Relevant bibliographies by topics / Probability of false alarm (PF)

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Academic literature on the topic 'Probability of false alarm (PF)'

Author: Grafiati
Published: 3 June 2025
Last updated: 22 June 2025

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Journal articles on the topic "Probability of false alarm (PF)"

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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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Dissertations / Theses on the topic "Probability of false alarm (PF)"

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Amirichimeh, Reza 1958. "Simulation and analytic evaluation of false alarm probability of a non-linear detector." Thesis, The University of Arizona, 1991. http://hdl.handle.net/10150/277966.

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One would like to evaluate and compare complex digital communication systems based upon their overall bit error rate. Unfortunately, analytical expressions for bit error rate for even simple communication systems are notoriously difficult to evaluate accurately. Therefore, communication engineers often resort to simulation techniques to evaluate these error probabilities. In this thesis importance sampling techniques (variations of standard Monte Carlo methods) are studied in relation to both linear and non-linear detectors. Quick simulation, an importance sampling method based upon the asymptotics of the error estimator, is studied in detail. The simulated error probabilities are compared to values obtained by numerically inverting Laplace Transform expressions for these quantities.
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Savage, Cynthia Leigh. "Optimizing Aviation Security Architectures using the SAFE Model." Thesis, Virginia Tech, 2003. http://hdl.handle.net/10919/41634.

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The Federal Aviation Administration (FAA) wishes to minimize the overall operational costs of their aviation security detection systems. These systems consist of a collection of security devices. The objective of this research is to develop an algorithm to design the optimal system of devices. The Secure Air Flight Effectiveness (SAFE) Model accomplishes this objective by using the probability of detection and the probability of giving a false alarm for each individual device. A Generalized Hill Climbing (GHC) algorithm was implemented to identify the system with the minimum operational cost. Suggestions for future research directions are also included.<br>Master of Science
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Li, Sun, Wang Yinfeng, and Zhang Qishan. "AN ADAPTIVE SIGNAL SEARCH ALGORITHM IN GPS RECEIVER." International Foundation for Telemetering, 1998. http://hdl.handle.net/10150/607349.

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International Telemetering Conference Proceedings / October 26-29, 1998 / Town & Country Resort Hotel and Convention Center, San Diego, California<br>GPS signal fixed dwell and variable dwell time sequential search algorithms are compared with probability of false alarm and detection and searching rate. An adaptive search algorithm is proposed according to different work modes and interference or jam circumstance, which has effectively improved signal acquiring speed and reliability. Mathematical simulation shows its correction and feasible.
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Zhang, Xiang. "Dynamic Probability Control Limits for Risk-Adjusted Bernoulli Cumulative Sum Charts." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/77889.

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The risk-adjusted Bernoulli cumulative sum (CUSUM) chart developed by Steiner et al. (2000) is an increasingly popular tool for monitoring clinical and surgical performance. In practice, however, use of a fixed control limit for the chart leads to quite variable in-control average run length (ARL) performance for patient populations with different risk score distributions. To overcome this problem, the simulation-based dynamic probability control limits (DPCLs) patient-by-patient for the risk-adjusted Bernoulli CUSUM charts is determined in this study. By maintaining the probability of a false alarm at a constant level conditional on no false alarm for previous observations, the risk-adjusted CUSUM charts with DPCLs have consistent in-control performance at the desired level with approximately geometrically distributed run lengths. Simulation results demonstrate that the proposed method does not rely on any information or assumptions about the patients' risk distributions. The use of DPCLs for risk-adjusted Bernoulli CUSUM charts allows each chart to be designed for the corresponding particular sequence of patients for a surgeon or hospital. The effect of estimation error on performance of risk-adjusted Bernoulli CUSUM chart with DPCLs is also examined. Our simulation results show that the in-control performance of risk-adjusted Bernoulli CUSUM chart with DPCLs is affected by the estimation error. The most influential factors are the specified desired in-control average run length, the Phase I sample size and the overall adverse event rate. However, the effect of estimation error is uniformly smaller for the risk-adjusted Bernoulli CUSUM chart with DPCLs than for the corresponding chart with a constant control limit under various realistic scenarios. In addition, there is a substantial reduction in the standard deviation of the in-control run length when DPCLs are used. Therefore, use of DPCLs has yet another advantage when designing a risk-adjusted Bernoulli CUSUM chart. These researches are results of joint work with Dr. William H. Woodall (Department of Statistics, Virginia Tech). Moreover, DPCLs are adapted to design the risk-adjusted CUSUM charts for multiresponses developed by Tang et al. (2015). It is shown that the in-control performance of the charts with DPCLs can be controlled for different patient populations because these limits are determined for each specific sequence of patients. Thus, the risk-adjusted CUSUM chart for multiresponses with DPCLs is more practical and should be applied to effectively monitor surgical performance by hospitals and healthcare practitioners. This research is a result of joint work with Dr. William H. Woodall (Department of Statistics, Virginia Tech) and Mr. Justin Loda (Department of Statistics, Virginia Tech).<br>Ph. D.
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Dongkai, Yang, Zhan Qishan, and Cheng Lee Lung. "AN IMPROVED AMI CODE USED IN TELEMETRY SYSTEM." International Foundation for Telemetering, 1998. http://hdl.handle.net/10150/609235.

