scholarly journals Doubly Constrained Robust Blind Beamforming Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Xin Song ◽  
Jingguo Ren ◽  
Qiuming Li
Keyword(s):  
Performance Optimization ◽  
Computational Cost ◽  
Taylor Series Expansion ◽  
Weight Vector ◽  
Constant Modulus Algorithm ◽  
Worst Case ◽  
Steering Vector ◽  
Complexity Cost ◽  
Signal Steering Vector Mismatches ◽  
Output Sinr

We propose doubly constrained robust least-squares constant modulus algorithm (LSCMA) to solve the problem of signal steering vector mismatches via the Bayesian method and worst-case performance optimization, which is based on the mismatches between the actual and presumed steering vectors. The weight vector is iteratively updated with penalty for the worst-case signal steering vector by the partial Taylor-series expansion and Lagrange multiplier method, in which the Lagrange multipliers can be optimally derived and incorporated at each step. A theoretical analysis for our proposed algorithm in terms of complexity cost, convergence performance, and SINR performance is presented in this paper. In contrast to the linearly constrained LSCMA, the proposed algorithm provides better robustness against the signal steering vector mismatches, yields higher signal captive performance, improves greater array output SINR, and has a lower computational cost. The simulation results confirm the superiority of the proposed algorithm on beampattern control and output SINR enhancement.

scholarly journals Robust adaptive beamforming with enhancing the interference suppression capability

2019 ◽  
Vol 8 ◽  
Author(s):  
Linxian Liu ◽  
Yang Li
Keyword(s):  
Performance Optimization ◽  
Signal To Noise Ratio ◽  
Interference Suppression ◽  
Adaptive Beamforming ◽  
Training Data ◽  
Worst Case ◽  
Diagonal Loading ◽  
Steering Vector ◽  
Robust Adaptive Beamforming ◽  
Robust Adaptive

AbstractThe steering vector mismatch causes signal self-nulling for adaptive beamforming when the training data contain the desired signal component. To prevent signal self-nulling, many beamformers use robust technology, which is usually equivalent to the diagonal loading approach. Unfortunately, the diagonal loading approach achieves better signal enhancement at the cost of losing its interference suppression capability, especially at high input signal-to-noise ratio. In this paper, a novel robust adaptive beamforming method is developed to improve the interference suppression capability. The proposed beamformer is based on the worst-case performance optimization technology with a new estimated steering vector and a special set parameter. Firstly, a subspace which is orthogonal to the interference's steering vector is obtained by using the interference-plus-noise covariance matrix; then a new steering vector which is orthogonal to each interference's steering vector is estimated; finally, the beamformer's weight is solved with the worst-case performance optimization technology with a special set parameter. Theoretical analysis of the interference suppression principle is analyzed in detail, and some simulation results are presented to evaluate the performance of the proposed beamformer.

Efficient Robust Blind Beamforming Algorithm

2011 ◽  
Vol 204-210 ◽  
pp. 1390-1393
Author(s):  
Xin Song ◽  
Jin Kuan Wang ◽  
Bin Wang
Keyword(s):  
Reference Signal ◽  
Adaptive Beamforming ◽  
Constant Modulus Algorithm ◽  
Steering Vector ◽  
Blind Adaptive ◽  
Blind Beamforming ◽  
Significant Performance ◽  
The Mean ◽  
Signal Steering Vector Mismatches ◽  
Beamforming Algorithm

Because blind adaptive beamforming algorithms do not depend on any reference signal, they have found numerous important applications in signal processing. However, the conventional constrained constant modulus algorithm (CMA) may suffer significant performance degradation in the presence of the slight mismatches between the actual and assumed signal steering vectors. In this paper, to combat the mismatches, a novel robust constrained CMA is proposed for implementing double constraints with recursive method updating, which is based on explicit modeling of uncertainties in the desired signal array response. The proposed robust constrained CMA provides an improved robustness against the signal steering vector mismatches, enhances the array system performance under random perturbations in sensor parameters and makes the mean output array SINR consistently close to the optimal one. The performance of the proposed algorithm is compared with that of linear constrained CMA algorithm by computer simulations, the results of which demonstrate a marked improvement in the robustness against signal steering vector mismatches.

scholarly journals Robust Adaptive Beamforming Based on Worst-Case and Norm Constraint

