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        Waveform Self-Adaption Data Association Algorithm for Cognitive Radar Tracking

        WANG Shu-liang;BI Da-ping;RUAN Huai-lin;Institute of Electronic Contermeasure,National University of Defence Technology;Key Laboratory of Electronic Restriction;  
        For the multiple cross-maneuvering targets tracking in the background of clutter,a waveform self-Adaption data association algorithm for cognitive radar tracking is proposed. This algorithm chooses the range-velocity-bearing as the measurement,and adjusts the waveform parameters to vary the error covariance of the measurement dynamically. Firstly,an optimization probability data association algorithm(OPDA) is given based on the information fusion theory. This algorithm fuses the target position characteristics and motion characteristics to classify the public measurement in the cross area,and makes the multiple cross-maneuvering targets tracking problem into the multiple single-maneuvering target tracking problem. Secondly,the Riccati equation is used to estimate the filtering covariance for the updated target track,and the next waveform is chosen adaptively to improve the tracking performance according to the criterion function of the waveform selection. Simulation results show that this algorithm enhances the environment adaptability of the PDA algorithm,and has superiority than the algorithm without waveform self-adaption.
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