A simulated annealing based approach for near-optimal sensor selection in TDOA localization system

Abstract

The Sensor Selection Problem refers to the challenge of selecting the most appropriate set of sensors from a larger pool to effectively monitor a physical system or environment under certain constraints. These constraints can include budget, energy consumption, spatial coverage, sensor functionality, and required measurement accuracy or resolution. The main goal is to optimize the selection of sensors to ensure effective monitoring, data collection, or system control while adhering to these constraints. This issue arises in a variety of applications. When the Sensor Selection Problem is combined with time-difference-of-arrival (TDOA) localization, the focus is on selecting the most appropriate set of sensors to accurately determine the location of the source based on the difference in signal arrival times Across sensors. Therefore, it becomes crucial to select the optimal subset of sensors that can achieve the desired goals. Sensor Selection Problem involves mathematical modeling and computational methods and optimization algorithms, to evaluate different combinations of sensors and select the optimal subset based on defined criteria and objectives. In this FYP, we address the Sensor Selection Problem in TDOA localization system, where a subset of K sensors is chosen from a total of N sensors such that the trace of the Cramer-Rao lower bound (CRLB) is minimized. We present a simulated annealing (SA) based method to solve the resulting minimization problem. Simulation results demonstrate the superior performance of the proposed method compared with the previous semidefinite relaxation (SDR) based method. This work is written as a paper and accepted for lecture presentation at the IEEE International Symposium on Circuits and Systems (ISCAS) 2024.