Open AccessDistributed UFIR Filtering with Applications to Environmental Monitoring
Author(s) -
Miguel Vazquez-Olguin,
Yuriy S. Shmaliy,
Oscar Ibarra Manzano,
Sandra Márquez-Figueroa
Publication year - 2021
Publication title -
wseas transactions on signal processing
Language(s) - English
Resource type - Journals
eISSN - 2224-3488
pISSN - 1790-5052
DOI - 10.37394/232014.2020.16.20
Subject(s) - robustness (evolution) , computer science , estimator , missing data , wireless sensor network , real time computing , robust statistics , data mining , filter (signal processing) , computer network , statistics , outlier , artificial intelligence , machine learning , mathematics , biochemistry , chemistry , computer vision , gene
Environmental monitoring requires an analysis of large and reliable amount of data collected through node stations distributed over a very wide area. Equipments used in such stations are often expensive that limits the amount of sensing stations to be deployed. The technology known as Wireless Sensor Networks (WNS) is a viable option to deliver low-cost sensor information. However, electromagnetic interference, damaged sensors, and the landscape itself often cause the network to suffer from faulty links as well as missing and corrupted data. Therefore robust estimators are required to mitigate such effects. In this sense, the unbiased finite impulse response (UFIR) filter is used as a robust estimator for applications over WSN, especially when the process statistics are unknown. In this paper, we investigate the robustness of the distributed UFIR (dUFIR) filter with optimal consensus on estimates against missing and incorrect data. The dUFIR algorithm is tested in two different scenarios of very unstable WSN using real data. It is shown that the dUFIR filter is more suitable for real life applications requiring the robustness against missing and corrupted measurements under the unknown noise statistics.