.5281/zenodo.5242967.
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It is worth mentioning that the use of wireless systems has been increased in recent years and supposed to highly increase in the few coming years because of the increasing demands of wireless applications such as mobile phones, Internet of Things (IoT), wireless sensor networks (WSNs), mobile applications and tablets. The scarcity of spectrum needs to be into consideration when designing a wireless system specially to do the two following questions; how to utilize efficiently the spectrum available for the available networks in sharing process and how to increase the throughput delivered to the serving users. The spectrum sharing between several types of wireless networks where networks are called cognitive networks is used to let networks cooperate with each other by borrowing some spectrum bands between them especially when there is an extra band that is not used. In this project, the simulation of spectrum sensing and sharing in cognitive networks is performed between two cognitive networks. This project discusses the performance of probability of energy detected (Pd) with different values of false alarm (Pf) and Signal-To-Noise Ratio (SNR) values to evaluate the performance of the sensing and sharing process in cognitive networks. The results show that when the request of sharing spectrum increased, the full sharing process occurs for a long time and the error rate decreases for small values of SNR.
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Chang, Chein-I., Shuhan Chen, Shengwei Zhong, and Yidan Shi. "Exploration of Data Scene Characterization and 3D ROC Evaluation for Hyperspectral Anomaly Detection." Remote Sensing 16, no. 1 (2023): 135. http://dx.doi.org/10.3390/rs16010135.
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Whether or not a hyperspectral anomaly detector is effective is determined by two crucial issues, anomaly detectability and background suppressibility (BS), both of which are very closely related to two factors, the datasets used for a selected hyperspectral anomaly detector and detection measures used for its performance evaluation. This paper explores how anomaly detectability and BS play key roles in hyperspectral anomaly detection (HAD). To address these two issues, we investigate three key elements attributed to HAD. One is a selected hyperspectral anomaly detector, and another is the datasets used for experiments. The third one is the detection measures used to evaluate the effectiveness of a hyperspectral anomaly detector. As for hyperspectral anomaly detectors, twelve commonly used anomaly detectors were evaluated and compared. To address the appropriate use of datasets for HAD, seven popular and widely used datasets were studied for HAD. As for the third issue, the traditional area under a receiver operating characteristic (ROC) curve of detection probability—PD versus false alarm probability, PF, (AUC(D,F))—was extended to 3D ROC analysis where a 3D ROC curve was developed to generate three 2D ROC curves from which eight detection measures could be derived to evaluate HAD in all round aspects, including anomaly detectability, BS and joint anomaly detectability and BS. Qualitative analysis showed that many works reported in the literature which claimed that their developed hyperspectral anomaly detectors performed better than other anomaly detectors are actually not true because they overlooked these two issues. Specifically, a comprehensive study via extensive experiments demonstrated that these 3D ROC curve-derived detection measures can be further used to address the various characterizations of different data scenes and also to provide explanations as to why certain data scenes are not suitable for HAD.
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Barnes, Lindsey R., David M. Schultz, Eve C. Gruntfest, Mary H. Hayden, and Charles C. Benight. "CORRIGENDUM: False Alarm Rate or False Alarm Ratio?" Weather and Forecasting 24, no. 5 (2009): 1452–54. http://dx.doi.org/10.1175/2009waf2222300.1.
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Abstract Two items need to be clarified from an earlier work of the authors. The first is that the layout of the 2 × 2 contingency table was reversed from standard practice, with the titles of “observed event” and “forecast” transposed. The second is that FAR should have represented “false alarm ratio,” not “false alarm rate.” Unfortunately, the terminology used in the atmospheric sciences is confusing, with authors as early as 1965 having used the terminology differently from currently accepted practice. More recent studies are not much better. A survey of peer-reviewed articles published in American Meteorological Society journals between 2001 and 2007 found that, of 26 articles using those terms, 10 (38%) used them inconsistently with the currently accepted definitions. This article recommends that authors make explicit how their verification statistics are calculated in their manuscripts and consider using the terms probability of false detection and probability of false alarm instead of false alarm rate and false alarm ratio.
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Qing, Chao Jin, Jin Cheng Wei, Ling Xia, Ying Liu, Chuan Hui Ma, and You Xi Tang. "Timing Acquisition with Cooperation of Two Distributed Receive Antennas over Flat-Fading Channels." Applied Mechanics and Materials 347-350 (August 2013): 1965–69. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.1965.
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In the flat Rayleigh channels of linear cell, two distributed receive antennas are employed to receive the signal transmitted from the mobile station (MS) with a single antenna. We exploit the false alarm probability at the central processor to guarantee that the false alarm probability at each distributed antenna does not exceed the pre-defined probability of false alarm. Based on the exploited probability of false alarm at the central processor, a cooperative detection threshold of each antenna is derived for threshold detection. According to the threshold detection, a maximum-likelihood (ML)-based timing acquisition method is proposed for distributed antenna systems (DAS). Without increasing the pre-defined probability of false alarm, the analysis and simulation results show that the correct acquisition probability and the missed detection probability for each distributed antenna can be improved with the proposed method wherever the MS is located.
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Xiao, Jun, Deng Yu Li, Xiao Xu Leng, and Jiao Rao Su. "False Alarm and Missing Alarm Models of Multiple Bits Watermarking." Applied Mechanics and Materials 543-547 (March 2014): 2155–58. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2155.
