Modified cost function for passive sensor data association

The Lagrangian relaxation algorithm is an effective approach to solve the problem of passive sensor data association. However, the cost function of the algorithm is computed by using least squares 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. The simulation results show that the improved relaxation algorithm based on the modified cost function has better performance than the original one, implying good application prospects.