在雷达高速机动目标的检测中,目标的运动特性会带来距离走动和多普勒徙动现象,不利于后续进行相参积累。传统算法为解决目标运动造成的影响,需要对目标的运动参数进行搜索,加大了计算量。针对上述问题,文中提出了一种基于慢时间序列反转变换(TRT)-二阶keystone 变换(SKT)-高阶模糊函数(HAF)的变加速目标快速相参积累算法。该算法通过TRT 和SKT 校正距离走动,利用HAF 估计目标加速度并构建相位补偿函数以校正多普勒徙动。与广义Radon-Fourier 变换相比,所提方法无需进行任何参数搜索,计算复杂度减少了3 个数量级。仿真结果表明,所提方法在检测门限上相比于Radon 分数阶模糊函数算法降低了5 dB,在低信噪比下有更好的检测性能。

When detecting high-speed maneuvering targets with radar, their motion characteristics can result in distance walking and Doppler migration, which hinders the accumulation of phase reference. To address the effects of target motion, various motion parameters are searched typically in traditional algorithms which increases computational requirements. In this paper, a fast phase parameter accumulation algorithm for variable acceleration targets based on time reversing transform (TRT)-second-order keystone transform (SKT)-high-order ambiguity function (HAF) is introduced to address the above issues. The SKT and TRT are used to compensate for walking distance. Phase compensation function constructed by HAF estimation of the target acceleration is used to compensate for Doppler migration. The proposed method has a computational complexity that is three orders of magnitude lower than the generalized Radon-Fourier transform, and does not require any parameter search. The simulation results demonstrate that the proposed method reduces the detection threshold by 5 dB in comparison to the Radon-fractional ambiguity function and exhibits superior detection performance at low signal-to-noise ratios.

TN957.51

国家自然科学基金资助项目(62172321)