Joint Detection, Tracking, and Classification of Multiple Maneuvering Star-Convex Extended Targets

The extended target (ET) joint detection, tracking, and classification (JDTC) algorithms based on elliptical shape utilize rough target size information for classification, which makes it difficult to effectively solve the problem of accurate classification of similarly sized targets. Therefore, this article proposes a multiple maneuvering star-convex ET JDTC algorithm called JDTC-MM-CBMeMBer filter. First, the target extent state is modeled as a star-convex shape via the star-convex random hypersurface model (RHM). By modeling the target class-related prior information with vector form, we construct its relationship with the simultaneous extent state and integrate it into the Bayesian filter framework for joint processing. Second, to solve the implementation difficulty due to the high-dimensional target state and strong nonlinear observation model in the star-convex RHM, we model the target kinematic by two separate vectors and use the particle filter (PF) to update target class probability. Next, we use the multiple model (MM) cardinality balanced multitarget multi-Bernoulli (CBMeMBer) filter to derive the JDTC recursion process of the multiple maneuvering star-convex ETs. At last, the Gamma-Gaussian-Gaussian mixture implementation is present. The simulation results show that: 1) compared with the ET JDTC algorithm based on an elliptical shape, the proposed filter can accurately classify targets with similar sizes but different shapes and 2) compared with the multiple ET tracking algorithm based on star-convex RHM, the proposed filter can significantly improve the target state estimation results without significantly increasing the running time.