Log in

Relevant bibliographies by topics / Signal Strength Estimation / Journal articles

To see the other types of publications on this topic, follow the link: Signal Strength Estimation.

Journal articles on the topic 'Signal Strength Estimation'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Signal Strength Estimation.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Li, Kejiong, Peng Jiang, Eliane L. Bodanese, and John Bigham. "Outdoor Location Estimation Using Received Signal Strength Feedback." IEEE Communications Letters 16, no. 7 (2012): 978–81. http://dx.doi.org/10.1109/lcomm.2012.050912.111805.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Yehya, Tamer, Yahya Mohasseb, and Ashraf Mahran. "Position Estimation in WiMAX Networks using Received Signal Strength." International Conference on Electrical Engineering 9, no. 9th (2014): 1–13. http://dx.doi.org/10.21608/iceeng.2014.30361.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Ning, Chao, Rui Li, and Kejiong Li. "Outdoor Location Estimation Using Received Signal Strength-Based Fingerprinting." Wireless Personal Communications 89, no. 2 (2016): 365–84. http://dx.doi.org/10.1007/s11277-016-3270-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Pedapolu, Pavan K., Pushkar Saraf, Pradeep Kumar, et al. "Regression Based Mobility Estimation Method Using Received Signal Strength." Wireless Personal Communications 101, no. 1 (2018): 359–74. http://dx.doi.org/10.1007/s11277-018-5692-7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

TAKIZAWA, Y., P. DAVIS, M. KAWAI, H. IWAI, A. YAMAGUCHI, and S. OBANA. "Self-Organizing Location Estimation Method Using Received Signal Strength." IEICE Transactions on Communications E89-B, no. 10 (2006): 2687–95. http://dx.doi.org/10.1093/ietcom/e89-b.10.2687.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Mott, John H., and Qingsong Ai. "Estimation of aircraft distances using transponder signal strength information." Cogent Engineering 5, no. 1 (2018): 1466619. http://dx.doi.org/10.1080/23311916.2018.1466619.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Obeidat, Huthaifa, Ali A. S. Alabdullah, Nazar T. Ali, et al. "Local Average Signal Strength Estimation for Indoor Multipath Propagation." IEEE Access 7 (2019): 75166–76. http://dx.doi.org/10.1109/access.2019.2918178.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Huang, He, and Bin Luo. "A Received Signal Strength Indication Adaptive Algorithm for Wireless Sensor Network." Applied Mechanics and Materials 273 (January 2013): 505–9. http://dx.doi.org/10.4028/www.scientific.net/amm.273.505.

Full text

Abstract:

Indoor environments are complicated and changeable, and RSSI (Received Signal Strength Indication) observations have great randomness, so the classic RSSI estimation algorithm has poor results in indoor environments. To solve this problem, a RSSI adaptive estimation algorithm (RAE-IW) based on Kalman filtering algorithm is presented in this paper, which achieves exact RSSI estimation, and fast adapts to the change of environmental parameters. Simulation results show that RAE-IW has low complexity, performs better than classic estimation methods in indoor environments, and applies to indoor wir

APA, Harvard, Vancouver, ISO, and other styles

9

LEI, KIN FONG, SHIH-CHUNG CHENG, MING-YIH LEE, and WEN-YEN LIN. "MEASUREMENT AND ESTIMATION OF MUSCLE CONTRACTION STRENGTH USING MECHANOMYOGRAPHY BASED ON ARTIFICIAL NEURAL NETWORK ALGORITHM." Biomedical Engineering: Applications, Basis and Communications 25, no. 02 (2013): 1350020. http://dx.doi.org/10.4015/s1016237213500208.

