【摘要】 针对分布式相参雷达(distributed coherent aperture radar,DCAR)精确的目标参数估计问题,首先建立了以多普勒分复用(Doppler division multiple access,DDMA)波形作为发射波形、存在滤波器网格失配的DCAR信号模型,接着分析并验证了滤波器网格失配严重影响目标参数估计进而降低DCAR信号相参合成性能,最后提出了联合全局-局域搜索和基于稀疏傅里叶变换(sparse Fourier transform,SFT)的两种DCAR目标参数估计方法来降低滤波器网格失配,联合全局-局域搜索的方法通过对滤波器局域加密的方式降低网格失配。为了降低运算量,利用目标信号频率相对于整体频域是稀疏的特性,采用基于SFT搜索的方法通过梯度下降的方式避免滤波器的遍历搜索。仿真实验验证了所提方法的有效性。更多还原

【Abstract】 For the problem of accurate target parameter estimation in distributed coherent aperture radar(DCAR),a DCAR signal model,which takes the Doppler division multiple access(DDMA)waveform as the transmitting waveform and the grid mismatch filtering into consideration,is firstly established.Then the detrimental effects on the target parameter estimation caused by grid mismatch filtering are analyzed and validated,the results of which would decrease DCAR signal coherence-synthetic performance.Finally,two DCAR target parameter estimation methods based on joint global-local search and sparse Fourier transform(SFT)are proposed,respectively.The joint global-local search method weakens the grid mismatch effects by means of setting refined filters locally.To reduce the computation burden,taking advantages of the characteristic that target frequencies are sparse relative to the whole frequency domain,the search method based on SFT is employed,which avoids the traversal search of filters by the gradient descent method.The effectiveness of proposed method is verified by the simulation experiments.更多还原