scholarly journals Robust Adaptive Beamforming with Optimal Covariance Matrix Estimation in the Presence of Gain-Phase Errors

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2930
Author(s):  
Di Yao ◽  
Xin Zhang ◽  
Bin Hu ◽  
Qiang Yang ◽  
Xiaochuan Wu
Keyword(s):  
Taylor Series Expansion ◽  
Adaptive Beamforming ◽  
Descent Method ◽  
Reconstruction Method ◽  
Noise Power ◽  
Quadratic Optimization Problem ◽  
Matrix Estimation ◽  
Closed Form Solutions ◽  
Adaptive Beamformer ◽  
Phase Errors

An adaptive beamformer is sensitive to model mismatch, especially when the desired signal exists in the training samples. Focusing on the problem, this paper proposed a novel adaptive beamformer based on the interference-plus-noise covariance (INC) matrix reconstruction method, which is robust with gain-phase errors for uniform or sparse linear array. In this beamformer, the INC matrix is reconstructed by the estimated steering vector (SV) and the corresponding individual powers of the interference signals, as well as noise power. Firstly, a gain-phase errors model of the sensors is deduced based on the first-order Taylor series expansion. Secondly, sensor gain-phase errors, the directions of the interferences, and the desired signal can be accurately estimated by using an alternating descent method. Thirdly, the interferences and noise powers are estimated by solving a quadratic optimization problem. To reduce the computational complexity, we derive the closed-form solutions of the second and third steps with compressive sensing and total least squares methods. Simulation results and measured data demonstrate that the performance of the proposed beamformer is always close to the optimum, and outperforms other tested methods in the case of gain-phase errors.

scholarly journals Compressed Sensing-Based DOA Estimation With Antenna Phase Errors

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 294 ◽  
Author(s):  
Linxi Liu ◽  
Xuan Zhang ◽  
Peng Chen
Keyword(s):  
Gradient Descent ◽  
Array Signal Processing ◽  
Doa Estimation ◽  
Descent Method ◽  
Estimation Problem ◽  
Reconstruction Method ◽  
Gradient Descent Method ◽  
Phase Errors ◽  
Antenna Phase ◽  
Cost Efficient

In array signal processing, the direction of arrivals (DOAs) of the received signals are estimatedby measuring the relative phases among antennas; hence, the estimation performance is reducedby the inconsistency among antennas. In this paper, the DOA estimation problem of the uniformlinear array (ULA) is investigated in the scenario with phase errors among the antennas, and adiagonal matrix composed of phase errors is used to formulate the system model. Then, by using thecompressed sensing (CS) theory, we convert the DOA estimation problem into a sparse reconstructionproblem. A novel reconstruction method is proposed to estimate both the DOA and the unknownphase errors, iteratively. The phase errors are calculated by a gradient descent method with thetheoretical expressions. Simulation results show that the proposed method is cost-efficient andoutperforms state-of-the-art methods regarding the DOA estimation with unknown phase errors.

scholarly journals Computationally Efficient Compressed Sensing-Based Method via FG Nyström in Bistatic MIMO Radar with Array Gain-Phase Error Effect

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jurong Hu ◽  
Evans Baidoo ◽  
Lei Zhan ◽  
Ying Tian
Keyword(s):  
Matching Pursuit ◽  
Phase Error ◽  
Mimo Radar ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Computationally Efficient ◽  
Angle Estimation ◽  
Bistatic Mimo Radar ◽  
Phase Errors ◽  
Sparse Signal Representation

In this paper, a robust angle estimator for uncorrelated targets that employs a compressed sense (CS) scheme following a fast greedy (FG) computation is proposed to achieve improved computational efficiency and performance for the bistatic MIMO radar with unknown gain-phase errors. The algorithm initially avoids the wholly computation of the received signal by compiling a lower approximation through a greedy Nyström approach. Then, the approximated signal is transformed into a sparse signal representation where the sparsity of the target is exploited in the spatial domain. Finally, a CS method, Simultaneous Orthogonal Matching Pursuit with an inherent gradient descent method, is utilized to reconstruct the signal and estimate the angles and the unknown gain-phase errors. The proposed algorithm, aside achieving closed-form resolution for automatically paired angle estimation, offers attractive computational competitiveness, specifically in large array scenarios. Additionally, the analyses of the computational complexity and the Cramér–Rao bounds for angle estimation are derived theoretically. Numerical experiments demonstrate the improvement and effectiveness of the proposed method against existing methods.

scholarly journals Performance Evaluation of Hexagonal Array Adaptive Beamforming Algorithm Based on Inverse QR-RLS for Active Sonar Application

