Adaptive Reduced-Rank Processing Based on Joint and Iterative Interpolation, Decimation, and Filtering

Reduced-Rank STAP Schemes for Airborne Radar Based on Switched Joint Interpolation, Decimation and Filtering Algorithm

“…The joint domain localized (JDL) approach, which is a beamspace reduced-dimension algorithm, was proposed by Wang and Cai [22] and investigated in both homogeneous and nonhomogeneous environments in [23], [24], respectively. Recently, reduced-rank filtering algorithms based on joint iterative optimization of adaptive filters [25], [26] and based on an adaptive diversity-combined decimation and interpolation scheme [27], [28] were proposed, respectively.…”

“…The joint domain localized (JDL) approach, which is a beamspace reduced-dimension algorithm, was proposed by Wang and Cai [22] and investigated in both homogeneous and nonhomogeneous environments in [23], [24], respectively. Recently, reduced-rank adaptive processing algorithms based on joint iterative optimization of adaptive filters [25], [26] and based on an adaptive diversity-combined decimation and interpolation scheme [27], [28] were proposed, respectively. In our prior work [26], a joint iterative optimization of adaptive filters STAP scheme using the linearly constrained minimum variance (LCMV) was considered and applied to airborne radar applications, resulting in a significant improvement both in convergence speed and SINR performance as compared with the existing reduced-rank STAP algorithms.…”

Robust Rank Reduction Algorithm with Iterative Parameter Optimization and Vector Perturbation

“…Reduced-rank signal processing techniques [7][8][9][10][11][12][13][14][15][16][17] provide a way to address some of the problems mentioned above. Reduced dimension methods are often needed to speed up the convergence of beamforming algorithms and reduce their computational complexity.…”

“…It offers a large reduction in the required number of training samples over full-rank methods [2], which may also addresses the problem of snapshot deficiency at low complexity. Several reduced-rank strategies for processing data collected from a large number of sensors have been reported in the last few years, which include beamspace methods [7], Krylov subspace techniques [13,14] and methods of joint and iterative optimization of parameters in [15][16][17].…”

Detecção De Sinais No Enlace Reverso De Sistemas Mimo De Larga Escala Com Múltiplos Usuários E Células

“…Outra estratégia de redução de complexidade foi proposta em [36]. Nesse caso, são utilizadas técnicas de redução de dimensionalidade [58] de forma a se alcançar melhores relações custo-benefício entre complexidade e desempenho.…”