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Sparsity-Based Direct Data Domain Space-Time Adaptive Processing with Intrinsic Clutter Motion

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Abstract

In this paper, we propose a sparsity-based direct data domain space-time adaptive processing (D3-STAP) algorithm for airborne radar that considers the intrinsic clutter motion (ICM). The proposed D3-STAP scheme models the received returns in the presence of ICM as a sparse measurement model. Then, we derive the principle of the sparsity-based D3-STAP that uses the focal underdetermined system solution (FOCUSS) method. The proposed D3-STAP algorithm estimates the clutter covariance matrix by a Hadamard product of the covariance matrix taper (CMT) and the clutter covariance matrix estimate with the FOCUSS technique. In addition, we develop a CMT adaptation approach for the proposed D3-STAP algorithm to automatically select the best CMT. Simulation results show that the proposed algorithm outperforms the existing D3-STAP using the least-squares technique and the sparsity-based D3-STAP algorithm without CMT.

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Notes

  1. The clutter rank can be estimated by counting the number of resolution grids that are occupied by the significant clutter spectrum components [28], which shows a prove for the conclusion in [33].

  2. Note that the above equation makes use of the fact that \(\left( \mathbf{A} \odot \mathbf{B}\right) \left( \mathbf{C} \odot \mathbf{D}\right) ^H = \left( \mathbf{A} \mathbf{C}^H\right) \odot \left( \mathbf{B} \mathbf{D}^H\right) ,\) where \(\mathbf{A}, \mathbf{B}\) are \(g \times h\) and \(\mathbf{C}, \mathbf{D}\) are \(e \times h\) matrices [7].

  3. This assumption is also seen in CMT methods developed by Guerci in [8].

  4. This is where we exploit both the orthogonality of \(\mathbf{Q}\) and the white Gaussianity of the thermal noise.

  5. Significant elements are defined by those whose powers are higher than the thermal noise level.

  6. Regarding the FOCUSS algorithm, the MATLAB code can be downloaded at http://dsp.ucsd.edu/~zhilin/MFCOUSS.m.

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Acknowledgments

This work was funded in part by National Natural Science Foundation of China under Grant 61401478.

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Authors and Affiliations

  1. College of Information Engineering, Shenzhen University, Shenzhen, Guangdong, China

    Zhaocheng Yang

  2. Research Institute of Space Electronics, Electronics Science and Engineering School, National University of Defense Technology, Changsha, 410073, China

    Yuliang Qin, Hongqiang Wang & Xiang Li

  3. Communications Research Group, Department of Electronics, University of York, York, YO10 5DD, UK

    Rodrigo C. de Lamare

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  1. Zhaocheng Yang
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  2. Yuliang Qin
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  4. Hongqiang Wang
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  5. Xiang Li
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Correspondence to Zhaocheng Yang.

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Yang, Z., Qin, Y., de Lamare, R.C. et al. Sparsity-Based Direct Data Domain Space-Time Adaptive Processing with Intrinsic Clutter Motion. Circuits Syst Signal Process 36, 219–246 (2017). https://doi.org/10.1007/s00034-016-0301-z

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  • DOI: https://doi.org/10.1007/s00034-016-0301-z

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