Tracking multiple maneuvering targets hidden in the Doppler blind zone (DBZ) is a challenging problem. To overcome the complicated problem, we proposed a tracker based on the multiple model probability hypothesis density (MM-PHD) filter. However, the PHD filter is only the first-order moment approximation of the multi-target Bayesian filter, and it cannot output track labels in principle. In order to improve the tracking performance, another novel tracker is proposed in this paper. To track multiple maneuvering targets, the proposed tracker is built on the latest multiple model generalized labeled multi-Bernoulli (MM-GLMB) filter. Moreover, it incorporates the minimum detectable velocity (MDV) to suppress the DBZ masking. Finally, a measurement-driven adaptive track initiation is introduced to address the fixed track initiation problem of the standard MM-GLMB filter. It is demonstrated through numerical examples that the proposed tracker outperforms the existing work significantly, especially in terms of both accuracy and robustness of cardinality estimation.

To track multiple maneuvering targets hidden in the Doppler blind zone (DBZ), we have proposed an MM-GLMB-DBZ tracker based on the latest multiple model generalized labeled multi-Bernoulli (MM-GLMB) filter. To further enhance the tracking performance, this paper combines sensor control techniques to the MM-GLMB-DBZ tracker. Macroscopically, the proposed algorithm consists of the MM-GLMB-DBZ tracker and a controller. Unlike conventional control approaches where separate prediction and update implementations are usually adopted, the proposed control algorithm constructs a systematic process flow for the joint prediction and update implementation of GLMB-like filters. Moreover, inside the core controller module, we apply the previously designed safety indicator and reward function for avoiding the DBZ, and derive the Cauchy-Schwarz divergence (CSD) compatible with the tracker. Hence, this control algorithm considers such factors as the safety of the sensor itself, the DBZ avoidance, and the improvement of the tracking accuracy. Numerical examples verify the effectiveness of the proposed control scheme, showing that it is significantly better than the random control strategy, the original MM-GLMB-DBZ tracker without the control technology, and the state-of-the-art control approach.