Distributed Spatial Filtering Over Networked Systems

This letter concerns distributed spatial filtering over networked systems, i.e., transforming signal values given for nodes to those with a desired spatial frequency characteristic via a distributed computation. An existing filtering algorithm can achieve only low-pass filter characteristics, which limits its range of applications. To address this limitation, we extend the aforementioned filtering algorithm using an additional design parameter. We then present a characterization of all the realizable filter characteristics as a necessary and sufficient condition for achieving distributed spatial filtering. As a result, it is shown that the extended algorithm increases the range of the realizable filter characteristics. The proposed method is verified not only by simulation but also by denoising experiments for a real sensor network. The results show that the proposed method effectively reduces spatial noise and achieves higher performance than an average consensus algorithm and an average filter.

[1]  Jing-Wen Yi,et al.  Average Consensus by Graph Filtering: New Approach, Explicit Convergence Rate, and Optimal Design , 2020, IEEE Transactions on Automatic Control.

[2]  Yuan Wang,et al.  Resilient Consensus Through Event-Based Communication , 2018, IEEE Transactions on Control of Network Systems.

[3]  Paul Wintz,et al.  Digital image processing (2nd ed.) , 1987 .

[4]  Toshiharu Sugie,et al.  Analysis and Design of Multi-Agent Systems in Spatial Frequency Domain: Application to Distributed Spatial Filtering in Sensor Networks , 2020, IEEE Access.

[5]  H.M. Wechsler,et al.  Digital image processing, 2nd ed. , 1981, Proceedings of the IEEE.

[6]  Richard M. Murray,et al.  Information flow and cooperative control of vehicle formations , 2004, IEEE Transactions on Automatic Control.

[7]  Reza Olfati-Saber,et al.  Consensus and Cooperation in Networked Multi-Agent Systems , 2007, Proceedings of the IEEE.

[8]  Pascal Frossard,et al.  The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains , 2012, IEEE Signal Processing Magazine.

[9]  Santiago Segarra,et al.  Optimal Graph-Filter Design and Applications to Distributed Linear Network Operators , 2017, IEEE Transactions on Signal Processing.

[10]  Yan Wan,et al.  Robust Formation Control for Multiple Quadrotors With Nonlinearities and Disturbances , 2020, IEEE Transactions on Cybernetics.

[11]  Pascal Frossard,et al.  Chebyshev polynomial approximation for distributed signal processing , 2011, 2011 International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS).

[12]  José M. F. Moura,et al.  Discrete Signal Processing on Graphs: Frequency Analysis , 2013, IEEE Transactions on Signal Processing.