H-PMHT with a Poisson measurement model

Abstract

The Histogram Probabilistic Multi-Hypothesis Tracker (H-PMHT) is an efficient multi-target approach to the Track-before-detect (TkBD) problem. The tracking is based on the generation of a synthetic histogram by quantising the energy in the sensor data. The resultant quantised measurement is then modelled using a multinomial distribution and target state estimation is performed via Expectation-Maximisation based mixture modeling. This paper presents an alternative derivation of the H-PMHT based on a Poisson measurement model. The benefits of this new derivation are two-fold. First, direct estimation of the measurement likelihood is now possible under this new formulation, thereby eliminating any need for measurement quantisation. Second, the new derivation results in an improved measure for track quality by incorporating a time-correlated estimate for the target mixing proportions.