A New Weighted Least-Squares Method for Hybrid TDOA-AOA Localization

This work presents a new weighted least-squares (WLS) method for source localization using angle-of-arrival (AOA) and time-difference-of-arrival (TDOA) measurements. Previous studies have shown that combining these two independent types of measurements yields more reliable results, as evidenced by lower Cramér-Rao lower bounds (CRLB) compared to using a single type of measurement. Our newly developed WLS algorithm has excellent performance when the source is close to the sensors but encounters challenges when the source is distant. To address these limitations, we develop an improved solution that incorporates a one-dimensional search process along the distance between the reference node and the source node. This enhancement significantly improves the algorithm’s performance in ‘far-field’ (i.e., when the source is far away from the sensors) scenarios. Our extensive simulation results demonstrate that the proposed algorithms substantially outperform existing methods in terms of root mean-squared error (RMSE), establishing a new benchmark for accuracy and reliability in hybrid AOA-TDOA localization.