A belief propagation algorithm based on track-before-detect for tracking low-observable and manoeuvering targets using multiple sensors

Abstract

It is notoriously challenging work to track an unknown number of low-observable manoeuvering targets. In this paper, a sequential Bayesian inference method based on the multiple-model dynamic model and track-before-detect measurement (TBD) model is proposed for tracking low-observable manoeuvering targets using multiple sensors. The multiple-model dynamic model is capable to characterise the dynamic behaviour of manoeuvering targets. The TBD measurement model can completely capture an echo signal without any preprocessing, furtherly handling with low-observable targets. The authors’ proposed method is based on a new multi-sensor statistical model that allows targets to interact and contribute to more than one data cell for the pixeled image TBD approach. Based on the factor graph representing the multi-sensor statistical model, the marginal posterior densities are derived by performing the message passing equations of the proposed belief propagation algorithm for target detection and target state estimation. The simulation results validate that the computational complexity of our proposed multi-sensor BP-TBD algorithm scales in the number of sensor nodes and demonstrate that its performance is superior among the state-of-the-art multi-sensor TBD methods.