Full text

Abstract:

Muscle contraction strength estimation using mechanomyographic (MMG) signal is typically calculated by the root mean square (RMS) amplitude. Raw MMG signal is processed by rectification, low-pass filtering, and mapping. In this work, beside RMS amplitude, another significant parameter of MMG signal, i.e. frequency variance (VAR), is introduced and used for constructing an algorithm for estimating the muscle contraction strength. Seven participants produced isometric contractions about the elbow while MMG signal and generated torque (resultant of muscle contraction strength) of biceps brachii w

APA, Harvard, Vancouver, ISO, and other styles

10

FUJII, Masahiro, Yuma HIROTA, Hiroyuki HATANO, Atsushi ITO, and Yu WATANABE. "Distance Estimation Based on Statistical Models of Received Signal Strength." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E99.A, no. 1 (2016): 199–203. http://dx.doi.org/10.1587/transfun.e99.a.199.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Verma, Gunjan, Fikadu T. Dagefu, Brian M. Sadler, and Jeffrey Twigg. "Direction of Arrival Estimation With the Received Signal Strength Gradient." IEEE Transactions on Vehicular Technology 67, no. 11 (2018): 10856–70. http://dx.doi.org/10.1109/tvt.2018.2869817.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Qiu, Tianyu, Xiao Fu, Nicholas D. Sidiropoulos, and Daniel P. Palomar. "MISO Channel Estimation and Tracking from Received Signal Strength Feedback." IEEE Transactions on Signal Processing 66, no. 7 (2018): 1691–704. http://dx.doi.org/10.1109/tsp.2018.2791974.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Laurendeau, Christine, and Michel Barbeau. "Insider attack attribution using signal strength-based hyperbolic location estimation." Security and Communication Networks 1, no. 4 (2008): 337–49. http://dx.doi.org/10.1002/sec.35.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Ohara, Kenichi, Yuji Abe, Tomohito Takubo, Yasushi Mae, Tamio Tanikawa, and Tatsuo Arai. "Range Estimation Technique Using Received Signal Strength Indication on Low Frequency Waves." Journal of Robotics and Mechatronics 23, no. 4 (2011): 466–74. http://dx.doi.org/10.20965/jrm.2011.p0466.

Full text

Abstract:

Recently, with the downsizing of computers and the development of wireless communication advances, sensor networks are being widely studied. However, it is necessary to know the location of each node, in order to apply sensor data. Many researchers have tried to find a good approach to position estimation in indoor environment. In our study, we focus on position estimation by using Received Signal Strength Indication (RSSI). It has the advantage of implementation with limited resources in the sensor network. However, since RSSI value is affected by multipath and obstacles, position estimation

APA, Harvard, Vancouver, ISO, and other styles

15

Abdel Meniem, Mohamed H., Ahmed M. Hamad, and Eman Shaaban. "GSM-Based Positioning Technique Using Relative Received Signal Strength." International Journal of Handheld Computing Research 4, no. 4 (2013): 38–51. http://dx.doi.org/10.4018/ijhcr.2013100103.

Full text

Abstract:

Context-aware applications have been gaining huge interest in the last few years. With cell phones becoming ubiquitous computing devices, cell phone localization has become an important research problem. Database Correlation Method (DCM) is a positioning technology that based on a database of a premeasured location dependent variable such as Received Signal Strength (RSS). DCM has shown superior in terms of accuracy. Absolute RSS values received from a base station change with time, but the relative RSS (RRSS) values which refer to the relations of the RSS values between different base station

APA, Harvard, Vancouver, ISO, and other styles

16

Miyagusuku, Renato, Atsushi Yamashita, and Hajime Asama. "2A2-L07 Analysis of two approaches to location estimation based on wireless signal strength propagation and Gaussian Processes." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2015 (2015): _2A2—L07_1—_2A2—L07_4. http://dx.doi.org/10.1299/jsmermd.2015._2a2-l07_1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Wojcicki, Piotr, Tomasz Zientarski, Malgorzata Charytanowicz, and Edyta Lukasik. "Estimation of the Path-Loss Exponent by Bayesian Filtering Method." Sensors 21, no. 6 (2021): 1934. http://dx.doi.org/10.3390/s21061934.