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International Telemetering Conference Proceedings / October 26-29, 1998 / Town & Country Resort Hotel and Convention Center, San Diego, California<br>An Improved AMI (Alternate Mark Inverse) Code used in telemetry system is proposed, its implementation and properties analysis are reported, including error performance analysis, power spectrum analysis, the relationship between acqusition probability of the first frame marker and error threshold and length of frame marker, etc. This type of code has the approximately identical power spectrum performance as the AMI Code. In addition, there have no long continuous zeroes in the data stream, which will cause phase-locked loop to fail. Using the Improved AMI Code, the equal probability of 0 and 1 is changed, which will increase acqusition probability of the first frame marker. Detailed description about how to create the Improved AMI Code is also discussed in this paper.
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Ζαμπούνη, Αικατερίνη. "Τεχνικές συμπιεσμένης καταγραφής για ανίχνευση φάσματος σε ασύρματα γνωστικά δίκτυα συνεργασίας". Thesis, 2015. http://hdl.handle.net/10889/8609.

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Είναι γνωστό από τη Θεωρία της Πληροφορίας, πως η δειγματοληψία σημάτων ακολουθεί το Θεώρημα των Shannon-Nyquist. Σύμφωνα με το θεώρημα αυτό, για την εκτέλεση της δειγματοληψίας ενός σήματος χωρίς απώλεια πληροφορίας, ο ρυθμός δειγματοληψίας αυτού θα πρέπει να είναι τουλάχιστον δύο φορές μεγαλύτερος από τη μεγαλύτερη συχνότητα που εμφανίζεται στο φάσμα του σήματος. Αυτή τη θεωρία κατάφερε – κατά κάποιο τρόπο - να ανατρέψει το 2006 μια νέα, αυτή της Συμπιεσμένης Καταγραφής που ξεκίνησε από δύο επιστημονικές εργασίες των Donoho, Candes, Romberg και Tao και η οποία έρχεται να αλλάξει τα έως σήμερα δεδομένα. Σήμερα, λίγα έτη αργότερα, μια αφθονία θεωρητικών πτυχών της συμπιεσμένης καταγραφής εξερευνάται ήδη σε περισσότερες από 1000 δημοσιεύσεις. Οι εφαρμογές αυτής της τεχνικής εκτείνονται και σε άλλα πεδία όπως η επεξεργασία εικόνας, η μαγνητική τομογραφία, η ανάλυση γεωφυσικών δεδομένων, η επεξεργασία εικόνας radar, η αστρονομία κ.α. Η μέθοδος της συμπιεσμένης καταγραφής ή αλλιώς Compressed Sensing ή Compressed Sampling, όπως αυτή είναι γνωστή στη βιβλιογραφία, στηρίζεται στη δυνατότητα ανακατασκευής αραιών σημάτων από πλήθος δειγμάτων αισθητά κατώτερο από αυτό που προβλέπει το θεωρητικό όριο του Nyquist. Έχει αποδειχθεί ότι, η ανακατασκευή αυτή είναι δυνατή όταν το σήμα ή έστω κάποιος μετασχηματισμός του περιέχει λίγα μη μηδενικά στοιχεία σε σχέση με το μήκος του. Στα πλαίσια αυτής της εργασίας παρουσιάζονται οι βασικές αρχές που διέπουν την ανακατασκευή αραιών σημάτων μέσω της επίλυσης υπο-ορισμένων συστημάτων γραμμικών εξισώσεων. Στη συγκεκριμένη εργασία, γίνεται μία προσπάθεια εφαρμογής της εν λόγω μεθόδου στα ανερχόμενα Cognitive Radio δίκτυα (Cognitive Radio Networks - CRN) τα οποία εμφανίζουν την ιδιότητα Spectrum Sharing. Σύμφωνα με αυτή την ιδιότητα, δηλαδή, το διαμοιρασμό του διαθέσιμου φάσματος, ο πρωταρχικός στόχος, είναι η ανίχνευση και η αναγνώριση των λεγόμενων spectrum holes σε ασύρματο περιβάλλον. Πιο συγκεκριμένα, παρουσιάζεται μια Distributed (κατανεμημένη) προσέγγιση συμπιεσμένης καταγραφής φάσματος για (τα ultra-) Wideband Cognitive Radio δίκτυα. Η τεχνική Compressed Sensing εφαρμόζεται σε τοπικά CRs του δικτύου, προκειμένου να ανιχνεύσει το υπερ-ευρύ φάσμα (ultra-wideband) με ρεαλιστική πολυπλοκότητα ανάκτησης του αρχικού σήματος. Οι φασματικές εκτιμήσεις από πολλαπλούς τοπικούς CRs του δικτύου «συνενώνονται» για να αποκομίσουν το χωρικό κέρδος ποικιλομορφίας (spatial diversity gain), το οποίο όσο αυξάνεται, βελτιώνει την ποιότητα ανίχνευσης, ειδικά στην περίπτωση των υπό εξασθένιση καναλιών (channel fading effect). Αρχικά, μελετάται ένας κατανεμημένος αλγόριθμος πλειοψηφίας (Distributed Consensus Algorithm) για να επιτευχθεί η συνεργασία κατά το στάδιο της ανίχνευσης της πληροφορίας που μεταφέρεται στο δίκτυο και έπειτα η αποστολή αυτής σε ένα fusion center. Αυτού του είδους