Particle probability hypothesis density filtering has become a promising approach for multi-target tracking due to its capability of handling an unknown and time-varying number of targets in a nonlinear, non-Gaussian system. However, its computational complexity linearly increases with the number of obtained observations and the number of particles, which can be very time consuming, particularl...

The cardinalized probability hypothesis density (CPHD) filter is an alternative approximation to the full multi-target Bayesian filter for tracking multiple targets. However, although the joint propagation of the posterior intensity and cardinality distribution in its recursion allows more reliable estimates of the target number than the PHD filter, the CPHD filter suffers from the spooky effec...

The cardinalized probability hypothesis density (CPHD) filter is a powerful tool for multitarget tracking (MTT). However, conventional CPHD filter discriminates targets from clutter only via the motion information, which is not reasonable in the situation of dense clutter. In the tracking, the amplitude of target returns is usually stronger than those coming from clutter, so the amplitude infor...

The unified cardinalized probability hypothesis density (CPHD) filters for extended targets and unresolved targets are proposed. The theoretically rigorous measurementupdate equations for the proposed filters are derived according to the theory of random finite set (RFS) and finite-set statistics (FISST). By assuming that the predicted distributions of the extended targets and unresolved target...