Abstract
A new beamformer based on covariance matrix reconstruction is introduced. The essence of the new approach is to eliminate the desired signal component in the sample covariance matrix and thus complex integral operation is avoided in the procession of covariance matrix reconstruction. Besides, the actual array steering vector is estimated by a new technique. Contrary to other reference beamformers, simulation results demonstrate the effectiveness of our proposed method.
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Acknowledgment
This work was supported in part by the Natural Science Foundation of Tianjin under Grant No. 18JCQNJC01500, by the National Key Research and Development Program of China under Grant No. 2017YFB0102501, by the Artificial intelligence Science and Technology Support planning Major project of Tianjin under Grant No. 17ZXRGGX00070, by the Tianjin Municipal Science and Technology Innovation Platform, Intelligent Transportation Coordination Control Technology Service Platform under Grant No. 16PTGCCX00150 and by the Scientific Research Program of Tianjin Municipal Education Committee under Grant No. JWK1609.
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Guan, Z., Shi, H., Zhang, L., Ma, N. (2019). Low-Complexity Robust Adaptive Beamforming Based on Covariance Matrix Reconstruction. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 515. Springer, Singapore. https://doi.org/10.1007/978-981-13-6264-4_24
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DOI: https://doi.org/10.1007/978-981-13-6264-4_24
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