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False alarm and missing alarm are two of the most important performances for multiple bits watermarking systems. In this paper, we study false alarm and missing alarm probability models when multiple watermarks or multiple bits watermarks embedded. We derive the false alarm and missing alarm probability models for dither modulation from the detection principle of the detectors. The theoretical results are compared with the experimental results obtained in the case of random work and watermark, and the comparison validates the accuracy of the models, and it also shows that random work and watermark have little influence on the false alarm and missing alarm probabilities, and this is the same with the situation when only one bit watermark is embedded by DM.
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Wang, Jian, Haisen Li, Guanying Huo, Chao Li, and Yuhang Wei. "A Multi-Beam Seafloor Constant False Alarm Detection Method Based on Weighted Element Averaging." Journal of Marine Science and Engineering 11, no. 3 (2023): 513. http://dx.doi.org/10.3390/jmse11030513.
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Due to the influence of environmental noise, sidelobe data, and tunnel emission under the background of multi-background underwater surveying and mapping, it is challenging to detect seafloor terrain in the background noise. Constant false alarm detection of seafloor terrain under the condition of constant false alarm probability has been an important research field. The constant false alarm detection can eliminate noise interference in a water body in the seabed topography mapping process and provide clear and accurate seabed topography information. Therefore, it is a challenging task to increase the detection probability, reduce the missing probability, and increase the detection speed in constant false alarm detection methods. Aiming at the shortcomings of the existing algorithms, this paper proposes an efficient weighted cell averaged constant false alarm detection method (WCA-CFAR). First, the cross-window reference unit sampling method is used to improve the detection speed and accurately sample the background noise unit. Then, the reference unit weighted average constant false alarm detection method is employed to calculate the detection threshold to achieve the purpose of target detection. The proposed method is verified by the simulation data detection test and a test on the actual lake test data. The test results show that the proposed method can effectively reduce the missing detection probability and improve the detection probability.
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Yang, Yuyao, and Chunbo Xiu. "Adaptive Constant False Alarm Detector Based on Composite Fuzzy Fusion Rules." Applied Sciences 15, no. 2 (2025): 942. https://doi.org/10.3390/app15020942.
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In order to improve the detection performance of the radar constant false alarm detector in a multiple-target environment, a Kaigh–Lachenbruch Quantile constant false alarm rate detector based on composite fuzzy fusion rules (CFKLQ-CFAR) is designed by combining fuzzy fusion rules and the Kaigh–Lachenbruch Quantile constant false alarm rate detector. Two sensors are used to collect environmental information, and the membership function value is calculated based on the collected information. Furthermore, the presence or absence of the target is judged compositely by four fuzzy fusion rules. CFKLQ-CFAR is applied to the variability index CFAR (VI-CFAR) detector, and an adaptive constant false alarm rate detector based on the composite fuzzy fusion rules (CFVI-CFAR) is designed to improve the performance of the radar constant false alarm detector in different environments. The simulation experiment results show that the average detection probability of CFKLQ-CFAR is 2.67% and 1.00% higher than that of KLQ-CFAR and the fuzzy logic fusion detector (FUMCA-CFAR) in a multiple-target environment. The average detection probability of CFVI-CFAR is 3.66% higher than that of the variability index heterogeneous clutter estimate modified ordered statistics CFAR (VIHCEMOS-CFAR) in a multiple-target environment, while in a clutter edge environment, the average false alarm probability of CFVI-CFAR is only 1.65% of that of VIHCEMOS-CFAR. Therefore, the performance of the radar constant false alarm detector has been effectively improved.
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Li, Ming, Chi-Hung Chi, Weijia Jia, et al. "Decision Analysis of Statistically Detecting Distributed Denial-of-Service Flooding Attacks." International Journal of Information Technology & Decision Making 02, no. 03 (2003): 397–405. http://dx.doi.org/10.1142/s0219622003000720.
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There are two statistical decision making questions regarding statistically detecting sings of denial-of-service flooding attacks. One is how to represent the distributions of detection probability, false alarm probability and miss probability. The other is how to quantitatively express a decision region within which one may make a decision that has high detection probability, low false alarm probability and low miss probability. This paper gives the answers to the above questions. In addition, a case study is demonstrated.
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Rébillat, Marc, Ouadie Hmad, Farid Kadri, and Nazih Mechbal. "Peaks Over Threshold–based detector design for structural health monitoring: Application to aerospace structures." Structural Health Monitoring 17, no. 1 (2017): 91–107. http://dx.doi.org/10.1177/1475921716685039.
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Structural health monitoring offers new approaches to interrogate the integrity of complex structures. The structural health monitoring process classically relies on four sequential steps: damage detection, localization, classification, and quantification. The most critical step of such process is the damage detection step since it is the first one and because performances of the following steps depend on it. A common method to design such a detector consists of relying on a statistical characterization of the damage indexes available in the healthy behavior of the structure. On the basis of this information, a decision threshold can then be computed in order to achieve a desired probability of false alarm. To determine the decision threshold corresponding to such desired probability of false alarm, the approach considered here is based on a model of the tail of the damage indexes distribution built using the Peaks Over Threshold method extracted from the extreme value theory. This approach of tail distribution estimation is interesting since it is not necessary to know the whole distribution of the damage indexes to develop a detector, but only its tail. This methodology is applied here in the context of a composite aircraft nacelle (where desired probability of false alarm is typically between 10−4 and 10−9) for different configurations of learning sample size and probability of false alarm and is compared to a more classical one which consists of modeling the entire damage indexes distribution by means of Parzen windows. Results show that given a set of data in the healthy state, the effective probability of false alarm obtained using the Peaks Over Threshold method is closer to the desired probability of false alarm than the one obtained using the Parzen-window method, which appears to be more conservative.
