Improved relaxation algorithm for passive sensor data association

Abstract

The Lagrangian relaxation algorithm is widely used for passive sensor data association. However, there are two major problems about it. Firstly, the cost function of the algorithm is computed by using least square estimation of the target position without taking the estimation errors into account. To solve this problem, a modified cost function is derived which can reflect the correlation between measurements more reasonably owing to the integration of estimation errors. Secondly, due to the fact that building the candidate assignment tree would take a lot of CPU time, we propose a statistic test based on indicator function with a great improvement of the computational efficiency. Simulation results show that both the correlation accuracy and the computational efficiency of the improved relaxation algorithm are higher than that of the traditional one.