2-D Pair-Matching Method for L-shaped Array Based on CS Theory
首发时间:2020-05-20
Abstract:To address the problems of high computational complexity and angle mismatch in 2-D DOA estimation using CS theory in the intelligent vehicle radar communication integrated system, this paper proposes a new method based on compressed sensing. Combined with the particularity of L-shaped array structure, this method firstly uses SVD processing to obtain a low-dimensional data matrix; secondly, defines the spatial synthesis angle for secondary dimensionality reduction; then uses the OMP algorithm and mathematical geometry to inversely obtain sparse parameters; finally, the subspace projection is used to achieve angle matching, so as to obtain the correct 2-D DOA estimation result. The theoretical derivation and experimental simulations show that this algorithm greatly reduces the computational complexity and improves the probability of pairing success. It also has obvious advantages for different SNR, array element number and snapshot number.
keywords: wireless communication technology radar communication Intergration compressed sensing(CS) singular value decomposition(SVD) redundant dictionary subspace projection
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基于压缩感知理论的L型阵列二维配对算法
摘要:针对智能汽车雷达通信一体化系统中利用压缩感知理论进行二维DOA估计中计算复杂度高和角度匹配错误的问题,论文提出了一种基于压缩感知的二维波达方向估计新方法。该方法结合L型阵列结构的特殊性,利用奇异值分解得到低维数据矩阵,再定义空间合成角进行二次降维,并借助OMP算法和数学几何反推得到稀疏参数,最后基于子空间正交的原理,得到正确的二维DOA结果。通过理论推导和实验仿真表明了该算法在极大降低计算复杂度的同时,提高了配对成功概率,对于不同信噪比、阵元数和快拍数也均有明显的优势。
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