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Advanced RSS-Based Multisource Localization: Sequential Hypothesis Testing for Robust Location Estimation
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DOI：10.1109/JSEN.2024.3463542.png)
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摘要
En 中文
Accurate estimation of the number, locations, and transmit powers of wireless signal sources is crucial for various applications, including surveillance and monitoring. In this article, we propose a novel algorithm based on received signal strength (RSS) measurements to address this problem. Our approach utilizes sequential binary hypothesis testing, offering a computationally efficient solution without prior knowledge of the number of sources. Through extensive simulations, we demonstrate the superior performance of our algorithm compared with the existing methods, such as exhaustive search and multiresolution (MR) search, in terms of accuracy and computational complexity. Notably, our algorithm exhibits robustness in scenarios with multiple sources and close proximity between them. We also conduct performance analysis to evaluate its sensitivity to noise variance and false alarm probability, showcasing its reliability under different conditions. Our work contributes to advancing wireless signal processing techniques and offers promising implications for enhanced surveillance and monitoring capabilities in wireless communication systems. Overall, our proposed algorithm presents an efficient and accurate solution for estimating wireless signal sources, with potential for significant impact on practical applications.
Keyword:
Location awareness
Sensors
Vectors
Testing
Wireless sensor networks
Wireless communication
Extraterrestrial measurements
Location fixing
maximum likelihood (ML)
multisource localization
received signal strength (RSS)
sequential hypothesis test
source enumeration
期刊
IF:
4.5
论文数:
2.1W
被引数:
7.3W
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