A Distributed Tracking Algorithm for Reconstruction of Graph Signals

The rapid development of signal processing on graphs provides a new perspective for processing large-scale data associated with irregular domains. In many practical applications, it is necessary to handle massive data sets through complex networks, in which most nodes have limited computing power. D...

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Bibliographic Details
Published in:IEEE journal of selected topics in signal processing Vol. 9; no. 4; pp. 728 - 740
Main Authors: Wang, Xiaohan, Wang, Mengdi, Gu, Yuantao
Format: Journal Article
Language:English
Published: IEEE 01.06.2015
Subjects:
Delays
Distributed algorithm
Estimation error
graph signal
Laplace equations
sampling and reconstruction
Signal processing algorithms
signal processing on graph
Signal reconstruction
time-varying signal
ISSN:1932-4553, 1941-0484
Online Access:Get full text
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Summary:The rapid development of signal processing on graphs provides a new perspective for processing large-scale data associated with irregular domains. In many practical applications, it is necessary to handle massive data sets through complex networks, in which most nodes have limited computing power. Designing efficient distributed algorithms is critical for this task. This paper focuses on the distributed reconstruction of a time-varying bandlimited graph signal based on observations sampled at a subset of selected nodes. A distributed least square reconstruction (DLSR) algorithm is proposed to recover the unknown signal iteratively, by allowing neighboring nodes to communicate with one another and make fast updates. DLSR uses a decay scheme to annihilate the out-of-band energy occurring in the reconstruction process, which is inevitably caused by the transmission delay in distributed systems. Proof of convergence and error bounds for DLSR are provided in this paper, suggesting that the algorithm is able to track time-varying graph signals and perfectly reconstruct time-invariant signals. The DLSR algorithm is numerically experimented with synthetic data and real-world sensor network data, which verifies its ability in tracking slowly time-varying graph signals.
ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2015.2403799