ο distributed αλγόριθμος που χρησιμοποιεί μόνο one-hop επικοινωνία, συγκλίνει γρήγορα σε συνολικά βέλτιστες λύσεις που λειτουργούν με χαμηλό φόρτο επικοινωνίας και υπολογισμού που είναι ανάλογο του μεγέθους του δικτύου. Ένα σενάριο που εξετάζεται στο πλαίσιο αυτής της εργασίας, είναι η συγκεντρωτική ανίχνευση φάσματος ευρείας ζώνης με επικαλυπτόμενες συχνότητες ή αλλιώς κανάλια που είναι κοινά (frequency overlapping) σε Cognitive Radio δίκτυα και τα οποία, χρησιμοποιούν την τεχνική Compressed Sensing καθώς επίσης και την από κοινού ανακατασκευή (Joint Reconstruction) του αρχικού σήματος. Τέλος, προτείνεται ένα σενάριο, μιας κατανεμημένης αυτή τη φορά, τεχνικής ανίχνευσης φάσματος, που βασίζεται σε κανόνες πλειοψηφίας. Τα αποτελέσματα της προσομοίωσης, σε περιβάλλον Matlab, επιβεβαιώνουν την αποτελεσματικότητα αυτής της προτεινόμενης προσέγγισης, δηλαδή την ανίχνευση φάσματος, από συνδυασμό Cognitive Radio δικτύων με αραιά επικαλυπτόμενες συχνότητες.<br>It is well known from Information Theory, that the sampling of signals should be performed as dictated by the celebrated Shannon – Nyquist theorem. According to this theorem, in order to fully recover a signal from its samples, it must be sampled at a sampling rate that should be at least twice the bandwidth of the signal. This theory has been significantly extended over the past few years by the advent of the so-called Compressed Sensing theory, which first appeared in seminal scientific articles of Donoho, Candes, Romberg and Tao in 2006. Nowadays, an abundance of theoretical aspects of compressed sensing is already explored in more than 1000 articles. Τhis technique has been applied in various fields such as image processing, magnetic tomography, analysis of geophysical data, radar image processing, astronomy etc. The method of Compressed Sensing, also known as Compressed Sampling, is related to the reconstruction of sparse signals from far fewer samples or measurements than what the theoretical limit of Nyquist suggests. It has been proved that, this reconstruction is possible when the signal or a transformation of it, contains just a few non-zero elements with respect to its length. In this work, we firstly summarize the basic principles that condition the reconstruction of sparse signals via the solution of underdetermined systems of linear equations. Next, in this Master Thesis we aim at implementing Compressed Sensing method in emerging Cognitive Radio (CR) networks with spectrum sharing. The first cognitive task preceding any dynamic spectrum access is the sensing and identification of spectral holes in wireless environments. In more detail, this work is mainly concerned with a distributed compressed spectrum sensing approach for (ultra-)wideband CR networks. Compressed sensing is performed at local CRs to scan the very wide spectrum at practical signal-acquisition complexity. Meanwhile, spectral estimates from multiple local CR detectors are fused to collect spatial diversity gain, which improves the sensing quality especially under fading channels. Initially, a distributed consensus algorithm is analyzed for collaborative sensing and fusion in a scenario where all nodes are estimating the same spectral bands. Using only one-hop local communications, this distributed algorithm converges fast to the globally optimal solutions, at low communication and computation load scalable to the network size. Another scenario that has been investigated in this thesis is the joint wideband spectrum sensing in frequency overlapping cognitive radio networks, using centralized compressive sensing techniques. Finally, for the latter scenario, a distributed compressive sensing technique, based on consensus, has been proposed. Simulation results in Matlab environment verify the effectiveness of proposed joint spectrum sensing approach in jointly sparse frequency overlapping cognitive radio networks.
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Feener, Jessica S. "Safeguards for Uranium Extraction (UREX) +1a Process." Thesis, 2010. http://hdl.handle.net/1969.1/ETD-TAMU-2010-05-270.