Full text

Abstract:

Regarding wireless sensor network parameter estimation of the propagation model is a most important issue. Variations of the received signal strength indicator (RSSI) parameter are a fundamental problem of a system based on signal strength. In the present paper, we propose an algorithm based on Bayesian filtering techniques for estimating the path-loss exponent of the log-normal shadowing propagation model for outdoor RSSI measurements. Furthermore, in a series of experiments, we will demonstrate the usefulness of the particle filter for estimating the RSSI data. The stability of this algorith

APA, Harvard, Vancouver, ISO, and other styles

18

Jia, Zixi, and Bo Guan. "Received signal strength difference–based tracking estimation method for arbitrarily moving target in wireless sensor networks." International Journal of Distributed Sensor Networks 14, no. 3 (2018): 155014771876487. http://dx.doi.org/10.1177/1550147718764875.

Full text

Abstract:

The surveillance system, which is mainly used for detecting and tracking moving targets, is one of the most significant applications of wireless sensor networks. Up to present, received signal strength indicator is the most common measuring mean for estimating the distance in sensor networks. However, in the presence of noise, it is impossible to gain the accurate distance based on received signal strength indicator. In this article, we propose a new tracking scheme based on received signal strength difference, which is the difference value of received signal strength indicators between two ne

APA, Harvard, Vancouver, ISO, and other styles

19

Gao, Zhisheng, Yaoshun Li, and Chunzhi Xie. "Parameter Estimation for the Field Strength of Radio Environment Maps." Wireless Communications and Mobile Computing 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/2475439.

Full text

Abstract:

The parameters of a radio environment map play an important role in radio management and cognitive radio. In this paper, a method for estimating the parameters of the radio environment map based on the sensing data of monitoring nodes is presented. According to the principles of radio transmission signal intensity losses, a theoretical variogram model based on a propagation model is proposed, and the improved theoretical variation function is more in line with the attenuation of radio signal propagation. Furthermore, a weight variogram fitting method is proposed based on the characteristics of

APA, Harvard, Vancouver, ISO, and other styles

20

P.M., Balasubramaniam, Arivoli S, and Prabhakaran N. "Performance of Signal Strength prediction in Data transmission Using Elliott wave Theory." International Journal of Computer Communication and Informatics 2, no. 1 (2020): 62–69. http://dx.doi.org/10.34256/ijcci2017.

Full text

Abstract:

The article describes an algorithm for predicting the future signals with the aid of past signal samples. In the real signal processing environment, the amplitude and unsystematic in phase signal are lead to more complex to estimation the signal, thereby, customer service is enhanced by forecast. The forecast of financial marketplace are usually done by means of Elliot wave theory. In this article possibility and applicability survey of the EW Theory is proposed in the paper towards the power of the signal forecast. In nature, the EW theory has free declining environment, and also uncomfortabl

APA, Harvard, Vancouver, ISO, and other styles

21

Chiou, Lih-Yih, Khurram Muhammand, and Kaushik Roy. "Signal Strength Based Switching Activity Modeling and Estimation for DSP Applications." VLSI Design 12, no. 2 (2001): 233–43. http://dx.doi.org/10.1155/2001/35832.

Full text

Abstract:

We present an effective switching activity modeling and estimation technique for components under resource sharing. The model uses word-level signal statistics to generate a single parameter, called signal strength. By using the signal strength, we can construct power models for the both cases of sharing and non-sharing of computing resources. The model enables us to effectively estimate switching activity at higher level of design abstraction. We have conducted several experiments using both synthetic and real data to evaluate our method. We have compared competing architectures for their rel

APA, Harvard, Vancouver, ISO, and other styles

22

Castro-Arvizu, Juan Manuel, Jordi Vilà-Valls, Ana Moragrega, Pau Closas, and Juan A. Fernandez-Rubio. "Received signal strength–based indoor localization using a robust interacting multiple model–extended Kalman filter algorithm." International Journal of Distributed Sensor Networks 13, no. 8 (2017): 155014771772215. http://dx.doi.org/10.1177/1550147717722158.

