Sensor Bias Estimation for Track-to-Track Association

Abstract

In this letter, we propose a heuristic method to address sensor bias estimation to improve track-to-track association accuracy. A novel multi-parameter cost function is derived from rigid transformation function and it is minimized by the covariance matrix adaptation evolution strategies algorithm. The proposed method is compared to other recognized methods under various simulation scenarios. The comparison results confirm that our approach accurately estimates sensor biases, provides higher correct association probability with low computational load compared to the competitor methods, and also it is robust to high missed and false track rates.