基于压缩感知的机动目标径向加速度估计

针对目前关于目标径向加速度估计的算法存在着采样频率过大及短时条件下精度不高的问题，提出了一种基于压缩感知（compressive sensing, CS）的机动目标径向加速度估计方法。该方法可以在不损失参数估计精度的条件下，用远低于奈奎斯特采样定理要求的采样速率进行采样。仿真实验验证了该方法有效性，并与基于分数阶傅里叶变换(fractional Fourier transform, FRFT)估计目标径向加速度方法进行了比较。仿真结果表明,该方法不仅所需的信号积累时长和采样速率大大降低，并且在估计精度方面也有明显的提高。

Radial acceleration estimation of maneuvering target based on compressive sensing

Due to the disadvantage of sampling at a high rate and long time duration in the current methods, a novel algorithm of radial acceleration estimation is proposed for maneuvering target based on compressive sensing (CS). In this method, the analyzed signal is sampled at a rate lower than Nyquist sampling theorem without sacrificing the accuracy of parameter estimate. The simulation results show that the proposed algorithm can effectively improve the precision of acceleration estimation with sampling at a low rate and short time compared with the fractional Fourier transformation (FRFT).