2009 ◽  
Vol 3 (3) ◽  
pp. 343-362 ◽  
Author(s):  
Q. Wang ◽  
X. Pan
Keyword(s):  
Spatial Filter ◽  
Adaptive Beamforming ◽  
Recursive Least Squares ◽  
Limited Area ◽  
Computationally Efficient ◽  
Rls Algorithm ◽  
Active Sonar ◽  
Adaptive Beamformer ◽  
Computation Efficiency ◽  
Computer Processor

In shallow water areas, to enhance underwater targets detection performance improve computation efficiency of active sonar, a computationally efficient adaptive beamformer (spatial filter) based on inverse QR (IQR) and recursive least-squares (RLS) is developed under fast Fourier transform framework, for standard hexagonal receiving array implementation. The IQR-RLS algorithm has good numerical stability and can be mapped onto coordinate rotation digital computer processor-based systolic arrays, which is suitable for real time applications. Using the proposed scheme to construct beamformer, which reduces computational complexity significantly and offers better converge rate than conventional adaptive beamformer. The simulation and lake test results demonstrates the algorithm improves interference (reverberation) suppression ability. It improves SNR about 2dB of still bottom target detection in reverberation limited area.

Implementation of a novel Fibonacci branch search optimizer for the design of the low sidelobe and deep nulling adaptive beamformer

2020 ◽  
Vol 12 (7) ◽  
pp. 660-677
Author(s):  
Haichuan Zhang ◽  
Fangling Zeng
Keyword(s):  
Optimization Technique ◽  
Optimal Solution ◽  
Fibonacci Sequence ◽  
Search Space ◽  
Adaptive Beamforming ◽  
Heuristic Optimization ◽  
Local Optimum ◽  
Adaptive Beamformer ◽  
High Convergence Rate ◽  
Global And Local

AbstractIn this work, we proposed an adaptive beamformer based on a novel heuristic optimization algorithm. The novel optimization technique inspired from Fibonacci sequence principle, designated as Fibonacci branch search (FBS), used new tree's branches fundamental structure and interactive searching rules to obtain the global optimal solution in the search space. The branch structure of FBS is selected using two types of multidimensional points on the basis of shortening fraction formed by Fibonacci sequence; in this mode, interactive global and local searching rules are implemented alternately to obtain the optimal solutions, avoiding stagnating in local optimum. The proposed FBS is also used here to construct an adaptive beamforming (ABF) technique as a real-time implementation to achieve near-optimal performance for its simplicity and high convergence rate, then, the performance of the FBS is compared with the five typical heuristic optimization algorithms. Simulation results demonstrate the superiority of the proposed FBS approach in locating the optimal solution with higher precision and reveal further improvement in the ABF performance.

scholarly journals Evaluation of Minimum Variance Distortionless Response Beamforming Algorithm Based Circular Antenna Arrays

2016 ◽  
Vol 11 (1) ◽  
pp. 1
Author(s):  
Suhail Najm Shahab ◽  
Ayib Rosdi Zainun ◽  
Balasim S. S. ◽  
Nurul Hazlina Noordin ◽  
Izzeldin Ibrahim Mohamed
Keyword(s):  
Antenna Arrays ◽  
Smart Antenna ◽  
Minimum Variance ◽  
Adaptive Beamforming ◽  
Antenna System ◽  
Noise Power ◽  
Noise Mitigation ◽  
Lte Networks ◽  
Continuous Growth ◽  
Minimum Variance Distortionless Response

Wireless data traffic is in a continuous growth, and there are increasing demands for wireless systems that provide deep interference suppression and noise mitigation. In this paper, adaptive beamforming (ABF) technique for Smart Antenna System (SAS) based on Minimum Variance Distortionless Response (MVDR) algorithm connected toCircular Antenna Array (CAA) is discussed and analyzed. The MVDR performance is evaluated by varying various parameters; namely the number of antenna elements, space separation between the elements, the number of interference sources, noise power label, and a number of snapshots. LTE networks allocate a spectrum band of 2.6 GHz is used for evaluating the MVDR performance. The MVDR performance is evaluated with two important metrics; beampattern and SINR. Simulation results demonstrate that as the antenna elements increase, the performance of the MVDR improves dramatically. This means the performance of MVDR greatly relies upon the number of the elements. Half of the wavelength is considered the best interelement spacing, the performance degraded as noise power increased, and more accurately resolution occurred when the number of snapshots increased. The proposed method was found to be performed better than some existing techniques. According to the result, the beampattern relies on the number of element and the separation between array elements. Also, the SINR strongly depends on noise power label and the number of snapshots.

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