Full text

Abstract:

Due to the vast increase in location-based services, currently there exists an actual need of robust and reliable indoor localization solutions. Received signal strength localization is widely used due to its simplicity and availability in most mobile devices. The received signal strength channel model is defined by the propagation losses and the shadow fading. In real-life applications, these parameters might vary over time because of changes in the environment. Thus, to obtain a reliable localization solution, they have to be sequentially estimated. In this article, the problem of tracking a

APA, Harvard, Vancouver, ISO, and other styles

23

Shrestha, Surendra, Reenu Mool, and Chun Kwan Park. "Mobile Location Estimation Based on Received Signal Strength using Circular Approach." Journal of Engineering and Applied Sciences 14, no. 1 (2019): 3922–26. http://dx.doi.org/10.36478/jeasci.2019.3922.3926.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

ISHIKAWA, Yuichiro, and Hiroshi IGARASHI. "Position Estimation by Received Signal Strength Indicator and Mobile Base Station." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2017 (2017): 2P2—C04. http://dx.doi.org/10.1299/jsmermd.2017.2p2-c04.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Jie Yin, Qiang Yang, and L. M. Ni. "Learning Adaptive Temporal Radio Maps for Signal-Strength-Based Location Estimation." IEEE Transactions on Mobile Computing 7, no. 7 (2008): 869–83. http://dx.doi.org/10.1109/tmc.2007.70764.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Lim, Geunhwi, Kwangwook Shin, Jin Suk Kim, and H. Yoon. "Signal strength-based link stability estimation in ad hoc wireless networks." Electronics Letters 39, no. 5 (2003): 485. http://dx.doi.org/10.1049/el:20030287.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Mertens, Michael, Martin Ulmke, and Wolfgang Koch. "Ground target tracking with RCS estimation based on signal strength measurements." IEEE Transactions on Aerospace and Electronic Systems 52, no. 1 (2016): 205–20. http://dx.doi.org/10.1109/taes.2015.140866.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Yang, Tao, and Xiaoping Wu. "Accurate location estimation of sensor node using received signal strength measurements." AEU - International Journal of Electronics and Communications 69, no. 4 (2015): 765–70. http://dx.doi.org/10.1016/j.aeue.2014.12.007.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Polak, Ladislav, Stanislav Rozum, Martin Slanina, Tomas Bravenec, Tomas Fryza, and Aggelos Pikrakis. "Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning." Sensors 21, no. 13 (2021): 4605. http://dx.doi.org/10.3390/s21134605.

Full text

Abstract:

The fingerprinting technique is a popular approach to reveal location of persons, instruments or devices in an indoor environment. Typically based on signal strength measurement, a power level map is created first in the learning phase to align with measured values in the inference. Second, the location is determined by taking the point for which the recorded received power level is closest to the power level actually measured. The biggest limit of this technique is the reliability of power measurements, which may lack accuracy in many wireless systems. To this end, this work extends the power

APA, Harvard, Vancouver, ISO, and other styles

30

Lisus, Daniil, Charles Champagne Cossette, Mohammed Shalaby, and James Richard Forbes. "Heading Estimation Using Ultra-Wideband Received Signal Strength and Gaussian Processes." IEEE Robotics and Automation Letters 6, no. 4 (2021): 8387–93. http://dx.doi.org/10.1109/lra.2021.3102300.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Chen, Liang, Heidi Kuusniemi, Yuwei Chen, Ling Pei, Tuomo Kröger, and Ruizhi Chen. "Motion Restricted Information Filter for Indoor Bluetooth Positioning." International Journal of Embedded and Real-Time Communication Systems 3, no. 3 (2012): 54–66. http://dx.doi.org/10.4018/jertcs.2012070104.

Full text

Abstract:

This paper studies wireless positioning using a network of Bluetooth signals. Fingerprints of received signal strength indicators (RSSI) are used for localization. Due to the relatively long interval between the available consecutive Bluetooth signal strength measurements, the authors propose a method of information filtering with speed detection, which combines the estimation information from the RSSI measurements with the prior information from the motion model. Speed detection is further assisted to correct the outliers of position estimation. The field tests show that the new algorithm pro

APA, Harvard, Vancouver, ISO, and other styles

32

Jo, Hyeon, and Seungku Kim. "Indoor Smartphone Localization Based on LOS and NLOS Identification." Sensors 18, no. 11 (2018): 3987. http://dx.doi.org/10.3390/s18113987.

