Multiple Extended Target Joint Tracking and Classification Based on GPs and LMB Filter

This letter proposes a novel multiple extended target (ET) joint tracking and classification (JTC) algorithm based on Gaussian processes (GPs) and labeled multi-Bernoulli (LMB) filter, called the ET-JTC-GP-LMB filter, which aims to track and classify simultaneously multiple ETs with the goal of improving estimation performance. Firstly, we construct the relationship between GP-based extension state and prior class information (PCI), and design a new class probability update method. Then, we integrate these two works into the GP-based ET LMB filtering framework, propose the ET-JTC-GP-LMB filter, and provide its gamma-Gaussian-Gaussian mixture implementation to form a closed recursion. Finally, we present an evaluation metric called class recognition rate (CRR) to evaluate classification performance. The simulation results demonstrate the superior performance of the proposed filter.