Cardinality Balanced Multi-Target Multi-Bernoulli filter for target tracking with amplitude information

The Cardinality Balanced Multi-Target Multi-Bernoulli (CBMeMBer) filter is a recursive Bayesian algorithm for estimating multiple target states with a varying target number in cluttered environment. Knowledge of the amplitude information plays a significant role in multi-target tracking and results in better tracking performance for harsh scenarios with low signal-to-ratio (SNR) or high false alarms, however, it has not been considered in CBMeMBer filter. In this paper, an improved CBMeMBer filter accommodating nonlinear measurement model, as well as unknown SNR, is proposed for radar sensors. Furthermore, the proposed filter is able to adaptively design the birth target density without the assumption that the birth density is known as a prior. Simulation results demonstrate the effectiveness and high estimation accuracy of the proposed filter over traditional CBMeMBer filter, particularly in the estimate of cardinality.