Total Difference Based Partial Sparse LCMV Beamformer

Abstract

Abstract-Recent research demonstrates that sparse beam pattern constraint can suppress the sidelobe level of the linear constraint minimum variance beamformer. Here we improve the standard beam pattern by replacing it with a combination of a total difference minimization constraint on the mainlobe and a standard C 1 norm minimization constraint on the sidelobe. As the new constraint matches the practical beam pattern better, the sidelobe level is further suppressed, while the robustness against the mismatch between the steering angle and the direction of arrival (DOA) of the desired signal, is maintained.

Adaptive Beamforming With Low Side Lobe Level Using Neural Networks Trained by Mutated Boolean Pso

“…Hw +w HR nw (16) (17) One of the parameters used to measure the effectiveness of a beamformer is the signal-to-interference-plus-noise ratio (SINR). Due to (15) and (17), SINR can be calculated by:…”

“…Our study presents a new adaptive beamforming (ABF) technique suitable for antenna arrays [3][4][5][6][9][10][11][12][13][14][15][16][17]. The technique is based on neural networks (NNs) [5,[18][19][20][21][22][23][24][25][26], which use training sets produced by a novel binary variant of Particle Swarm Optimization (PSO) [27][28][29][30][31][32][33], called Mutated Boolean PSO (MBPSO) [10].…”

“…Taking into account that the geometry of the array is time-independent, a dynamically shaped pattern is achieved by applying on the array elements appropriate excitation weights that vary with time. These weights are calculated in real time by beamforming techniques [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Therefore, the beamforming algorithm must be completed as fast as possible.…”

A Robust Direct Data Domain Least Squares Beamforming With Sparse Constraint

“…Recently, sparse constraint on beam pattern has been used in beamforming algorithms [22][23][24] to gain performance improvement. Some modified sparse constraints are used in [25,26] to enhance the performance of sidelobe suppression. In this paper, the sparsity of the beam pattern is used in the D 3 LS algorithm to reduce the high sidelobe level while sustain its capability to suppress interference and noise within a snapshot.…”

Space-Time Adaptive Processing Based on Weighted Regularized Sparse Recovery

“…Recently, motivated by sparse representaton/sparse recovery (SR) techniques used in radar [5][6][7][8], several authors have considered SR ideas for moving target indication (MTI) and STAP problems, such as sparse-recovery-based STAP type (SR-STAP) algorithms in [9][10][11][12][13][14][15], L1-regularized STAP filters in [16,17], etc.. The basic idea of SR-STAP type algorithms is to regularize a linear inverse problem by including prior knowledge that the clutter spectrum is sparse in the angle-Doppler plane.…”