Robust Least-SquareLocalization Based on Relative Angular Matrix in Wireless Sensor Networks.

Area of Science:

• Computer Engineering
• Wireless Communication
• Network Systems

Background:

• Accurate positioning is crucial for wireless sensor networks (WSN).
• Conventional cooperative positioning algorithms, like least squares (LS), are sensitive to initial position estimates.
• This sensitivity impacts the ubiquitous implementation of cooperative positioning.

Purpose of the Study:

• To investigate the sensitivity of least squares algorithms to initial position selection in WSN.
• To develop a robust cooperative positioning algorithm for WSN.
• To improve the accuracy and robustness of positioning in various environments.

Main Methods:

• Identified a sufficient and unnecessary condition for the convexity of the least squares cost function.
• Developed a novel algorithm that transforms the cost function into a globally convex function.
• Algorithm utilizes the null space of the relative angle matrix for targets within a convex polygon.

Main Results:

• The proposed algorithm demonstrates high positioning accuracy in simulations.
• The method is robust in both line-of-sight (LOS) and non-line-of-sight (NLOS) environments.
• The algorithm is applicable to both time of arrival (TOA) and angle of arrival/time of arrival (AOA/TOA) scenarios.

Conclusions:

• The developed algorithm effectively overcomes the limitations of conventional least squares methods.
• Ensuring cost function convexity is key to robust and accurate WSN positioning.
• The proposed approach offers a promising solution for ubiquitous cooperative positioning in WSN.