Parameter Estimation via Dictionary Subspace Projection for Sonar Target Angle Estimation

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Abstract
The beamforming obtains the azimuth estimation of the target by extracting the information from the data covariance matrix. However, the beamforming has much-unextracted information in the covariance matrix. Therefore, this paper proposes the Parameter Estimation via Dictionary Subspace Projection (PEDSP) algorithm that further extracts information from the covariance matrix. Through the regularization constraint, the spatial power distribution is limited to the signal subspace to ensure the correctness of the target estimation. In addition, the weighted is used to constrain the spatial power distribution vector to ensure that the targets in the space remain sparse. In the case of the above two constraints, the beamforming results are fitted by a dictionary, and the azimuth estimation of the target is obtained by quadratic programming. The experimental results show that the proposed algorithm can estimate the target azimuth despite the inaccurate sparse constraint. Furthermore, sea trial show that PEDSP can still get correct results in complex environments.
Funding Information
  • National Natural Science Foundation of China (52127901)

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