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Simmons, Kevin M., and Daniel Sutter. "False Alarms, Tornado Warnings, and Tornado Casualties." Weather, Climate, and Society 1, no. 1 (2009): 38–53. http://dx.doi.org/10.1175/2009wcas1005.1.
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Abstract This paper extends prior research on the societal value of tornado warnings to the impact of false alarms. Intuition and theory suggest that false alarms will reduce the response to warnings, yet little evidence of a “false alarm effect” has been unearthed. This paper exploits differences in the false-alarm ratio across the United States to test for a false-alarm effect in a regression model of tornado casualties from 1986 to 2004. A statistically significant and large false-alarm effect is found: tornadoes that occur in an area with a higher false-alarm ratio kill and injure more people, everything else being constant. The effect is consistent across false-alarm ratios defined over different geographies and time intervals. A one-standard-deviation increase in the false-alarm ratio increases expected fatalities by between 12% and 29% and increases expected injuries by between 14% and 32%. The reduction in the national tornado false-alarm ratio over the period reduced fatalities by 4%–11% and injuries by 4%–13%. The casualty effects of false alarms and warning lead times are approximately equal in magnitude, suggesting that the National Weather Service could not reduce casualties by trading off a higher probability of detection for a higher false-alarm ratio, or vice versa.
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Abdulhamid, Mohanad. "Review on The Performance of Softened Two-Bit Hard Combination Scheme for Cooperative Spectrum Sensing." Technological Engineering 15, no. 1 (2018): 51–54. http://dx.doi.org/10.1515/teen-2018-0010.
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Abstract This paper measures the performance of cooperative spectrum sensing, over Rayleigh fading channel and additive white Gaussian noise, based on softened two-bit hard combination scheme. Two measures based on energy detection are considered including effect of false alarm probability, and effect of number of users. Simulation results show that the detection probability increases with the increase of false alarm probability, number of users, and signal-to-noise-ratio.
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Han, Qi Wei, Ting Huang, Fei Qiang Chen, Jun Wei Nie, and Fei Xue Wang. "A GNSS Interference Monitoring Method with Low False Alarm and Low Missed Detection Probability." Applied Mechanics and Materials 333-335 (July 2013): 605–10. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.605.
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Interference monitoring and analysis for GNSS frequency bands plays an important role in construction and development of satellite navigation systems, which can promote interference source locating, and has much benefit for system construction and the development of anti-jamming equipments. Due to high satellite orbits, GNSS signals reached the ground are very weak and submerged below the thermal noise, which makes it vulnerable to interference. Interference sources for satellite navigation system require only a small transmission power; however, a significant interference effect can be obtained. Therefore, a high sensitivity is needed by interference monitoring for satellite navigation system. The interference judgment threshold is close to thermal noise power, which often causes a higher probability of false alarm. It is very important to reduce the probability of false alarm at the same time to ensure high sensitivity. In this paper, a high sensitivity (low missed detection probability) and low false alarm interference monitoring method is proposed, a dual decision threshold is designed, thus the probability of false alarm can be effectively reduced at the same time of identifying interference accurately. The experimental results demonstrated the effectiveness of the algorithm.
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Isrorudin, Isrorudin, Agus Maulana, Asep Maryono, Fergyanto E. Gunawan, and Muhammad Asrol. "An AN ANALYSIS OF EMERGENCY RESPONSE COSTS DUE TO FALSE ALARM SYSTEM." JARES (Journal of Academic Research and Sciences) 7, no. 1 (2022): 1–10. http://dx.doi.org/10.35457/jares.v7i1.1665.
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Losses from fire events can be minimized if the fire detection and alarm system installed in an area function properly. The conditions will be different if an alarm that sounds is not the result of real conditions that can trigger a fire - also known as a false alarm. The false alarm condition gives a loss to the company. In this research, an analysis and comparison of costs in the detection and alarm systems conducted on the existing company fires with the same risk of false alarms, repairs fire detection and alarm system, and investment costs. The probability of a false alarm calculated in the existing condition, to know the potential losses charged to the company due to unnecessary emergency response activities. As well, investment costs were analysed to improve the performance of the system. Two alternative conditions were found to improve company’s performance.
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Suseela, B., and D. Sivakumar. "Cognitive Radio and Its Impact of Throughput with Channel Optimization Techniques." Journal of Computational and Theoretical Nanoscience 14, no. 1 (2017): 430–34. http://dx.doi.org/10.1166/jctn.2017.6339.
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Spectrum scarcity has gained a great challenge in the current scenarios of wireless communication. In order to optimize the spectrum usage on the other hand cognitive networks has shown a considerable growth. This paper tries to focus on optimization with particle swarm optimization in cognitive networks (PSO-CN) and tree seed algorithm in cognitive networks (TSA-CN) which are multichannel based. The algorithm is based on higher probability of detection and throughput with lower probability of false alarm. The lower probability of false alarm has been achieved without compromising on the transmission rate with TSA-CN. The convergence time is found to be quicker with TSA-CN. Results with matlab based simulator shows there is an increase in throughput and decrease in false alarm with TSA algorithm than the PSO algorithm.