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As nuclear energy grows in the United States and around the world, the expansion of the nuclear fuel cycle is inevitable. All currently deployed commercial reprocessing plants are based on the Plutonium - Uranium Extraction (PUREX) process. However, this process is not implemented in the U.S. for a variety of reasons, one being that it is considered by some as a proliferation risk. The 2001 Nuclear Energy Policy report recommended that the U.S. "develop reprocessing and treatment technologies that are cleaner, more efficient, less waste-intensive, and more proliferation-resistant." The Uranium Extraction (UREX+) reprocessing technique has been developed to reach these goals. However, in order for UREX+ to be considered for commercial implementation, a safeguards approach is needed to show that a commercially sized UREX+ facility can be safeguarded to current international standards. A detailed safeguards approach for a UREX+1a reprocessing facility has been developed. The approach includes the use of nuclear material accountancy (MA), containment and surveillance (C/S) and solution monitoring (SM). Facility information was developed for a hypothesized UREX+1a plant with a throughput of 1000 Metric Tons Heavy Metal (MTHM) per year. Safeguard goals and safeguard measures to be implemented were established. Diversion and acquisition pathways were considered; however, the analysis focuses mainly on diversion paths. The detection systems used in the design have the ability to provide near real-time measurement of special fissionable material in feed, process and product streams. Advanced front-end techniques for the quantification of fissile material in spent nuclear fuel were also considered. The economic and operator costs of these systems were not considered. The analysis shows that the implementation of these techniques result in significant improvements in the ability of the safeguards system to achieve the objective of timely detection of the diversion of a significant quantity of nuclear material from the UREX+1a reprocessing facility and to provide deterrence against such diversion by early detection.
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Book chapters on the topic "Probability of false alarm (PF)"

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He, Miao, Kexin Jia, and Ting Cheng. "False Alarm Probability of the Digital Channelized Receiver Based CA-CFAR Detector." In Web Information Systems and Mining. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23971-7_12.

