TDOA Positioning Based on Bayesian Regularization Method
Hongbin Zhu · 2025
In the passive location system based on time difference of arrival (TDOA), the geometric relationship between the base station and the target is an important factor affecting the location accuracy. Under such conditions, the positioning equations become ill-conditioned, resulting in notably amplified positioning errors. To mitigate such ill-conditioned problems and minimize the impact of unfavorable geometric configurations on positioning accuracy, this paper proposes a Bayesian regularization framework that constrains the solution's feasible region while enhancing numerical stability. The incorporation of a regularization term derived from prior information within the Bayesian framework has substantially improved positioning accuracy at locations characterized by unfavorable base station-target geometries compared to unoptimized performance.