Full text

Abstract:

Accurate localization technology is essential for providing location-based services. Global positioning system (GPS) is a typical localization technology that has been used in various fields. However, various indoor localization techniques are required because GPS signals cannot be received in indoor environments. Typical indoor localization methods use the time of arrival, angle of arrival, or the strength of the wireless communication signal to determine the location. In this paper, we propose an indoor localization scheme using signal strength that can be easily implemented in a smartphone.

APA, Harvard, Vancouver, ISO, and other styles

33

Olama, Mohammed M., Kiran K. Jaladhi, Seddik M. Djouadi, and Charalambos D. Charalambous. "Recursive Estimation and Identification of Time-Varying Long-Term Fading Channels." Research Letters in Signal Processing 2007 (2007): 1–5. http://dx.doi.org/10.1155/2007/17206.

Full text

Abstract:

This paper is concerned with modeling of time-varying wireless long-term fading channels, parameter estimation, and identification from received signal strength data. Wireless channels are represented by stochastic differential equations, whose parameters and state variables are estimated using the expectation maximization algorithm and Kalman filtering, respectively. The latter are carried out solely from received signal strength data. These algorithms estimate the channel path loss and identify the channel parameters recursively. Numerical results showing the viability of the proposed channe

APA, Harvard, Vancouver, ISO, and other styles

34

A, Pasumpon Pandian. "Novel Distance Estimation based Localization Scheme for Wireless Sensor Networks using Modified Swarm Intelligence Algorithm." December 2020 2, no. 4 (2021): 171–76. http://dx.doi.org/10.36548/jsws.2020.4.006.

Full text

Abstract:

Wireless sensor networks (WSN) consists of a huge number of nodes that are positioned randomly to obtain information regarding the environment and communicate with each other. On detection of an event, obtaining information regarding the geographical location of the sensor is beneficial in most applications. Range-free and range-based localization schemes are the major categories of localization algorithms available. Range-free localization algorithms utilize the connectivity information to provide a cost efficient localization solution. On the other hand, range-based localization schemes use

APA, Harvard, Vancouver, ISO, and other styles

35

Urban, Mike, Timo Tigges, Michael Klum, Alexandru Pielmus, and Reinhold Orglmeister. "Improvement of Stroke Volume Estimation with Bioimpedance Measurement by LSTM Network Approach Based on ECG during Ergometry." Current Directions in Biomedical Engineering 5, no. 1 (2019): 33–36. http://dx.doi.org/10.1515/cdbme-2019-0009.

Full text

Abstract:

AbstractStroke volume (SV) and cardiac output (CO) estimations with non-invasive approaches like thoracic electrical bioimpedance (TEB) measurement become state of the art in clinical practice. Despite the advantages like low costs, low risk of infection and relatively easy application, there are also disadvantages like the sensitivity to movement artifacts and, electrode displacement mistakes. The bioimpedance signal acquired with a tetrapolar measurement has a relatively weak signal strength compared with another common recorded signal, e.g., the electrocardiogram (ECG). For reconstruction a

APA, Harvard, Vancouver, ISO, and other styles

36

Cascado-Caballero, Daniel, Lourdes Duran-Lopez, Juan Pedro Dominguez-Morales, Daniel Gutierrez-Galan, Claudio Amaya-Rodriguez, and Manuel Dominguez-Morales. "Implementing a Distance Estimator for a Wildlife Tracking System Based on 802.15.4." Electronics 8, no. 12 (2019): 1438. http://dx.doi.org/10.3390/electronics8121438.

