Joint DTC based on FISST and generalised Bayesian risk

Abstract

This study proposes a recursive solution to target joint detection, tracking, and classification (DTC) based on finite set statistics (FISST) and generalised Bayesian risk. A new Bayesian risk is defined involving the costs of target existence probability estimation (detection), state estimation (tracking), and classification. The estimates and costs are calculated within the FISST framework for different hypotheses and decisions of the target class, and the optimal solution is then derived to minimise the new Bayesian risk. As different costs are unified, the inter-dependence of DTC is considered, and these three sub-problems are solved jointly. The effects of the parameters of the new Bayesian risk are also analysed. Simulations show that the authors’ method has a better overall performance compared with traditional methods.