Abstract
Nested Tail-Biting Convolutional Codes Construction for Short Packet Communications
Highlights
Adaptive beamforming is a classic problem in array signal processing and has been widely applied in radar, sonar, communications, microphone array processing, medical imaging and other fields [1]
We develop a null broadening robust beamforming algorithm based on decomposition and iterative second-order cone programming (NB-DISOCP)
Simulation results show that the proposed NB-DISOCP approach can effectively broaden the null width and enhance the null depth, and it is robust against steering vector (SV) mismatch
Summary
Adaptive beamforming is a classic problem in array signal processing and has been widely applied in radar, sonar, communications, microphone array processing, medical imaging and other fields [1]. In [17], a null broadening beamformer based on multi-parameter quadratic programming is proposed, which constrains the null depth and sidelobe level on the basis of the CMT method. Mao et al [20] propose a null broadening approach based on projection transformation and diagonal loading (PDL) It combines the CMT method and projection transformation, and the width and depth of its nulls are better than those of the CMT method’s. A suppression level is set to make the array responses on the angular sectors of non-stationary interferences lower than it, so as to control the width and depth of nulls. Simulation results show that the proposed NB-DISOCP approach can effectively broaden the null width and enhance the null depth, and it is robust against SV mismatch.
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