Abstract
This paper deals with the problem of detecting the moving range-extended target in the distributed MIMO radar. As the distributed MIMO radar is equipped with multiple transmit and receive antennas, the interference covariance matrices corresponding to different transmit-receive (Tx-Rx) antennas are modeled as random matrices which express nonhomogeneous environments in this paper first. Then a knowledge-aided model which makes these random matrices share a prior covariance matrix structure is built to simulate the characteristics of clutter and noise in nonhomogeneous environments. Finally, we design a new knowledge-aided Wald (KA-Wald) detector to detect the range-extended target for the distributed MIMO radar. Simulation results show that the proposed detector possesses a better detection performance compared with the traditional Wald detector. And relative to the knowledge-aided generalized likelihood ratio test (KA-GLRT) detector, the proposed KA-Wald detector has a similar detection performance but a higher detection efficiency.
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Acknowledgments
Part of this work was supported by National Nature Science Foundation of China (Grant No. 61471019, 61673146 and 61771028).
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Wang, N., Sun, J., Wang, W. (2018). Knowledge-Aided Wald Detector for Range-Extended Target in Nonhomogeneous Environments. In: Yuan, H., Geng, J., Liu, C., Bian, F., Surapunt, T. (eds) Geo-Spatial Knowledge and Intelligence. GSKI 2017. Communications in Computer and Information Science, vol 848. Springer, Singapore. https://doi.org/10.1007/978-981-13-0893-2_44
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DOI: https://doi.org/10.1007/978-981-13-0893-2_44
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