跟踪机动目标的雷达波形选择新方法

针对多项式预测模型描述的机动目标，提出了一种新的雷达波形选择算法。由于机动目标的运动方程满足多项式的规律，以运动目标的多项式预测模型作为状态方程，Kalman滤波器可以很好地跟踪目标的位移和速度信息,并得到估计误差以及其预测。利用跟踪器得到目标状态估计误差的预测误差椭圆为基准，通过分数阶傅里叶变换来旋转测量误差椭圆,以使得雷达的测量误差椭圆与目标跟踪算法对目标状态的估计误差的预测的误差椭圆正交，从而得到了最优的波形选择。仿真结果表明，所提出的算法在性能上优于对比算法。

New waveform selection approach to tracking maneuver targets

A new approach to waveform selection for tracking the maneuver target which is described by a polynomial prediction model is proposed. Because the motion equation of the target is a polynomial function of the time, the polynomial prediction model can describe the motion state of the target accurately. If the polynomial prediction model is taken as the state equation of the Kalman filter, the Kalman filter can be used to track the position and velocity of the target and predict the prior uncertainty error ellipse of the tracking system. According to the prior uncertainty error ellipse, the fractional Fourier transform (FrFT) is used to rotate the measurement error ellipse to make them orthogonal to each other. In this way, the optimal waveform is selected. The simulation results show that the proposed algorithm outperforms the constrast algorithms.