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Yang, Bo Fan, Rui Wang, Gang Wang, and Li Zhao. "Multi-Sensor N-P Criterion Fusion Detection Based on Weighting by SNR." Advanced Materials Research 1044-1045 (October 2014): 818–24. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.818.
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Aiming at signal detection of radar target, concerning about on the basis of the influence of SNR on detection probability when false alarm probability is given based on N-P criterion, a kind of multi-sensor fusion detection based on SNR is put forward. It can improve system’s detection probability under the condition of required false alarm probability in the detection of low SNR signal. The simulation results show that the detection performance is significantly increased, no matter fusion detection system is composed of same sensors working in the same working point or different sensors.
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Hu, Xiao-Li, Pin-Han Ho, and Limei Peng. "Statistical Properties of Energy Detection for Spectrum Sensing by Using Estimated Noise Variance." Journal of Sensor and Actuator Networks 8, no. 2 (2019): 28. http://dx.doi.org/10.3390/jsan8020028.
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In energy detection for cognitive radio spectrum sensing, the noise variance is usually assumed given, by which a threshold is set to guarantee a desired constant false alarm rate (CFAR) or a constant detection rate (CDR). However, in practical situations, the exact information of noise variance is generally unavailable to a certain extent due to the fact that the total noise consists of time-varying thermal noise, receiver noise, and environmental noise, etc. Hence, setting the thresholds by using an estimated noise variance may result in different false alarm probabilities from the desired ones. In this paper, we analyze the basic statistical properties of the false alarm probability by using estimated noise variance, and propose a method to obtain more suitable CFAR thresholds for energy detection. Specifically, we first come up with explicit descriptions on the expectations of the resultant probability, and then analyze the upper bounds of their variance. Based on these theoretical preparations, a new method for precisely obtaining the CFAR thresholds is proposed in order to assure that the expected false alarm probability can be as close to the predetermined as possible. All analytical results derived in this paper are testified by corresponding numerical experiments.
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Echard, J. D. "Estimation of radar detection and false alarm probability." IEEE Transactions on Aerospace and Electronic Systems 27, no. 2 (1991): 255–60. http://dx.doi.org/10.1109/7.78300.
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Huang, Di, Xian Zhu, Yi Gong, and Fuhu Chen. "A Combined Passive Automatic Detection Method of Constant False Alarms and Multiple Nodes." Journal of Physics: Conference Series 2458, no. 1 (2023): 012035. http://dx.doi.org/10.1088/1742-6596/2458/1/012035.
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Abstract Aiming at the problem of whether multi-array passive joint detection can improve the detection performance of sonar, this paper analyzes in detail the relationship between the detection probability, false alarm probability, the input signal-to-noise ratio, and integration time under the condition of single array detection, and then obtains the input signal-to-noise ratio and integration time required to achieve the same detection probability and false alarm probability under different joint detection rules. It is concluded that the multi-array joint detection can reduce the required input signal-to-noise ratio and integration time and provide detection performance, which is verified by simulation experiments.
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Al-Rawi, M. "Performance measurement of one-bit hard decision fusion scheme for cooperative spectrum sensing in CR." International Review of Applied Sciences and Engineering 8, no. 1 (2017): 9–16. http://dx.doi.org/10.1556/1848.2017.8.1.3.
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This paper measures the performance of cooperative spectrum sensing, over Rayleigh-fading channel and additive white Gaussian noise, based on one-bit hard decision scheme for both AND and OR rules. Three measures based on energy detection are considered including effect of false alarm probability, effect of number of users, and effect of number of samples. Simulation results show that the detection probability increases with increasing false alarm probability, number of users, and number of samples for both AND and OR rules. Also, the performance of OR rule is better than the performance of AND rule.
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Thompson, W. Burt. "Alpha Is Not the False Alarm Rate: An Activity to Dispel a Common Statistical Misconception." Teaching of Psychology 46, no. 1 (2018): 72–79. http://dx.doi.org/10.1177/0098628318816156.
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When a psychologist announces a new research finding, it is often based on a rejected null hypothesis. However, if that hypothesis is true, the claim is a false alarm. Many students mistakenly believe that the probability of committing a false alarm equals alpha, the criterion for statistical significance, which is typically set at 5%. Instructors should take specific steps to dispel this belief because it leads students to misinterpret statistical test results and it reinforces the more general misconception that results can be interpreted in isolation, without reference to theory or prior research. In the present study, students worked with a web app that shows how the false alarm rate is a function of the prior probability of an effect, statistical power, and alpha. Quiz scores suggest the activity helps correct the misconception, which can improve how students conduct and interpret research.
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Fernandes, Chrystinne, Simon Miles, and Carlos José Pereira Lucena. "Detecting False Alarms by Analyzing Alarm-Context Information: Algorithm Development and Validation." JMIR Medical Informatics 8, no. 5 (2020): e15407. http://dx.doi.org/10.2196/15407.
