Advanced RSS-Based Multisource Localization: Sequential Hypothesis Testing for Robust Location Estimation

doi:10.1109/JSEN.2024.3463542

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Advanced RSS-Based Multisource Localization: Sequential Hypothesis Testing for Robust Location Estimation

2024-11-15

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Hamid Ketabalian

Mehrzad Biguesh *

Abbas Sheikhi

DOI：10.1109/JSEN.2024.3463542

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Abstract

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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.

Keywords:

Location awareness

Sensors

Vectors

Testing

Wireless sensor networks

Wireless communication

Extraterrestrial measurements

Location fixing

maximum likelihood (ML)

multisource localization

received signal strength (RSS)