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Hongtao Li ◽  
Ke Wang ◽  
Chaoyu Wang ◽  
Yapeng He ◽  
Xiaohua Zhu
Keyword(s):  
Performance Optimization ◽  
Adaptive Beamforming ◽  
Worst Case ◽  
Steering Vector ◽  
Robust Adaptive Beamforming ◽  
Simulation Results ◽  
Robust Adaptive ◽  
Norm Constraint

A novel robust adaptive beamforming based on worst-case and norm constraint (RAB-WC-NC) is presented. The proposed beamforming possesses superior robustness against array steering vector (ASV) error with finite snapshots by using the norm constraint and worst-case performance optimization (WCPO) techniques. Simulation results demonstrate the validity and superiority of the proposed algorithm.

scholarly journals Robust Recursive Algorithm under Uncertainties via Worst-Case SINR Maximization

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Xin Song ◽  
Feng Wang ◽  
Jinkuan Wang ◽  
Jingguo Ren
Keyword(s):  
Recursive Algorithm ◽  
Weight Vector ◽  
Training Data ◽  
Worst Case ◽  
Diagonal Loading ◽  
Fast Convergence Rate ◽  
The Mean ◽  
Simulation Results ◽  
Signal Steering Vector Mismatches ◽  
Sinr Maximization

The performance of traditional constrained-LMS (CLMS) algorithm is known to degrade seriously in the presence of small training data size and mismatches between the assumed array response and the true array response. In this paper, we develop a robust constrained-LMS (RCLMS) algorithm based on worst-case SINR maximization. Our algorithm belongs to the class of diagonal loading techniques, in which the diagonal loading factor is obtained in a simple form and it decreases the computation cost. The updated weight vector is derived by the descent gradient method and Lagrange multiplier method. It demonstrates that our proposed recursive algorithm provides excellent robustness against signal steering vector mismatches and the small training data size and, has fast convergence rate, and makes the mean output array signal-to-interference-plus-noise ratio (SINR) consistently close to the optimal one. Some simulation results are presented to compare the performance of our robust algorithm with the traditional CLMS algorithm.

Feasibility Robust Optimization via Scenario Generation and Reduction

2018 ◽  
Author(s):  
Eliot Rudnick-Cohen ◽  
Jeffrey W. Herrmann ◽  
Shapour Azarm
Keyword(s):  
Robust Optimization ◽  
Optimization Problems ◽  
Computational Cost ◽  
Optimization Techniques ◽  
Scenario Generation ◽  
Test Problems ◽  
Optimization Approach ◽  
Uncertain Parameters ◽  
Worst Case ◽  
Robust Solution

Feasibility robust optimization techniques solve optimization problems with uncertain parameters that appear only in their constraint functions. Solving such problems requires finding an optimal solution that is feasible for all realizations of the uncertain parameters. This paper presents a new feasibility robust optimization approach involving uncertain parameters defined on continuous domains without any known probability distributions. The proposed approach integrates a new sampling-based scenario generation scheme with a new scenario reduction approach in order to solve feasibility robust optimization problems. An analysis of the computational cost of the proposed approach was performed to provide worst case bounds on its computational cost. The new proposed approach was applied to three test problems and compared against other scenario-based robust optimization approaches. A test was conducted on one of the test problems to demonstrate that the computational cost of the proposed approach does not significantly increase as additional uncertain parameters are introduced. The results show that the proposed approach converges to a robust solution faster than conventional robust optimization approaches that discretize the uncertain parameters.

scholarly journals Distributed and collaborative Web Change Detection system

2015 ◽  
Vol 12 (1) ◽  
pp. 91-114 ◽  
Author(s):  
Víctor Prieto ◽  
Manuel Álvarez ◽  
Víctor Carneiro ◽  
Fidel Cacheda
Keyword(s):  
Change Detection ◽  
Search Engines ◽  
Web Site ◽  
Detection System ◽  
Computational Cost ◽  
Web Pages ◽  
Web Page ◽  
Case Scenario ◽  
Worst Case ◽  
The Web

Search engines use crawlers to traverse the Web in order to download web pages and build their indexes. Maintaining these indexes up-to-date is an essential task to ensure the quality of search results. However, changes in web pages are unpredictable. Identifying the moment when a web page changes as soon as possible and with minimal computational cost is a major challenge. In this article we present the Web Change Detection system that, in a best case scenario, is capable to detect, almost in real time, when a web page changes. In a worst case scenario, it will require, on average, 12 minutes to detect a change on a low PageRank web site and about one minute on a web site with high PageRank. Meanwhile, current search engines require more than a day, on average, to detect a modification in a web page (in both cases).

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