Abstract:

The probability hypothesis density (PHD) filter is an effective algorithm for multitarget tracking, and the conventional PHD filter is only suitable for a single sensor system. Since the multisensor version of the PHD filter is possible but computationally intractable, some approximations are proposed in many practical applications. A heuristic approximation, named iteratedcorrector approximation, is the default approach for multisensor problems. However, the order of the sensors for updating impacts the filter results seriously in this algorithm. Then, a multisensor PHD filter is proposed to solve this problem, however, there is a scale unbalance problem in its implementation. Aiming at above problems, an improved algorithm is proposed, which calculates the joint likelihood function in the product manner and the scale factor in the summation manner respectively. Simulation results show that the proposed algorithm can solve the scale unbalance problem effectively and has a better performance than the iterated corrector approximation in terms of state filtering and target number estimation, which has a good application prospect.