Full text

Abstract:

In this work, a novel distance estimation mechanism using received signal strength indication (RSSI) signals with ZigBee modules is designed, implemented and tested in several scenarios. This estimator was used for a research project focused on a wildlife behavioral classification system deployed in Doñana’s National Park. As a supporting feature for that project, this work was implemented for locating animal’s collars acting as wireless nodes in order to find those who went outside of the coverage area of the network or that were accidentally detached from animals. This work describes the sys

APA, Harvard, Vancouver, ISO, and other styles

37

Yao, Zheng, Huaiyu Wu, Yang Chen, Zhihuan Chen, and Xiujuan Zheng. "Reliable Wi-Fi Indoor Localization in Case of AP Loss by Using Integrated Model Based on Signal Anomaly Detector and Signal Distance Corrector." Discrete Dynamics in Nature and Society 2021 (July 13, 2021): 1–11. http://dx.doi.org/10.1155/2021/5579931.

Full text

Abstract:

When developing a Wi-Fi indoor positioning system in a real-world environment, the problems we have to face are that some access points’ signal strength fluctuates extensively or even loses contact due to the cybersecurity threats, leading to the fact that the indoor location system cannot get reliable application in a real-world environment. To solve this problem, we propose a new integrated model based on signal anomaly detector and signal distance corrector to provide reliable position estimation when the access points’ signal is lost under cybersecurity threats. The signal anomaly detector

APA, Harvard, Vancouver, ISO, and other styles

38

Gross, Jason, Yu Gu, and Matthew Rhudy. "Fixed-Wing UAV Attitude Estimation Using Single Antenna GPS Signal Strength Measurements." Aerospace 3, no. 2 (2016): 14. http://dx.doi.org/10.3390/aerospace3020014.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

JIN, Seung-Hwan, Jae-Kark CHOI, Nan HAO, and Sang-Jo YOO. "Distance Estimation by Sequential Rearrangement of Signal Strength in Wireless Sensor Networks." IEICE Transactions on Communications E94-B, no. 9 (2011): 2634–37. http://dx.doi.org/10.1587/transcom.e94.b.2634.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Wang, Gicheol, and Gihwan Cho. "Secure Cluster Head Sensor Elections Using Signal Strength Estimation and Ordered Transmissions." Sensors 9, no. 6 (2009): 4709–27. http://dx.doi.org/10.3390/s90604709.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Mott, John H., and Darcy M. Bullock. "Estimation of Aircraft Operations at Airports Using Mode-C Signal Strength Information." IEEE Transactions on Intelligent Transportation Systems 19, no. 3 (2018): 677–86. http://dx.doi.org/10.1109/tits.2017.2700764.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Zhang, Hao. "Location Estimation Approach of Multiple Sensor Nodes Using Received Signal Strength Measurements." Journal of Information and Computational Science 12, no. 9 (2015): 3365–72. http://dx.doi.org/10.12733/jics20105958.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Banakh, V. A., and I. A. Razenkov. "Refractive turbulence strength estimation based on the laser echo signal amplification effect." Optics Letters 41, no. 19 (2016): 4429. http://dx.doi.org/10.1364/ol.41.004429.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Swanepoel, J. W. H., C. F. de Beer, and H. Loots. "Estimation of the Strength of a Periodic Signal from Photon Arrival Times." Astrophysical Journal 467 (August 1996): 261. http://dx.doi.org/10.1086/177600.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Zhu, Quan Zheng, Le Yang, and Wei Li. "Simple and Robust RSSI Estimation Using M-Estimator." Advanced Materials Research 756-759 (September 2013): 3946–51. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3946.

Full text

Abstract:

Accurate estimation of the received signal strength indicator (RSSI) from a set of sequentially measured ones is essential for a number of practical applications including link quality evaluation for sensor network routing, indoor wireless localization and more recently, handover in health monitoring systems. This paper develops a simple and robust RSSI estimation algorithm that can effectively mitigate the magnitude variation in the RSSI measurements due to the combined effects of fast fading and non-line-of-sight (NLOS) signal propagation. The new method is based on the robust M-estimator an

APA, Harvard, Vancouver, ISO, and other styles

46

Wu, Luo, Jia, Sun, Sheng, and Jiang. "RSSI-Power-Based Direction of Arrival Estimation of Partial Discharges in Substations." Energies 12, no. 18 (2019): 3450. http://dx.doi.org/10.3390/en12183450.