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Singh, Prashant, Shivam Kapri, Rajat Saklani, Prateek Mehta, and Sudhir Kumar Chaturvedi. "Estimation of Probability of Detection and False Alarm Alert for Swerling Targets." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5903-2_10.

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Migliaccio, M., F. Nunziata, A. Marino, C. Brekke, and S. Skrunes. "Ocean Applications." In Polarimetric Synthetic Aperture Radar. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56504-6_6.

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AbstractIn this chapter, the most promising techniques to observe oil slicks and to detect metallic targets at sea using polarimetric synthetic aperture radar (SAR) data are reviewed and critically analysed. The detection of oil slicks in SAR data is made difficult not only by the presence of speckle but also by the presence of, e.g. biogenic films, low-wind areas, rain cells, currents, etc., which increase the false alarm probability. The use of polarimetric features has been shown to both observe oil slicks and distinguish them from weak-damping look-alikes but also to extract some of their properties. Similarly to oil slicks, the same factors can hamper the detection of metallic targets at sea. The radiometric information provided by traditional single-channel SAR is not generally sufficient to unambiguously detect man-made metallic targets over the sea surface. This shortcoming is overcome by employing polarimetry, which allows to fully characterize the scattering mechanism of such targets.
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Wu, Peixuan, Xiaoyong Du, and Weidong Hu. "Cascaded GLRT Radar/Infrared Lidar Information Fusion Algorithm for Weak Target Detection." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_6.

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AbstractTo deal with the problem of weak target detection, a cascaded generalized likelihood ratio test (GLRT) radar/infrared lidar heterogeneous information fusion algorithm is proposed in this paper. The algorithm makes full use of the target characteristics in microwave/infrared spectrum and the scanning efficiency of different sensors. According to the correlation of target position in the multi-sensor view field, the GLRT statistic derived from the radar measurements is compared with a lower threshold so as to generate initial candidate targets with high detection probability. Subsequently, the lidar is guided to scan the candidate regions and the final decision is made by GLRT detector to discriminate the false alarm. To get the best detection performance, the optimal detection parameters are obtained by nonlinear optimization for the cascaded GLRT Radar/Infrared lidar heterogeneous information fusion detection algorithm. Simulation results show that the cascaded GLRT heterogeneous information fusion detector comprehensively utilizes the advantages of radar and infrared lidar sensors in detection efficiency and performance, which effectively improves the detection distance upon radar weak targets within the allowable time.
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Laroussi, Toufik, and Mourad Barkat. "An Efficient Closed Form Approach to the Evaluation of the Probability of False Alarm of the ML-CFAR Detector in a Pulse-to-Pulse Correlated Clutter." In Signal Processing for Image Enhancement and Multimedia Processing. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-72500-0_3.

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Bagchi, Srijibendu. "Cepstrum-Based Spectrum Hole Search in Different Fading Scenario in Cognitive Radio Network." In Handbook of Research on Recent Developments in Intelligent Communication Application. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1785-6.ch002.

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Cognitive radio is now acknowledged as a potential solution to meet the spectrum scarcity problem in radio frequency range. To achieve this objective proper identification of vacant frequency band is necessary. In this article a detection methodology based on cepstrum estimation has been proposed that can be done through power spectral density estimation of the received signal. The detection has been studied under different channel fading conditions along with Gaussian noise. Two figures of merit are considered here; false alarm probability and detection probability. For a specific false alarm probability, the detection probabilities are calculated for different sample size and it has been established through numerical results that the proposed detector performs quite well in different channel impairments.
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"Narrowband Probability of Detection (PD) and false Alarm Rates (FAR)." In Signal Processing for Intelligent Sensor Systems. CRC Press, 2000. http://dx.doi.org/10.1201/9780203909959.ch11.