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Background Although alarm safety is a critical issue that needs to be addressed to improve patient care, hospitals have not given serious consideration about how their staff should be using, setting, and responding to clinical alarms. Studies have indicated that 80%-99% of alarms in hospital units are false or clinically insignificant and do not represent real danger for patients, leading caregivers to miss relevant alarms that might indicate significant harmful events. The lack of use of any intelligent filter to detect recurrent, irrelevant, and/or false alarms before alerting health providers can culminate in a complex and overwhelming scenario of sensory overload for the medical team, known as alarm fatigue. Objective This paper’s main goal is to propose a solution to mitigate alarm fatigue by using an automatic reasoning mechanism to decide how to calculate false alarm probability (FAP) for alarms and whether to include an indication of the FAP (ie, FAP_LABEL) with a notification to be visualized by health care team members designed to help them prioritize which alerts they should respond to next. Methods We present a new approach to cope with the alarm fatigue problem that uses an automatic reasoner to decide how to notify caregivers with an indication of FAP. Our reasoning algorithm calculates FAP for alerts triggered by sensors and multiparametric monitors based on statistical analysis of false alarm indicators (FAIs) in a simulated environment of an intensive care unit (ICU), where a large number of warnings can lead to alarm fatigue. Results The main contributions described are as follows: (1) a list of FAIs we defined that can be utilized and possibly extended by other researchers, (2) a novel approach to assess the probability of a false alarm using statistical analysis of multiple inputs representing alarm-context information, and (3) a reasoning algorithm that uses alarm-context information to detect false alarms in order to decide whether to notify caregivers with an indication of FAP (ie, FAP_LABEL) to avoid alarm fatigue. Conclusions Experiments were conducted to demonstrate that by providing an intelligent notification system, we could decide how to identify false alarms by analyzing alarm-context information. The reasoner entity we described in this paper was able to attribute FAP values to alarms based on FAIs and to notify caregivers with a FAP_LABEL indication without compromising patient safety.
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Dong, Qinghai, Wei Li, Ruihua Shi, et al. "Strong Clutter Suppression Using Spatial and Signal Similarity for Multi-Channel SAR Ground-Moving-Target Indication." Remote Sensing 15, no. 20 (2023): 4913. http://dx.doi.org/10.3390/rs15204913.
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This paper presents a new two-stage approach for suppressing strong clutter and detecting moving targets using scatterers’ spatial structure and signal similarity. Compared with the traditional strong clutter suppression methods, the proposed method considers both the spatial similarity and the channel correlation of the scatterers, effectively alleviating the false alarm probability and avoiding the missed detection problem caused via identifying strong moving targets as strong stationary clutter. Additionally, a detector is presented based on the linear degree of the radial velocity interferometric phase (LDRVP) to eliminate false alarms from isolated strong scatter points and the edges of strong scatterers. The experimental results of the X-band radar indicate the presented approach’s lower false alarm probability and superior robustness.
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Zhang, Chaozhu, Jing Zhang, and Chengyuan Liu. "Rao and Wald Tests for Adaptive Detection in Partially Homogeneous Environment with a Diversely Polarized Antenna." Scientific World Journal 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/369103.
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This study considers Rao test and Wald test for adaptive detection based on a diversely polarized antenna (DPA) in partially homogeneous environment. The theoretical expressions for the probability of false alarm and detection are derived, and constant false alarm rate (CFAR) behaviour is remarked on. Furthermore, the monotonicities of detection probability of the two detectors are proved, and a polarization optimization detection algorithm to enhance the detection performance is proposed. The numerical simulations are conducted to attest to the validity of the above theoretical analysis and illustrate the improvement in the detection performance of the proposed optimization algorithm.
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Mamchenko, M. V. "Approach and Algorithm for Evaluating the Allowed Signal/Noise Ratio of Robotic Lidars under External Influences." Proceedings of the Southwest State University 26, no. 3 (2023): 129–50. http://dx.doi.org/10.21869/2223-1560-2022-26-3-129-150.
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Purpose or research. The aim of the study is to ensure the safe operation of robotics by developing methods, approaches and algorithms for information processing, and describing their functioning.Methods. The paper proposes an approach to estimation allowed signal/noise ratio (SNR) for robotic LiDARs based on the predetermined probability of occurrence of «false alarm» under unintended influences. The synthesized probabilistic approach is based on the physical fundaments of infrared radiation, and the Bayesian theory using the Neyman-Pearson criterion. The feature of the proposed approach is the use of the given threshold of «false alarm» occurrence, and the probability of occurrence of interference in the analytical apparatus, as well as consideration of the characteristics of photodetectors. This allows expressing analytically and calculating the value of the allowed SNR when stabilizing the level of «false alarms» against background noise caused by this type of interference.Results. The formed and presented dependencies can be used as one of the operating characteristics in the development and selection of optoelectronic system of LiDAR’s measurement system. Based on the fixed value of «false alarm», and the resulting graphical expression of the operating characteristic (obtained characteristics) it is possible to choose a LiDARs system with necessary technical parameters.Conclusion. The probabilistic approach and the corresponding algorithm for selecting the threshold SNR value based on the Neyman-Pearson criterion were developed. The approach allows minimizing the probability of «ignoring» the object when scanning, since the probability of «false alarm» does not exceed the given threshold value. Mathematical and methodological support for the design of LiDARs is presented, taking into account a priori estimation of the allowed SNR value, and the probability of reflected pulse detection, without preliminary estimates of probabilistic characteristics of object detection. The presented algorithm has a set of raw data (in the form of the values of the received signal with a noise component) as an input. Its output is represented by a set of error probability dependencies for different SNR thresholds.
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Lomi, V., D. Tonetto, and L. Vangelista. "False alarm probability-based estimation of multipath channel length." IEEE Transactions on Communications 51, no. 9 (2003): 1432–34. http://dx.doi.org/10.1109/tcomm.2003.816974.
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Noonan, Joseph P., and David A. Marquis. "False Alarm Probability of a DWT-Based Estimation Algorithm." Digital Signal Processing 6, no. 3 (1996): 155–59. http://dx.doi.org/10.1006/dspr.1996.0016.
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Zhang, C. F., J. W. Xu, Y. P. Men, et al. "Fast radio burst detection in the presence of coloured noise." Monthly Notices of the Royal Astronomical Society 503, no. 4 (2021): 5223–31. http://dx.doi.org/10.1093/mnras/stab823.
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ABSTRACT In this paper, we investigate the impact of correlated noise on fast radio burst (FRB) searching. We found that (1) the correlated noise significantly increases the false alarm probability; (2) the signal-to-noise ratios (S/N) of the false positives become higher; (3) the correlated noise also affects the pulse width distribution of false positives, and there will be more false positives with wider pulse width. We use 55-h observation for M82 galaxy carried out at Nanshan 26m radio telescope to demonstrate the application of the correlated noise modelling. The number of candidates and parameter distribution of the false positives can be reproduced with the modelling of correlated noise. We will also discuss a low S/N candidate detected in the observation, for which we demonstrate the method to evaluate the false alarm probability in the presence of correlated noise. Possible origins of the candidate are discussed, where two possible pictures, an M82-harboured giant pulse and a cosmological FRB, are both compatible with the observation.
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Wang, Rui, Yahui Li, Hui Sun, and Kaixin Yang. "Multisensor-Weighted Fusion Algorithm Based on Improved AHP for Aircraft Fire Detection." Complexity 2021 (January 28, 2021): 1–10. http://dx.doi.org/10.1155/2021/8704924.
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Aiming at the high false alarm rate when using single sensor to detect fire in aircraft cabin, a multisensor data fusion method is proposed to detect fire. First, the weights of multiple factors, that is, temperature, smoke concentration, CO concentration, and infrared ray intensity in the event of fire, were calculated by using the improved analytic hierarchy process (AHP) method on each sensor node of wireless sensor network, and the probability of fire event in the cabin was evaluated by multivariable-weighted fusion method. Second, based on the mutual support among the evaluation data of fire probabilities of each node, the adaptive weight coefficient is assigned to each evaluation value, and the weighted fusion of all evaluation values of each node is conducted to obtain the fire probability. In the end, compared to the threshold of probability, the fire alarm is determined. Comparing the proposed algorithm to the grey fuzzy neural network fusion algorithm and fuzzy logic fusion algorithm in terms of the time consumption for fire detection and sending alarm and the accuracy of fire alarm perspectives, the experiments demonstrate that the proposed fire detection algorithm can detect the fire within 10s and reduce the false alarm rate to less than 0.5%, which verifies the superiority of the algorithm in promptness and accuracy. In the meanwhile, the fault tolerance of the algorithm is proved as well.
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Ha, Il-Kyu, and You-Ze Cho. "Analysis of factors affecting the speed of probabilistic target search using unmanned aerial vehicles." International Journal of Distributed Sensor Networks 15, no. 9 (2019): 155014771987761. http://dx.doi.org/10.1177/1550147719877610.
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When searching for targets using unmanned aerial vehicles, speed is important for many applications such as the discovery of patients in a medical emergency. The speed of operation of actual unmanned aerial vehicles is strongly related to the performance of the camera sensor used for target recognition, search altitude, and the search algorithm employed by the unmanned aerial vehicle. In this study, the major factors affecting the speed of a probabilistic unmanned aerial vehicle target search are analyzed. In particular, simulations are performed to analyze the influence of the search altitude, sensor false alarm rate, and sensor missed detection rate on the required travel distance and the time required for a search. Furthermore, the search performance of an unmanned aerial vehicle is analyzed by varying the search altitude with fixed false alarm and missed detection probabilities. The simulation results show that the search performance is significantly affected by changes in the false alarm and missed detection probabilities of the sensor, and it confirms that the effect of the missed detection probability is greater than that of the false alarm probability. The second simulation proves that the altitude of an unmanned aerial vehicle is a very important factor for the speed of a target search. In particular, the result shows that, for a real data set, the search distance and time at 10 and 5 m are about 2.8 times and 14.3 times larger, respectively, than those at 20 m.
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Gallus, G., M. Marchi, and G. Radaelli. "Poisson Approximation to a Negative Binomial Process in the Surveillance of Rare Health Events." Methods of Information in Medicine 30, no. 03 (1991): 206–9. http://dx.doi.org/10.1055/s-0038-1634838.
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AbstractThe Poisson approximation to a negative binomial process is evaluated regarding the surveillance of rare health events in the framework of the “Sets” scheme. This scheme defines an alarm in terms of “distance” between consecutive events of interest. The system’s parameters are determined by minimizing the expected delay for an alarm when a given increase in the event rate has occurred, subject to a restriction on the rate of false alarms. It is shown that the main consequence of the Poisson approximation lies in an increase of the false alarm probability with respect to the assigned one, whilst influence on the expected delay for a true alarm is lower. It is, however, found that over a large range of practical instances, the Poisson assumption provides a reasonable description of the negative binomial process.
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Mohammad A. AL-Hussain, Ali, and Maher K. Mahmood. "SPECTRUM SENSING OF WIDE BAND SIGNALS BASED ON ENERGY DETECTION WITH COMPRESSIVE SENSING." Journal of Engineering and Sustainable Development 24, no. 06 (2020): 83–90. http://dx.doi.org/10.31272/jeasd.24.6.7.
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Compressive sensing (CS) technique is used to solve the problem of high sampling rate with wide band signal spectrum sensing where high speed analogue to digital converter is needed to do that. This leads to difficult hardware implementation, large time of sensing and detection with high consumptions power. The proposed approach combines energy-based detection, with CS compressive sensing and investigates the probability of detection, and the probability of false alarm as a function of the SNR, showing the effect of compression to spectrum sensing performance of cognitive radio system. The Discrete Cosine Transform (DCT) is used as a sparse representation basis of the received signal, and random matrix as a compressive matrix. The 𝓁1 norm algorithm is used to reconstruct the original signal. A closed form of probability of detection and probability of false alarm are derived. Computer simulation shows clearly that the compression ratio, recovery error and SNR level affect the probability of detection.
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Ali, Hazlina, Sharifah Soaad Syed Yahaya, and Zurni Omar. "Robust Hotelling Control Chart with Consistent Minimum Vector Variance." Mathematical Problems in Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/401350.
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Recently, an alternative robust control chart based on a new robust estimator known as minimum vector variance (MVV) estimator, , was introduced in Phase II. was able to detect out-of-control signal and simultaneously control false alarm rate even as the dimension increased. However, the estimated UCLs of are large as compared to the traditional chart. In this study, we improved the MVV estimators in terms of consistency and bias. The result showed great improvement in the control limit values while maintaining its good performance in terms of false alarm and probability of detection.
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Kong, Sijie, Jin Zhou, and Wenli Ma. "Effect Analysis of Optical Masking Algorithm for GEO Space Debris Detection." International Journal of Optics 2019 (March 26, 2019): 1–8. http://dx.doi.org/10.1155/2019/2815890.
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A method with high detection rate, low false-alarm rate, and low computational cost is presented for removing stars and noise and detecting space debris with signal-to-noise ratio (SNR>3) in consecutive raw frames. The top-hat transformation is implemented firstly to remove background, then a masking technique is proposed to remove stars, and finally, a weighted algorithm is used to detect the pieces of space debris. The simulation samples are images taken by 600 mm ground-based telescope. And a series of simulation targets are added to the image in order to test the detection rate and false-alarm rate of different SNRs. The telescope in this paper is worked in “staring target mode.” The experimental results show that the proposed method can detect space debris effectively with low false-alarm by only three frames. When the SNR is higher than 3, the detection probability can reach 94%, and the false-alarm rate is below 2%. The running time of this algorithm is within 1 second. Additionally, algorithm performance tests in different regions are also carried out. A large set of image sequences are tested, which proves the stableness and effectiveness of the proposed method.
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Mboungam, Abdel Hamid Mbouombouo, Zhi Yongfeng, and Wilfried Andre Tiako Youani. "Moving Target Detection Using CA, SO and GO-CFAR detectors in Nonhomogeneous Environment." Applied Mathematics and Sciences An International Journal (MathSJ) 10, no. 1/2 (2023): 11–30. http://dx.doi.org/10.5121/mathsj.2023.10202.
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Modernization of radar technology and improved signal processing techniques are necessary to improve detection systems in complex situations. A fundamental problem in radar systems is to automatically detect targets while maintaining a desired constant false alarm probability. This work studies two detection approaches, the first with a fixed threshold and the other with an adaptive one. In the latter, we have learned the three types of detectors CA, SO, and GO-CFAR. This research aims to apply intelligent techniques to improve detection performance in a nonhomogeneous environment using standard CFAR detectors. The objective is to maintain the false alarm probability and enhance target detection by combining intelligent techniques. With these objectives in mind, implementing standard CFAR detectors is applied to nonhomogeneous environment data. The primary focus is understanding the reason for the false detection when applying standard CFAR detectors in a nonhomogeneous environment and how to avoid it using intelligent approaches
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Talaei Khoei, Tala, Shereen Ismail, Khair Al Shamaileh, Vijay Kumar Devabhaktuni, and Naima Kaabouch. "Impact of Dataset and Model Parameters on Machine Learning Performance for the Detection of GPS Spoofing Attacks on Unmanned Aerial Vehicles." Applied Sciences 13, no. 1 (2022): 383. http://dx.doi.org/10.3390/app13010383.
