Multiple extended stealth target tracking from image observation based on non‐linear sub‐random matrices approach

The detection and tracking of extended stealth targets (ESTs) is a challenging task in radar technology, especially if from image observations because of the fluctuations of radar cross section. To overcome this challenge, multi‐Bernoulli (MB) filter can be used to extract the extended target (ET) states in efficient and reliable manner. Recently, the MB‐filter‐based random matrices model (RMM) approach has been proposed for ellipsoidal ET tracking with additional state variables. However, RMM‐MB filter is demonstrated with known detection profile, which is unsuitable for EST tracking. Thus, a joint detection and tracking of multiple ESTs based on track‐before‐detect (TBD) approach, which is an efficient way to track low‐observable ESTs, is proposed. In EST‐RMM‐TBD scenarios, although the extension ellipsoid is efficient, it may not be accurate because of some missing useful information, such as size, shape, and orientation. To address this, a EST‐sub‐RMM‐TBD composed of sub‐ellipses is introduced, each representing an RMM. Based on such models, a sub‐RMM‐MB‐TBD filter is used to estimate the kinematic states and extensions of sub‐objects for each EST. Furthermore, a sequential Monte Carlo (SMC) implementation to estimate non‐linear kinematic EST state is applied. The results indicate that the proposed SMC‐sub‐RMM‐MB‐TBD filter has more accurate cardinality estimation and smaller optimal sub‐pattern assignment errors than the recent extended tracking filters.