Null broadening adaptive beamforming based on covariance matrix reconstruction and similarity constraint

In this paper, a procedure for the null broadening algorithm design with respect to the nonstationary interference is proposed. In contrast to previous works, we first impose nulls toward the regions of the nonstationary interference based on the reconstruction of the interference-plus-noise covaria...

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Vydáno v:EURASIP journal on advances in signal processing Ročník 2017; číslo 1; s. 1 - 10
Hlavní autoři: Qian, Junhui, He, Zishu, Xie, Julan, Zhang, Yile
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 03.01.2017
Springer
Springer Nature B.V
Témata:
Advanced Techniques for Radar Signal Processing
Algorithms
Covariance matrix
Engineering
Interference
Mathematical analysis
Mathematical models
Optimization
Quantum Information Technology
Reconstruction
Signal,Image and Speech Processing
Similarity
Spintronics
Semidefinite programming
Similarity constraint
Quadratically constrained quadratic programming
Robust adaptive beamforming
ISSN:1687-6180, 1687-6172, 1687-6180
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Shrnutí:In this paper, a procedure for the null broadening algorithm design with respect to the nonstationary interference is proposed. In contrast to previous works, we first impose nulls toward the regions of the nonstationary interference based on the reconstruction of the interference-plus-noise covariance matrix. Additionally, in order to provide a restriction on the shape of the beam pattern, a similarity constraint is enforced at the design stage. Then, the adaptive weight vector can be computed via maximizing a new signal-to-interference-plus-noise ratio (SINR) criterion subject to similarity constraint. Mathematically, the design original problem is expressed as a nonconvex fractional quadratically constrained quadratic programming (QCQP) problem with additional constraint, which can be converted into a convex optimisation problem by semidefinite programming (SDP) techniques. Finally, an optimal solution can be found by using the Charnes-Cooper transformation and the rank-one matrix decomposition theorem. Several numerical examples are performed to validate the performance of the proposed algorithm.
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ISSN:1687-6180
1687-6172
1687-6180
DOI:10.1186/s13634-016-0440-1