Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.706067
Title: Efficient robust adaptive beamforming algorithms for sensor arrays
Author: Ruan, Hang
ISNI:       0000 0004 6062 6574
Awarding Body: University of York
Current Institution: University of York
Date of Award: 2016
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Abstract:
Sensor array processing techniques have been an important research area in recent years. By using a sensor array of a certain configuration, we can improve the parameter estimation accuracy from the observation data in the presence of interference and noise. In this thesis, we focus on sensor array processing techniques that use antenna arrays for beamforming, which is the key task in wireless communications, radar and sonar systems. Firstly, we propose a low-complexity robust adaptive beamforming (RAB) technique which estimates the steering vector using a Low-Complexity Shrinkage-Based Mismatch Estimation (LOCSME) algorithm. The proposed LOCSME algorithm estimates the covariance matrix of the input data and the interference-plus-noise covariance (INC) matrix by using the Oracle Approximating Shrinkage (OAS) method. Secondly, we present cost-effective low-rank techniques for designing robust adaptive beamforming (RAB) algorithms. The proposed algorithms are based on the exploitation of the cross-correlation between the array observation data and the output of the beamformer. Thirdly, we propose distributed beamforming techniques that are based on wireless relay systems. Algorithms that combine relay selections and SINR maximization or Minimum Mean-Square- Error (MMSE) consensus are developed, assuming the relay systems are under total relay transmit power constraint. Lastly, we look into the research area of robust distributed beamforming (RDB) and develop a novel RDB approach based on the exploitation of the cross-correlation between the received data at the relays and the destination and a subspace projection method to estimate the channel errors, namely, the cross-correlation and subspace projection (CCSP) RDB technique, which efficiently maximizes the output SINR and minimizes the channel errors. Simulation results show that the proposed techniques outperform existing techniques in various performance metrics.
Supervisor: de Lamare, Rodrigo C. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.706067  DOI: Not available
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