Distributed Estimation With Adaptive Cluster Learning Over Asynchronous Data Fusion

• Published in: IEEE transactions on aerospace and electronic systems Vol. 59; no. 5; pp. 1 - 12
• Main Authors: Hua, