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

Author

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  • Tao Chen
  • Huanxin Wu
  • Lutao Liu

Abstract

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.

Suggested Citation

  • Tao Chen & Huanxin Wu & Lutao Liu, 2014. "A Joint Doppler Frequency Shift and DOA Estimation Algorithm Based on Sparse Representations for Colocated TDM‐MIMO Radar," Journal of Applied Mathematics, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnljam:v:2014:y:2014:i:1:n:421391
    DOI: 10.1155/2014/421391
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    References listed on IDEAS

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    1. Shen Yin & Guang Wang & Xu Yang, 2014. "Robust PLS approach for KPI-related prediction and diagnosis against outliers and missing data," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(7), pages 1375-1382, July.
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