Vessel fusion tracking with a dual-frequency high-frequency surface wave radar and calibrated by an automatic identification system
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摘要: 高频地波雷达(HFSWR)和自动船只确认系统(AIS)是船只跟踪的重要传感器。高频地波雷达可以用来跟踪探测区域的所有船只,而AIS只能用来确认合作船只的信息。由于海杂波的干扰,使用单频率地波雷达的船只跟踪会淹没在布拉格峰值的盲区里,改变探测频率是克服这一缺点的有效手段。在这种背景下,我们提出一种基于AIS校准的双频雷达融合探测算法。因为不同频率的地波雷达测量与AIS的测量值存在系统误差,所以AIS信息可以用来估计和校准地波雷达的每个频率的系统误差。首先,将合作目标的点迹测量与地波雷达的点迹测量通过JVC分配算法进行点迹关联。从合作船只的点迹关联结果中,双频雷达的系统误差可以估计和校准。其次,基于校准的双频雷达数据,使用融合JPDA-UKF算法进行船只跟踪。通过真实探测的数据的实验结果显示所提算法可以实时跟踪船只,相比单频率跟踪可以进一步提高跟踪能力和跟踪精度。Abstract: High-frequency surface wave radar (HFSWR) and automatic identification system (AIS) are the two most important sensors used for vessel tracking. The HFSWR can be applied to tracking all vessels in a detection area, while the AIS is usually used to verify the information of cooperative vessels. Because of interference from sea clutter, employing single-frequency HFSWR for vessel tracking may obscure vessels located in the blind zones of Bragg peaks. Analyzing changes in the detection frequencies constitutes an effective method for addressing this deficiency. A solution consisting of vessel fusion tracking is proposed using dual-frequency HFSWR data calibrated by the AIS. Since different systematic biases exist between HFSWR frequency measurements and AIS measurements, AIS information is used to estimate and correct the HFSWR systematic biases at each frequency. First, AIS point measurements for cooperative vessels are associated with the HFSWR measurements using a JVC assignment algorithm. From the association results of the cooperative vessels, the systematic biases in the dual-frequency HFSWR data are estimated and corrected. Then, based on the corrected dual-frequency HFSWR data, the vessels are tracked using a dual-frequency fusion joint probabilistic data association (JPDA)-unscented Kalman filter (UKF) algorithm. Experimental results using real-life detection data show that the proposed method is efficient at tracking vessels in real time and can improve the tracking capability and accuracy compared with tracking processes involving single-frequency data.
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