AOA-Assisted TDOA Localization Based on Improved Whale Optimization Algorithm for Asynchronous Wireless System Networks

Abstract

This paper proposes a AOA-assisted TDOA localization method based on an improved whale optimization algorithm (IWOA) combining TDOA and AOA for asynchronous wireless sensor networks (WSNs). A TDOA compensation scheme is first introduced to address network asynchrony. This scheme uses the communication timestamps between anchor nodes to estimate relative clock deviations and clock offsets. Then, a fusion algorithm using the compensated TDOA and AOA measurements enables high-precision localization with only two angle-measuring anchor nodes and a set of TDOA anchor nodes, reducing the number of required anchor nodes and improving deployment flexibility. Finally, the integration of IWOA enhances the optimization process, improving robustness. Simulations demonstrate that the proposed IWOA achieves superior robustness and accuracy under severe noise and adverse geometric conditions, significantly outperforming the Chan algorithm and other metaheuristic benchmarks. Meanwhile, the CWLS algorithm exhibits competitive performance in well-calibrated scenarios, revealing their complementary characteristics. The proposed method provides an effective solution for asynchronous WSNs, especially in challenging measurement environments.