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Halmos, Maurice J. "Detection Statistics, Probability of Detection and False Alarm, and Statistical Improvement Techniques." In Introduction to Laser Radar: A New Light on Imaging. SPIE, 2024. http://dx.doi.org/10.1117/3.2669193.ch5.

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R, Durga, and Selvaraj D. "Effective Usage of Available Spectrum Using Dispersion Detection Technique." In Intelligent Systems and Computer Technology. IOS Press, 2020. http://dx.doi.org/10.3233/apc200172.

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Spectrum sensing techniques are used for aquising the frequency spectrum in cognitive radio. From research, the efficiency of the spectrum sensing technique increases only if its complexity is increased and if its complexity is decreased then its efficiency decreases. so, a new technique is proposed in this paper based on Dispersion Detection (DD) to balance both complexity and efficiency. Using this detection technique, the false alarm probability is derived for multiple antenna using test statistic distribution. The decision threshold is derived to provide the accurate results. The derived values are verified with Monto Carlo simulation.
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Wang Yu-bo, Wang Hai-jun, Jiao Wei-dong, Zhou Bo, and Shen Xiao-yun. "A Preprocessing Algorithm Before Conflict Detection Based on Hybrid Surveillance." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2017. https://doi.org/10.3233/978-1-61499-785-6-8.

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The probability of flight conflict is increased because of the increasing of flight flow and uncertainty of flight route. Considering the problem that redundant operation which caused by a large number of non-related targets. A preprocessing algorithm before conflict detection was proposed. Aircrafts within the surveillance scope of Automatic Dependent Surveillance-Broadcast (ADS-B) IN were divided into 26 different regions, and the correlation between targets was distinguished by special rule of each region. Then the correlated targets were further processed, but uncorrelated ones were blocked. So the calculation of conflict analysis for surveilling surrounding targets was decreased. Above all, the processing principle that security first should be followed, and rather false alarm but never leakage alarm. Finally, the necessity and validity of the proposed algorithm is examined by using Monte Carlo experiment. The simulation results show that not less than 30% of 30 random objects are blocked. The proposed algorithm is effective and accurate for detecting the correlation of them.
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Conference papers on the topic "Probability of false alarm (PF)"

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Jia, Yizhen, Mingcheng Xu, Haozhe Zhang, Guanshuai Wang, and Jun Zou. "Approximate False Alarm Probability of Hchirp System Based on LoRa." In 2024 7th International Conference on Information Communication and Signal Processing (ICICSP). IEEE, 2024. https://doi.org/10.1109/icicsp62589.2024.10809271.

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Tucker, Brian, and Paula Dempsey. "Health Monitoring Survey of Bell 412EP Transmissions." In Vertical Flight Society 72nd Annual Forum & Technology Display. The Vertical Flight Society, 2016. http://dx.doi.org/10.4050/f-0072-2016-11468.

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Health and usage monitoring systems (HUMS) use vibration-based Condition Indicators (CI) to assess the health of helicopter powertrain components. A fault is detected when a CI exceeds its threshold value. The effectiveness of fault detection can be judged on the basis of assessing the condition of actual components from fleet aircraft. The Bell 412 HUMS-equipped helicopter is chosen for such an evaluation. A sample of 20 aircraft included 12 aircraft with confirmed transmission and gearbox faults (detected by CIs) and eight aircraft with no known faults. The associated CI data is classified into "healthy" and "faulted" populations based on actual condition and these populations are compared against their CI thresholds to quantify the probability of false alarm and the probability of missed detection. Receiver Operator Characteristic analysis is used to optimize thresholds. Based on the results of the analysis, shortcomings in the classification method are identified for slow-moving CI trends. Recommendations for improving classification using time-dependent receiver-operator characteristic methods are put forth. Finally, lessons learned regarding OEM-operator communication are presented.
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Anitori, Laura, Matern Otten, and Peter Hoogeboom. "False alarm probability estimation for Compressive Sensing radar." In 2011 IEEE Radar Conference (RadarCon). IEEE, 2011. http://dx.doi.org/10.1109/radar.2011.5960529.

