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Blind Channel and Source Estimation in Networked Systems

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2014年第17期62卷 4611-4626页

作者： Yu, Chengpu Xie, Lihua Soh, Yeng Chai Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

In this paper, we study the blind channel and source estimation in sensor networks, where the channels are modeled by FIR filters and the source signal is deterministic. Distributed estimation algorithms for networked systems under noise-free and noisy measurements are developed, which blindly identify the multiple channels, followed by the source signal estimation. The key to the proposed algorithms lies in the adaptation of the blind system identification technique for the distributed channel estimation. In the presence of measurement noises, conventional blind identification methods cannot be straightforwardly realized in distributed environments. Instead, two stable distributed algorithms are introduced, which can avoid trivial solutions for the blind identification problem. Convergence properties of the proposed algorithms are provided, and simulation examples are given to show the performances of the proposed algorithms.

关键词： Blind system identification consensus based gradient method multi-channel deconvolution

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On Recursive Blind Equalization in Sensor Networks

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2015年第3期63卷 662-672页

作者： Yu, Chengpu Xie, Lihua Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

In this paper, we study the distributed blind equalization of networked single-input multi-output (SIMO) systems. An indirect distributed equalization framework is presented, which estimates the transfer functions followed by the associated equalizers. Two distributed indirect equalization algorithms are proposed: one depends on multiple average consensus operations, and the other relies on the combination of innovation and one average consensus operation. The former generates an approximate equalizer for which the associated estimation error is determined by the number of average consensus operations, while the latter can provide an accurate equalizer estimation under some mild conditions. The proposed algorithms estimate the desired equalizer recursively and recover the source signal in real time. Furthermore, the distributed equalization under a time-varying topology is investigated as well. Convergence properties of the proposed algorithms are established and numerical simulations are carried out to show the performances of the proposed algorithms.

关键词： Blind channel equalization consensus based gradient method mutually referenced equalizers

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DISTRIBUTED BLIND SYSTEM IDENTIFICATION IN SENSOR NETWORKS

DISTRIBUTED BLIND SYSTEM IDENTIFICATION IN SENSOR NETWORKS

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Yu, Chengpu Xie, Lihua Soh, Yeng Chai Nanyang Technol Univ Sch Elect & Elect Engn EXQUISITUS Ctr E City Singapore 639798 Singapore

ISBN: (纸本)9781479928934

This paper studies the blind identification of multi-channel FIR systems in the context of sensor networks. Distributed identification algorithms are developed for both noise-free and noise-contaminated networked systems. The proposed algorithms distribute the data storage and computational load among multiple agents connected by a specified topology, and are fulfilled via information exchanges among neighboring agents without the need of fusion centers. In the presence of measurement noises, a stabilized distributed algorithm is provided which can avoid trivial estimations of the multiple channels. In addition, convergence properties of the proposed algorithms are provided, and simulation examples are given to show the performances of the proposed algorithms.

关键词： Blind identification multi-agent system consensus based gradient method

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Distributed recursive blind equalization of networked systems 11

Distributed recursive blind equalization of networked system...

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11th World Congress on Intelligent Control and Automation

作者： Yu, Chengpu Xie, Lihua Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

ISBN: (纸本)9781479958252

In this paper, we study the distributed blind equalization of networked single-input multi-output (SIMO) systems for which the transfer functions from a common source to multiple outputs are described by FIR filters. The blind distributed equalization is carried out by firstly estimating the multiple FIR filters followed by the associated equalizers. Two different schemes are employed for the distributed realization of blind equalization: one depends on multiple average-consensus operations, and the other relies on the combination of innovation and one average-consensus operation. The former generates an approximate equalizer for which the associated estimation error is determined by the number of average-consensus operations, while the latter can provide an accurate equalizer estimation under some mild conditions. Finally, numerical simulations are carried out to show the performances of the proposed algorithms.

关键词： Blind channel equalization mutually referenced equalizers consensus based gradient method

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