Joint detection and tracking of non-ellipsoidal extended targets based on cubature Kalman-CBMeMBer sub-random matrices filter

Joint detection and tracking of multiple extended targets (ETs) from image observations is a challenging radar technology; especially for extended stealth targets (ESTs). This work provides a new approach for the ESTs tracking under the non-linear Gaussian system based on track-before-detect (TBD) approach. The sequential Monte Carlo cardinality-balanced multi-target multi-Bernoulli (SMC-CBMeMBer) filter provides a good framework to cope with TBD approach. However, this filter suffers from the particles’ degradation problem seriously; especially for ETs tracking. Recently, the cubature Kalman (CK)-CBMeMBer filter which employs a third-degree spherical-radical cubature rule has been proposed to handle the non-linear models, the CK-CBMeMBer filter is more accurate and more principled in mathematical terms compared to SMC-CBMeMBer filter. To this point, the authors address a TBD of ESTs with extended CK-CBMeMBer filter based on random matrix model (RMM), which is an efficient way to track ellipsoidal ESTs. In RMM-ESTs scenarios, although the extension ellipsoid is efficient, it may not be accurate enough because of lacking useful information, such as size, shape, and orientation. Therefore, they introduce a filter composed of sub-ellipses; each one is represented by a RMM. The results confirm the effectiveness and robustness of the proposed filter.