A Dynamic Model-Based Doppler-Adaptive Correlation Filter for Maritime Radar Target Tracking

This article deals with the problem of tracking targets with X-band marine radars in the complicated sea clutter background. By jointly exploiting the target's kinematic and appearance information, we propose a novel dynamic model-based Doppler-adaptive correlation filter (DDACF). The proposed tracker mainly consists of two modules. First, a DACF is developed based on the kernel correlation filter. This filter can effectively represent the appearance patterns of the target and is adaptive to the motion Doppler by using a multifrequency-centered filter bank. Second, the Bernoulli filter is employed to represent the kinematic patterns of the target. Within this filter, the converted measurement Kalman filter with range rate (CMKFRR) is applied to overcome the inconsistency between coordinate systems of the motion and measurement models. By exploiting the hybrid measurement likelihood, the two modules are then fused within the Bayesian framework to achieve improved tracking performance in the complicated sea clutter background. Experimental results based on both the simulated and real radar data demonstrate that the proposed tracker outperforms its representative counterparts.