Robust Adaptive Beamforming with Sensor Position Errors Using Weighted Subspace Fitting-Based Covariance Matrix Reconstruction

Chen, Peng; Yang, Yixin; Wang, Yong; Ma, Yuanliang

doi:10.3390/s18051476

Cited by 21 publications

(22 citation statements)

References 20 publications

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“…Adaptive beamforming, which can enhance the signal of interest (SOI) while suppressing interferences automatically, is a data-dependent method and is widely applied in the fields of array signal processing, including radar, sonar, and wireless communication, etc. [1][2][3][4]. The standard Capon beamforming is one of the well-known types, which can achieve the optimum output signal-to-interference-plus-noise ratio (SINR) under ideal conditions without any model mismatches.…”

Section: Introductionmentioning

confidence: 99%

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

Yao

Xin

et al. 2020

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

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

Yao

Xin

et al. 2020

Sensors

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“…3 The beamforming with the use of hardware circuits such as analog phase shifters, variable attenuators, and complex phase switching circuitry etc., is referred to as analog beamforming. 6,7 Analog beamforming has several disadvantages, such as if the array pattern is to be altered or if the operating frequency is changed, the hardware needs to be replaced with a suitable one. With the advent of digital signal processors (DSP), 8 and high-speed analog to digital converters (ADC), 6 the digital beamforming technique 6,7 had been developed to overcome these said difficulties.…”

Section: Introductionmentioning

confidence: 99%

“…6,7 Analog beamforming has several disadvantages, such as if the array pattern is to be altered or if the operating frequency is changed, the hardware needs to be replaced with a suitable one. With the advent of digital signal processors (DSP), 8 and high-speed analog to digital converters (ADC), 6 the digital beamforming technique 6,7 had been developed to overcome these said difficulties. We have dealt with the concept of analog beamforming, wherein the phase shift associated with each antenna element is represented in the form of weight, determined in real-time with the adaptive algorithms in the reconfigurable hardware written in MATLAB program.…”

Section: Introductionmentioning

confidence: 99%

“…Beam shaping can be visualized as an adaptive optimization of received power pattern. Well-known adaptive algorithms least mean square (LMS), recursive least squares (RLS), 6,7 sample matrix inversion (SMI) used in wireless communication for signal optimization are widely used for beam shaping. [13][14][15][16][17][18][19][20][21] Our aim in this article is to design a smart antenna test-bed for antenna beam shaping in small scale fading channel with the help of wireless open-access research platform (WARP) boards, a moderately expensive, portable FPGA board, writing generic programming codes over a user-friendly MATLAB interface using the basic adaptive algorithms at first and to make comparative performance analysis.…”

Section: Introductionmentioning

confidence: 99%

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Reconfigurable method of smart antenna beam shaping in indoor fading environment using adaptive algorithms: An exhaustive experimental study

Imtiaj

Bhattacharya

Misra

2020

Int J RF Microw Comput Aided Eng

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Adaptive algorithms for beam shaping of a phased array antenna and multiple-input multiple-output (MIMO) system gaining importance in today's advanced wireless networks to mitigate interference effects and distortion in the receiving signal due to multipath, small scale, and large scale fading effects. This article deals with the development of reconfigurable field programmable gate array (FPGA)-based hardware for smart antenna system to explore parameter dependencies, drawbacks, and relative performance comparison of popular adaptive beamforming and interference suppression algorithms. These are least mean square, recursive least squares (RLS), and sample matrix inversion (SMI) used in real-time under laboratory environment where the existing wireless channel between transmitters and receivers is linear timevarying in nature due to presence of secondary sources giving rise to smallscale fading. For this at first, we propose a novel received signal strength indicator-based procedure to measure the radiation pattern of the antenna under an echoic indoor environment on a reconfigurable and portable FPGA system named wireless open-access research platform (WARP), controllable by generic programming codes over a user-friendly MATLAB interface. For better performance, the SMI algorithm was modified to increase block size rather than block shifting in general SMI. Later a comparative study was performed under varying conditions to observe the best utilization of three adaptive algorithms in beam shaping. In all cases, SMI performs the best with less beam shaping error and faster convergence, validating its use in a real-time fading environment.

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“…To overcome this shortcoming, Refs. [25,26,27,28,29,30,31,32,33,34,35,36,37,38] developed new robust CB (RCB or RDCB) techniques against such estimation errors. These techniques could be roughly divided into two categories: worst-case and stochastic.…”

Section: Introductionmentioning

confidence: 99%

Robust Distributed Collaborative Beamforming for Wireless Sensor Networks with Channel Estimation Impairments

Smida

Zaidi

Affes

et al. 2019

Sensors

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We propose a new collaborative beamforming (CB) solution robust (i.e., RCB) against major channel estimation impairments over dual-hop transmissions through a wireless sensor network (WSN) of K nodes. The source first sends its signal to the WSN. Then, each node forwards its received signal after multiplying it by a properly selected beamforming weight. The latter aims to minimize the received noise power while maintaining the desired power equal to unity. These weights depend on some channel state information (CSI) parameters. Hence, they have to be estimated locally at each node, thereby, resulting in channel estimation errors that could severely hinder CB performance. Exploiting an efficient asymptotic approximation at large K, we develop alternative RCB solutions that adapt to different implementation scenarios and wireless propagation environments ranging from monochromatic (i.e., scattering-free) to polychromatic (i.e., scattered) ones. Besides, in contrast to existing techniques, our new RCB solutions are distributed (i.e., DCB) in that they do not require any information exchange among nodes, thereby dramatically improving both WSN spectral and power efficiencies. Simulation results confirm that the proposed robust DCB (RDCB) techniques are much more robust in terms of achieved signal-to-noise ratio (SNR) against channel estimation errors than best representative CB benchmarks.

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Robust Adaptive Beamforming with Sensor Position Errors Using Weighted Subspace Fitting-Based Covariance Matrix Reconstruction

Cited by 21 publications

References 20 publications

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

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

Reconfigurable method of smart antenna beam shaping in indoor fading environment using adaptive algorithms: An exhaustive experimental study

Robust Distributed Collaborative Beamforming for Wireless Sensor Networks with Channel Estimation Impairments

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