最小冗余线阵的ES-DOA估计算法研究

Study on ES-DOA Estimation Algorithm of Minimum-Redundancy Linear Arrays

  • 摘要: 采用最小冗余线阵可以显著增大天线的阵列孔径,但其相关矩阵不是Toeplitz矩阵,致使空间平滑解相干等方法失效,限制其在相干信号环境下的使用。结合信号功率估计,采用基于特征空间DOA估计算法,使得在较低信噪比情况下的DOA估计具有很高的精度。该算法同时采用了前后向空间平滑技术,不用减小阵列孔径就可以实现信号去相干。将该算法应用于最小冗余线阵,提高了阵列的DOA估计性能,且不影响阵列孔径。仿真结果证实了该算法的具有较高的精度和较强的鲁棒性。

     

    Abstract: Arrays aperture can be improved greatly with minimum-redundancy linear arrays(MRLA). But MRLA’s covariance matrix is not a Toeplitz matrix, which makes spatial smoothing decorrelation method and etc futile to constrain MRLA’s applications in correlated circumstances. Eigenspace-DOA (ESDOA) algorithm, uniting the estimation of signals’ powers, can produce high resolutions of DOA estimation with a lower SNR. ES-DOA algorithm, utilizing forward-back technique meanwhile, does no harm to arrays’ aperture. ES-DOA algorithm in MRLA has improved MRLA’s DOA estimation capability without impairing its aperture. Simulation results have also shown its high precision and robustness.

     

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