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Cao, Xuan Duc, Bao Dong To, Tuan Anh Nguyen, and Van Tuan Vu. "20km high accuracy, low false alarm probability laser rangefinder." In Solid State Lasers XXXII: Technology and Devices, edited by W. Andrew Clarkson and Ramesh K. Shori. SPIE, 2023. http://dx.doi.org/10.1117/12.2648621.

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Abraham, D. A. "Probability of false alarm estimation in oversampled active sonar systems." In 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258). IEEE, 1999. http://dx.doi.org/10.1109/icassp.1999.761315.

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Bin Hu and Peter Seiler. "Probability bounds for false alarm analysis of fault detection systems." In 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2013. http://dx.doi.org/10.1109/allerton.2013.6736633.

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Vijaya Kumar, Bhagavatula, Chulung Chen, and James D. Brasher. "Correlation filters assessed in terms of probability of detection and probability of false alarm." In Optical Engineering and Photonics in Aerospace Sensing, edited by David P. Casasent. SPIE, 1993. http://dx.doi.org/10.1117/12.160289.

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Wang, Xiaoxu, Haoran Cui, and Quan Pan. "Efficient False Alarm Probability Identification for Linear System with Uncertain Measurement." In 2018 IEEE CSAA Guidance, Navigation and Control Conference (GNCC). IEEE, 2018. http://dx.doi.org/10.1109/gncc42960.2018.9018960.

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Gentner, Christian, Ingmar Groh, Stephan Sand, and Armin Dammann. "False alarm and detection probability for NLOS detection in LTE environments." In Solutions (MC-SS). IEEE, 2011. http://dx.doi.org/10.1109/mc-ss.2011.5910721.

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Gentner, Christian, and Ingmar Groh. "Analytical Derivation of the False Alarm and Detection Probability for NLOS Detection." In 2011 IEEE Vehicular Technology Conference (VTC 2011-Spring). IEEE, 2011. http://dx.doi.org/10.1109/vetecs.2011.5956491.

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Reports on the topic "Probability of false alarm (PF)"

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Raynal, Ann Marie, Armin Walter Doerry, John A. Miller, Edward E. Bishop, and Volker Horndt. Shadow Probability of Detection and False Alarm for Median-Filtered SAR Imagery. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1323596.

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Clausen, Jay, Vuong Truong, Sophia Bragdon, et al. Buried-object-detection improvements incorporating environmental phenomenology into signature physics. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45625.

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The ability to detect buried objects is critical for the Army. Therefore, this report summarizes the fourth year of an ongoing study to assess environ-mental phenomenological conditions affecting probability of detection and false alarm rates for buried-object detection using thermal infrared sensors. This study used several different approaches to identify the predominant environmental variables affecting object detection: (1) multilevel statistical modeling, (2) direct image analysis, (3) physics-based thermal modeling, and (4) application of machine learning (ML) techniques. In addition, this study developed an approach using a Canny edge methodology to identify regions of interest potentially harboring a target object. Finally, an ML method was developed to improve automatic target detection and recognition performance by accounting for environmental phenomenological conditions, improving performance by 50% over standard automatic target detection and recognition software.
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Clausen, Jay, Michael Musty, Anna Wagner, Susan Frankenstein, and Jason Dorvee. Modeling of a multi-month thermal IR study. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41060.

