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A Joint Doppler Frequency Shift and DOA Estimation Algorithm Based on Sparse Representations for Colocated TDM-MIMO Radar

机译:基于Colocated TDM-MIMO雷达稀疏表示的关节多普勒频移和DOA估计算法

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摘要

We address the problem of a new joint Doppler frequency shift (DFS) and direction of arrival (DOA) estimation for colocated TDM-MIMO radar that is a novel technology applied to autocruise and safety driving system in recent years. The signal model of colocated TDM-MIMO radar with few transmitter or receiver channels is depicted and “time varying steering vector” model is proved. Inspired by sparse representations theory, we present a new processing scheme for joint DFS and DOA estimation based on the new input signal model of colocated TDM-MIMO radar. An ultracomplete redundancy dictionary for angle-frequency space is founded in order to complete sparse representations of the input signal. The SVD-SR algorithm which stands for joint estimation based on sparse representations using SVD decomposition with OMP algorithm and the improved M-FOCUSS algorithm which combines the classical M-FOCUSS with joint sparse recovery spectrum are applied to the new signal model’s calculation to solve the multiple measurement vectors (MMV) problem. The improved M-FOCUSS algorithm can work more robust than SVD-SR and JS-SR algorithms in the aspects of coherent signals resolution and estimation accuracy. Finally, simulation experiments have shown that the proposed algorithms and schemes are feasible and can be further applied to practical application.
机译:我们解决了一个新的关节多普勒频率偏移(DFS)和到达方向(DOA)估计的CONOCOCATEDTDM-MIMO雷达,这是一种近年来应用于自动折叠和安全驾驶系统的新技术。描绘了几个发射器或接收器通道的光学TDM-MIMO雷达的信号模型,并证明了“时间变化转向载体”模型。灵感来自稀疏表示理论,我们为基于Colocated TDM-MIMO雷达的新输入信号模型提出了一种用于联合DFS和DOA估计的新处理方案。用于角度空间的超完整冗余字典是为了完成输入信号的稀疏表示。基于使用SVD分解的稀疏表示具有与OMP算法的稀疏表示的SVD-SR算法以及将具有关节稀疏恢复谱的经典M-Focuss的改进的M-Focuss算法应用于新的信号模型的计算来解决多重测量向量(MMV)问题。改进的M-Focuss算法可以在相干信号分辨率和估计精度的各方面上工作比SVD-SR和JS-SR算法更鲁棒。最后,仿真实验表明,所提出的算法和方案是可行的,可以进一步应用于实际应用。

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