Volume 22, Issue 4 (JIAEEE Vol.22 No.4 2025)                   Journal of Iranian Association of Electrical and Electronics Engineers 2025, 22(4): 4-14 | Back to browse issues page

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Zayyani H, Bekrani M. Improving Target Localization Using the Linear Least Squares Method and Received Signal Strength Under Path Loss Exponent Uncertainty. Journal of Iranian Association of Electrical and Electronics Engineers 2025; 22 (4) :4-14
URL: http://jiaeee.com/article-1-1793-en.html
Improving Target Localization Using the Linear Least Squares Method and Received Signal Strength Under Path Loss Exponent Uncertainty
Hadi Zayyani , Mehdi Bekrani *
Department of Electrical and Computer Engineering, Qom University of Technology
Abstract:   (661 Views)

Wireless sensor networks consist of a set of sensor nodes that communicate with each other via radio waves. In many applications, obtaining sensor data is crucial for determining the target source's location. Various localization algorithms have been developed based on the characteristics of the received signal. In this paper, a localization algorithm based on received signal strength (RSS) using a log-normal model is proposed. In this model, the path loss exponent is considered a random variable with a uniform distribution. To enhance localization accuracy, a cost function is defined, and the steepest descent method is employed to minimize the error. The proposed algorithm is simulated in different scenarios with four, eight, and twelve antennas. The results demonstrate that the proposed method accurately estimates the source position while maintaining low computational complexity. Moreover, increasing the number of antennas improves localization accuracy. Simulation results show that under the condition of using 8 sensors and a signal-to-noise ratio (SNR) of 10 dB, the root mean square error (RMSE) is reduced by 50% compared to the closest competing method, namely the semidefinite programming (SDP) approach. Furthermore, by increasing the number of sensors from 8 to 12 (with the same SNR), the RMSE decreases from 0.25 meters to 0.20 meters, indicating an improvement of approximately 20% in localization accuracy.

Keywords: wireless sensor network, localization, linear least squares, maximum likelihood estimator
Full-Text [PDF 1087 kb]   (202 Downloads)    
Type of Article: Research | Subject: Communication
Received: 2025/02/8 | Accepted: 2025/10/25 | Published: 2026/01/22
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