Unknown Clutter Estimation by FMM Approach in Multitarget Tracking Algorithm

Abstract EN

Finite mixture model (FMM) approach is a research focus in multitarget tracking field.

The clutter was treated as uniform distribution previously.

Aiming at severe bias caused by unknown and complex clutter, a multitarget tracking algorithm based on clutter model estimation is put forward in this paper.

Multitarget likelihood function is established with FMM.

In this frame, the algorithms of expectation maximum (EM) and Markov Chain Monte Carlo (MCMC) are both consulted in FMM parameters estimation.

Furthermore, target number and multitarget states can be estimated precisely after the clutter model fitted.

Association between target and measurement can be avoided.

Simulation proved that the proposed algorithm has a good performance in dealing with unknown and complex clutter.