Multitarget tracking with the IMM and Bayesian networks: Empirical studies

This paper concentrates on multitarget tracking (MTT) simulation. The purpose of this paper is to simulate 11 targets in the noisy environment. The sensors used in the simulations are passive. First, we use the interactive multiple model (IMM) algorithm with the probabilistic data association (PDA) algorithm. The PDA is not capable to process attribute observations (i.e. observations of features such as the form of wings, radio frequency etc.). Therefore we have applied Bayesian networks to our tracking system, since they are capable to process attribute observations. The main gain of using the Bayesian networks is that the type of the target is possible to determine. In this paper we briefly recapitulate the most important features of the IMM, PDA and Bayesian networks. We also discuss how the establish attribute association probabilities, which are possible to fuse with the association probabilities computed by the PDA. We have executed the simulations 30 times. In this study we show one typical example of tracking with IMM and PDA as well as tracking with IMM, PDA and Bayesian networks. We conclude that tracking results with IMM and PDA are quite satisfactory. Tracking using the Bayesian networks produces slightly better results and identified the targets correct.