针对捷变频雷达目标参数估计与强欺骗干扰抑制问题,提出了一种适用于距离-速度维稀疏处理模型的超分辨-恒虚警(CFAR)-自适应重构算法,实现了目标搜索、距离-速度参数估计及欺骗干扰抑制。基于量化格点的距离-速度超分辨谱实现了目标距离-速度参数与字典元素的匹配,并采用CFAR 算法对距离速度平面进行二维搜索,以实现目标检测与距离速度参数估计。采用自适应重构对消方法从观测向量矩阵中减去已估计目标分量,迭代过程中更新二维谱噪声子空间维度,循环采用距离-速度谱、CFAR 及自适应对消实现剩余目标估计。通过每次迭代过程中的多目标估计与对消,解决了强欺骗干扰情况下的弱目标参数估计问题。该方法为捷变频雷达距离速度维稀疏表示欠定方程数学模型提供了另一种求解途径。

A super-resolution-constant false alarm(CFAR)-adaptive reconstruction algorithm suitable for sparse processing models in the range velocity dimension is proposed to address the problem of target parameter estimation and strong deception interference suppression in frequency agile radar. This algorithm achieves target search, range velocity parameter estimation and deception interference suppression. The distance velocity super-resolution spectrum based on quantized lattice points achieves the matching of target distance velocity parameters with dictionary elements, and uses the CFAR algorithm to perform two-dimensional search on the distance velocity plane to achieve target detection and distance velocity parameter estimation. The adaptive reconstruction cancellation method is used to subtract the estimated target components from the observation vector matrix. During the iteration process, the two-dimensional spectral noise subspace dimension is updated, and the distance velocity spectrum, CFAR, and adaptive cancellation are cyclically used to achieve the remaining target estimation. By using multi-objective estimation and cancellation during each iteration process, the problem of weak target parameter estimation under strong deception interference has been solved. An alternative approach to solve the underdetermined equation of the sparse representation is offered in the range and velocity dimensions for agile frequency radar.

TN957.51;TN911.23