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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >A Novel Two-Dimensional Sparse-Weight NLMS Filtering Scheme for Passive Bistatic Radar
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A Novel Two-Dimensional Sparse-Weight NLMS Filtering Scheme for Passive Bistatic Radar

机译：被动双基地雷达的新型二维稀疏加权NLMS滤波方案

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摘要

In passive bistatic radars, weak target echoes may often be masked by direct path interference, multipath components, and strong target echoes, making weak target detection a challenging problem. The conventional 1-D adaptive cancelation algorithms, such as the normalized least mean square (NLMS), cannot effectively suppress strong target echoes when their Doppler frequencies spread. In addition, the continuous distribution of the NLMS weight vector does not match the sparse characteristics of strong multipath components and target echoes, thus resulting in degraded cancelation performance. Motivated by this fact, a novel 2-D sparse-weight NLMS filtering scheme is proposed by extending the NLMS to a 2-D structure, in which the weight vector is sparsely distributed and adaptively adjusted based on the sparse strong multipath components and target echoes.
机译：在无源双基地雷达中，弱目标回波通常会被直接路径干扰，多径分量和强目标回波所掩盖，这使得弱目标检测成为一个难题。常规的一维自适应消除算法（如归一化最小均方（NLMS））在多普勒频率扩展时不能有效地抑制强目标回波。此外，NLMS权向量的连续分布与强多径分量和目标回波的稀疏特征不匹配，从而导致抵消性能下降。基于这一事实，提出了一种新的2-D稀疏加权NLMS滤波方案，该方法将NLMS扩展为2-D结构，其中基于稀疏的强多径分量和目标回波来稀疏分布和自适应调整权重矢量。

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