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Inconsistent and unacceptable probability of detection (PD) and false alarm rates (FAR) due to varying environmental conditions hamper buried object detection. A 4-month study evaluated the environmental parameters impacting standoff thermal infra-red(IR) detection of buried objects. Field observations were integrated into a model depicting the temporal and spatial thermal changes through a 1-week period utilizing a 15-minute time-step interval. The model illustrates the surface thermal observations obtained with a thermal IR camera contemporaneously with a 3-d presentation of subsurface soil temperatures obtained with 156 buried thermocouples. Precipitation events and subsequent soil moisture responses synchronized to the temperature data are also included in the model simulation. The simulation shows the temperature response of buried objects due to changes in incoming solar radiation, air/surface soil temperature changes, latent heat exchange between the objects and surrounding soil, and impacts due to precipitation/changes in soil moisture. Differences are noted between the thermal response of plastic and metal objects as well as depth of burial below the ground surface. Nearly identical environmental conditions on different days did not always elicit the same spatial thermal response.
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Clausen, Jay, Susan Frankenstein, Jason Dorvee, et al. Spatial and temporal variance of soil and meteorological properties affecting sensor performance—Phase 2. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41780.

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An approach to increasing sensor performance and detection reliability for buried objects is to better understand which physical processes are dominant under certain environmental conditions. The present effort (Phase 2) builds on our previously published prior effort (Phase 1), which examined methods of determining the probability of detection and false alarm rates using thermal infrared for buried-object detection. The study utilized a 3.05 × 3.05 m test plot in Hanover, New Hampshire. Unlike Phase 1, the current effort involved removing the soil from the test plot area, homogenizing the material, then reapplying it into eight discrete layers along with buried sensors and objects representing targets of inter-est. Each layer was compacted to a uniform density consistent with the background undisturbed density. Homogenization greatly reduced the microscale soil temperature variability, simplifying data analysis. The Phase 2 study spanned May–November 2018. Simultaneous measurements of soil temperature and moisture (as well as air temperature and humidity, cloud cover, and incoming solar radiation) were obtained daily and recorded at 15-minute intervals and coupled with thermal infrared and electro-optical image collection at 5-minute intervals.
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Clausen, Jay, Christopher Felt, Michael Musty, et al. Modernizing environmental signature physics for target detection—Phase 3. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/43442.

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The present effort (Phase 3) builds on our previously published prior efforts (Phases 1 and 2), which examined methods of determining the probability of detection and false alarm rates using thermal infrared for buried object detection. Environmental phenomenological effects are often represented in weather forecasts in a relatively coarse, hourly resolution, which introduces concerns such as exclusion or misrepresentation of ephemera or lags in timing when using this data as an input for the Army’s Tactical Assault Kit software system. Additionally, the direct application of observed temperature data with weather model data may not be the best approach because metadata associated with the observations are not included. As a result, there is a need to explore mathematical methods such as Bayesian statistics to incorporate observations into models. To better address this concern, the initial analysis in Phase 2 data is expanded in this report to include (1) multivariate analyses for detecting objects in soil, (2) a moving box analysis of object visibility with alternative methods for converting FLIR radiance values to thermal temperature values, (3) a calibrated thermal model of soil temperature using thermal IR imagery, and (4) a simple classifier method for automating buried object detection.
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Bragdon, Sophia, Vuong Truong, and Jay Clausen. Environmentally informed buried object recognition. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45902.

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The ability to detect and classify buried objects using thermal infrared imaging is affected by the environmental conditions at the time of imaging, which leads to an inconsistent probability of detection. For example, periods of dense overcast or recent precipitation events result in the suppression of the soil temperature difference between the buried object and soil, thus preventing detection. This work introduces an environmentally informed framework to reduce the false alarm rate in the classification of regions of interest (ROIs) in thermal IR images containing buried objects. Using a dataset that consists of thermal images containing buried objects paired with the corresponding environmental and meteorological conditions, we employ a machine learning approach to determine which environmental conditions are the most impactful on the visibility of the buried objects. We find the key environmental conditions include incoming shortwave solar radiation, soil volumetric water content, and average air temperature. For each image, ROIs are computed using a computer vision approach and these ROIs are coupled with the most important environmental conditions to form the input for the classification algorithm. The environmentally informed classification algorithm produces a decision on whether the ROI contains a buried object by simultaneously learning on the ROIs with a classification neural network and on the environmental data using a tabular neural network. On a given set of ROIs, we have shown that the environmentally informed classification approach improves the detection of buried objects within the ROIs.
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