研究了多目标环境中的认知雷达目标跟踪问题,提出了一种基于波形优化和快速粒子滤波的多目标跟踪方法.在量测模型中,基于采样的接收数据建立量测方程,以克服多目标跟踪中的数据关联问题;在状态模型中,与量测模型相匹配,联合估计目标运动状态(位置、速度)和散射系数.为实现多目标跟踪和提高跟踪性能,从联合收发自适应处理角度出发设计跟踪算法和发射波形:1)接收自适应.由于量测数据的维数以及跟踪模型的非线性程度较高,为实现对多目标的有效跟踪以及降低跟踪算法的运算复杂度,采用改进的粒子滤波方法对目标状态进行实时估计；2)发射自适应.考虑到信噪比与跟踪性能关系以及量测模型的特点,基于最优信噪比准则实现了对发射波形的优化.仿真结果表明文中所提出的跟踪方法能够有效的跟踪上目标,且所设计的自适应波形的跟踪性能优于传统固定波形.%The problem of target tracking for cognitive radar in the multiple-target environment is studied, and a tracking method for tracking multiple targets is proposed based on waveform optimization and quick particle filter. In the measurement model, the measurement equation is modeled by the sampled received data to avoid the problem of data association. In the state model, the positions, velocities and the scattering coefficients of multiple targets are estimated jointly to match the measurement model. Consider cognitive radar which can transmit and receive signals adaptively: 1) receive signals adap-tively. In order to decrease the computing complexity caused by the high dimension of measurement data, the modified particle filter is used to track the multiple targets; 2) transmit signals adaptively. Based on the positive relation between signal-to-noise rate (SNR) and tracking performance, the transmitted waveform is optimized based on SNR criterion. Simulation results show that the multiple targets can be precisely tracked by the proposed method, and the tracking performance of a-daptive waveform is better than that of traditional fixed waveform.
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