Full text

Abstract:

The localization of partial discharges in air-insulated substations using ultra-high frequency technology is widely studied for power equipment early warning purposes. Ultra-high frequency partial discharge localization systems are usually based on the time-difference of electromagnetic wave signals. However, the large size of test equipment and the need for a high sampling rate and time synchronization accuracy limit their practical application. To address this challenge, this paper proposes a power-based partial discharge direction of arrival method using a received signal strength indicator

APA, Harvard, Vancouver, ISO, and other styles

47

Thimmaiah, Sudha H., and Mahadevan G. "A Radio Signal Strength Based Localization Error Optimization Technique for Wireless Sensor Network." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 3 (2018): 839. http://dx.doi.org/10.11591/ijeecs.v11.i3.pp839-847.

Full text

Abstract:

Wireless Sensor Networks (WSN) is useful in collecting data from various sensor devices that are distributed over a network which is generally positioned in a stationary manner. Wireless sensor based communication system is an ever growing sector in the industry of communication. Wireless infrastructure is a network that enables correspondence between various devices associated through an infrastructure protocol. Finding the position or location of sensor node (Localization) is an important factor in sensor network for proving efficient service to end user. The existing technique proposed so f

APA, Harvard, Vancouver, ISO, and other styles

48

Hu, Xiao-Li, Pin-Han Ho, and Limei Peng. "Performance Analysis of Maximum Likelihood Estimation for Transmit Power Based on Signal Strength Model." Journal of Sensor and Actuator Networks 7, no. 3 (2018): 38. http://dx.doi.org/10.3390/jsan7030038.

Full text

Abstract:

We study theoretical performance of Maximum Likelihood (ML) estimation for transmit power of a primary node in a wireless network with cooperative receiver nodes. The condition that the consistence of an ML estimation via cooperative sensing can be guaranteed is firstly defined. Theoretical analysis is conducted on the feasibility of the consistence condition regarding an ML function generated by independent yet not identically distributed random variables. Numerical experiments justify our theoretical discoveries.

APA, Harvard, Vancouver, ISO, and other styles

49

Jaafar, Azhar, Norashikin M. Thamrin, and Noorolpadzilah Mohamed Zan. "Wireless sensor network calibration technique for low-altitude unmanned aerial vehicle localization in paddy field." Bulletin of Electrical Engineering and Informatics 10, no. 1 (2021): 208–15. http://dx.doi.org/10.11591/eei.v10i1.2512.

Full text

Abstract:

This paper presents the use of the received signal strength indicator (RSSI) from the RF signal to estimate the distance from a point where the signal is transmitted to the point where the signal is received. This can be a challenge as in the paddy field, the watery and dry conditions, as well as the height of the paddy plant can affect signal transmission during this estimation process. Two low-cost ground beacons, Beacon1 and Beacon2 (The coordinator), are used and placed in a known location with a fixed distance across the paddy field, which becomes the reference point during the distance e

APA, Harvard, Vancouver, ISO, and other styles

50

Liu, Ying, Jun Feng Su, and Ming Qiang Zhu. "The Location Algorithm Based on Square-Root Cubature Kalman Filter." Applied Mechanics and Materials 325-326 (June 2013): 1525–29. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1525.

Full text

Abstract:

When wireless signal is used for indoor localization, there is no consistent relationship between signal strength received by the receiving nodes and distance from the receiving nodes to the receiving nodes, so there is a larger localization error for the Received Signal Strength Indication (RSSI) in the indoor environment. A new received signal strength indicator parameter estimation algorithm based on square-root cubature kalman filter is proposed in this paper, this algorithm utilizes Square-root Cubature Kalman filter (SCKF) to estimate the target’s position and the RSSI channel attenuatio

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!