Maneuvering target tracking from nautical radar images using particle-Kalman filters

In this paper, a new combined particle-Kalman filter (PF-KF)-based visual tracking approach is designed for maneuvering target tracking from X-band nautical radar images. Unlike existing target tracking approaches used by nautical radar, this approach incorporates a histogram-based visual tracking strategy to estimate the target position and velocity. It applies a sampling importance resampling (SIR) particle filter to obtain preliminary target positions, and then a Kalman filter to derive refined target position and velocity. A Bhattacharyya coefficient-based similarity function is employed to compare the reference target and candidate target models, which are constructed by a kernel-based histogram in radar images. An enhanced reference target model construction method that employs constant false alarm rate (CFAR) processing to enable automatic determination of reference region is proposed to improve the tracking stability and accuracy. Comparison of the target information obtained by the proposed PF-KF method from various field X-band nautical radar image sequences with those measured by GPS shows the proposed approach can provide a reliable and flexible online target tracking for nautical radar application. It is also shown that, in the scenario of strong sea clutter, the proposed approach outperforms the PF-only-based approach and the classical tracking approach which combines order-statistics (OS) CFAR processing and the Kalman filter.