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中文摘要：
针对实际非均匀环境空时自适应处理（STAP）难以获取足够独立同分布样本的问题，该文提出一种基于压缩感知的运动目标检测方法。该方法首先将待检测距离门的回波数据变换至2维频率域进行能量积累，并构造相应2维频率域的冗余字典，然后利用贝叶斯压缩感知技术提取若干强静止散射点谱峰，估计杂波谱能量支撑区，最后通过基于加权的最小l1范数优化模型实现杂波抑制与运动目标检测。理论分析及仿真实验结果表明该方法具有较高的角度和多普勒分辨率，并且在低信噪比情况下也可以获得良好的检测效果。
英文摘要：
For conventional Space-Time Adaptive Processing （STAP）, sufficient Independent and Identically Distributed （IID） samples are hard to obtain by the clutter within heterogeneity. To mitigate this problem, a Compressive Sensing （CS） based Ground Moving Target Indication （GMTI） method is proposed. In the method, firstly the space-time data of the interested range bin are transformed to two-dimensional frequency fields to accumulate the signal energy and the corresponding redundant dictionary is constructed; Then several primary clutter spectrum peaks are extracted by Bayesian compressive sensing technique to estimate the clutter ridge; Finally, the weighted l1 minimization optimization model is constructed to realize the ground moving target indication. Simulation results and theory analysis demonstrate that the proposed method attains good resolution meanwhile it takes on excellent performance in low SNR.
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