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The iterative search approach DOA estimation of monostatic L-shaped array MIMO radar

机译:L型阵列MIMO雷达的迭代搜索DOA估计

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In the DOA estimation of monostatic L-shaped array MIMO radar, Multiple Signal Classification algorithm is efficient. But the peak searching process of Multiple Signal Classification algorithm needs large amount of spectrum calculation. Focusing on the spectrum peak searching process of Multiple Signal Classification, an iterative search approach to reduce the calculation amount is proposed. The first- and second-order derivatives of Multiple Signal Classification spectrum functions are achieved and the calculation amount is analyzed. Two-dimensional Newton iteration methods are applied with multisearching threads and derivation information. The searching approach can greatly reduce the computational complexity of Multiple Signal Classification spectrum peak searching. The total calculation amount of the first and second derivatives is about 15 times of the spectrum function. However, in the two-dimensional searching, especially in the high accuracy processes, the amount of searched points can be reduced by ten hundreds times, and the computation is much lower than the common spectrum peak searching method. The simulation results show that when the search thread number reaches 100, the searching process can effectively achieve the entire spectrum peak and get the correct DOA estimation.
机译:在单静态L形阵列MIMO雷达的DOA估计中,多信号分类算法是有效的。但是多信号分类算法的峰值搜索过程需要大量的频谱计算。针对多信号分类的频谱峰值搜索过程,提出了一种减少计算量的迭代搜索方法。得到了多信号分类频谱函数的一阶和二阶导数,并对计算量进行了分析。二维牛顿迭代方法与多重搜索线程和派生信息一起应用。该搜索方法可以大大降低多信号分类频谱峰值搜索的计算复杂度。一阶和二阶导数的总计算量约为频谱函数的15倍。但是,在二维搜索中,尤其是在高精度处理中,搜索点的数量可以减少一百倍,并且计算量远低于普通频谱峰值搜索方法。仿真结果表明,当搜索线程数达到100时,搜索过程可以有效地实现整个频谱峰值,并获得正确的DOA估计。

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