DOA Estimation Algorithm Based on Reduced-dimensional Beamspace with Real-valued ESPRIT for Monostatic MIMO Radar
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摘要: 单基地多输入多输出(MIMO)雷达的波达方向(DOA)估计问题是近年来研究的热点。高维度的MIMO雷达数据，导致传统旋转不变性参数估计技术(ESPRIT)算法需要付出较大的运算代价。在低信噪比、低快拍数的条件下，传统ESPRIT算法性能会严重下降。为了克服传统ESPRIT算法的以上缺点，该文提出一种降维波束空间的实值ESPRIT算法。该算法通过转换矩阵，将高维度MIMO雷达数据转换到低维度的数据，从而去除数据中的冗余。然后再将低维数据变换到波束空间，构造实值旋转不变性等式，用以估计目标的角度。仿真结果表明，在低信噪比和低快拍数时，相比于传统ESPRIT算法，该文所提方法具有更好的角度估计性能和更少的运算量。Abstract: The Direction Of Arrival (DOA) estimation is a hot topic for a monostatic Multiple Input Multiple Output (MIMO) radar in recent years. The conventional Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) algorithms need to pay much computation cost because of the high-dimensional MIMO radar data. When the Signal-to-Noise Ratio (SNR) is low and the number of sample are small, the performance of the conventional ESPRIT algorithms degrades seriously. To overcome the disadvantages of conventional ESPRIT algorithms, a novel algorithm which is called as reduced-dimensional beamspace with real-valued ESPRIT for monostatic MIMO radar is proposed. To eliminate the redundancy, the high-dimensional MIMO radar data is transformed into the low-dimensional data through the transformation matrix. To reduce further the computation complexity, the low-dimensional data is transformed into beamspace. Then the real-valued rotation invariance equation is constructed to estimate the target’s DOA. Simulation results show the proposed algorithm has better angle estimation performance and less computation burden than traditional ESPRIT algorithms.
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