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GPS spoofing attacks are a severe threat to unmanned aerial vehicles. These attacks manipulate the true state of the unmanned aerial vehicles, potentially misleading the system without raising alarms. Several techniques, including machine learning, have been proposed to detect these attacks. Most of the studies applied machine learning models without identifying the best hyperparameters, using feature selection and importance techniques, and ensuring that the used dataset is unbiased and balanced. However, no current studies have discussed the impact of model parameters and dataset characteristics on the performance of machine learning models; therefore, this paper fills this gap by evaluating the impact of hyperparameters, regularization parameters, dataset size, correlated features, and imbalanced datasets on the performance of six most commonly known machine learning techniques. These models are Classification and Regression Decision Tree, Artificial Neural Network, Random Forest, Logistic Regression, Gaussian Naïve Bayes, and Support Vector Machine. Thirteen features extracted from legitimate and simulated GPS attack signals are used to perform this investigation. The evaluation was performed in terms of four metrics: accuracy, probability of misdetection, probability of false alarm, and probability of detection. The results indicate that hyperparameters, regularization parameters, correlated features, dataset size, and imbalanced datasets adversely affect a machine learning model’s performance. The results also show that the Classification and Regression Decision Tree classifier has an accuracy of 99.99%, a probability of detection of 99.98%, a probability of misdetection of 0.2%, and a probability of false alarm of 1.005%, after removing correlated features and using tuned parameters in a balanced dataset. Random Forest can achieve an accuracy of 99.94%, a probability of detection of 99.6%, a probability of misdetection of 0.4%, and a probability of false alarm of 1.01% in similar conditions.
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Maranda, B. H. "On the false alarm probability for an overlapped FFT processor." IEEE Transactions on Aerospace and Electronic Systems 32, no. 4 (1996): 1452–56. http://dx.doi.org/10.1109/7.543866.
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Wang, Ye, Yalin Zhang, Qinyu Zhang, and Shaohua Wu. "Optimal Selection of False Alarm Probability for Dynamic Spectrum Access." IEEE Communications Letters 17, no. 5 (2013): 844–47. http://dx.doi.org/10.1109/lcomm.2012.030413.121025.
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Kamal, Mustafa Subhi, and Jiwa Abdullah. "New algorithm for multi targets detection in clutter edge radar environments." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 1 (2020): 420. http://dx.doi.org/10.11591/ijeecs.v18.i1.pp420-427.
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<p>This paper deals with the problem of multi target detection that appears inside clutter cloud which represent the worst radar environments by using constant false alarm rate CFAR algorithm, in order to achieve maximum probability of detection with constant false alarm rate, to detect target in such environments it need to construct robust constant false alarm CFAR algorithm that excise the target spikes from CFAR window and deal with clutter edges in order to give best possible estimation to the noise background. Modified cell averaged (CA-CFAR) is analyzed and compared with Two important algorithms which are cell averaged (CA-CFAR) and ordered statistics (OS-CFAR) algorithms in additional to the modified CA-CFAR algorithm. All these algorithms were simulated with mat lab and applied them to matlab clutter test model that represent different radar environment cases. Tradeoff among these algorithms depending on their responses.</p>
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Researcher. "MACHINE LEARNING BASED FALSE ALARMS REDUCTION FOR SMALL INFRARED TARGETS." International Journal of Artificial Intelligence & Machine Learning (IJAIML) 3, no. 2 (2024): 140–62. https://doi.org/10.5281/zenodo.13751988.
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There are many techniques for feature detection in Small Infrared Images and to minimize false alarm rates up-gradation is a must. This paper gives the idea of machine learning dependent target detection where feature extraction is done using Discrete Wavelet Transformation and feature selection is done using Nature Inspire Algorithm that is Particle Swarm Optimization at the end classification is done using classifier Random Forest with filter-based target detection method. Modified Top- Hat filter is used for the filtered database. DWT is used for extraction of features that breaks a target into small parts termed as wavelets obtained from mother wavelet via shifting and dilation. Before feature selection each feature is analyzed using two different approaches first is intensity distribution and other one is probability density function and from both the observation median, mean, entropy, variance shows the best result because overlapping of probability distribution is lease in these four. PSO works on the principle of bird flocking together after calculating gbest and pbest of each feature, Variance, Mode, Kurtosis, Zernike moment, Entropy, Skewness are the highest-ranking feature values these features are used further for the classification purpose. Out of all the classifiers logistic regression and random forest classifiers have shown best results for the false alarm reduction rate. Earlier many wrapper-based feature selection algorithms have been used but PSO and random forest classifiers have combinedly reduced false alarm rate by 7.9 whereas without classifiers the false alarm rate was 30.6. FAR without classifier and with classifier improved by 3.7 factor.
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Brooks, Harold E., and James Correia. "Long-Term Performance Metrics for National Weather Service Tornado Warnings." Weather and Forecasting 33, no. 6 (2018): 1501–11. http://dx.doi.org/10.1175/waf-d-18-0120.1.
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Abstract Tornado warnings are one of the flagship products of the National Weather Service. We update the time series of various metrics of performance in order to provide baselines over the 1986–2016 period for lead time, probability of detection, false alarm ratio, and warning duration. We have used metrics (mean lead time for tornadoes warned in advance, fraction of tornadoes warned in advance) that work in a consistent way across the official changes in policy for warning issuance, as well as across points in time when unofficial changes took place. The mean lead time for tornadoes warned in advance was relatively constant from 1986 to 2011, while the fraction of tornadoes warned in advance increased through about 2006, and the false alarm ratio slowly decreased. The largest changes in performance take place in 2012 when the default warning duration decreased, and there is an apparent increased emphasis on reducing false alarms. As a result, the lead time, probability of detection, and false alarm ratio all decrease in 2012. Our analysis is based, in large part, on signal detection theory, which separates the quality of the warning system from the threshold for issuing warnings. Threshold changes lead to trade-offs between false alarms and missed detections. Such changes provide further evidence for changes in what the warning system as a whole considers important, as well as highlighting the limitations of measuring performance by looking at metrics independently.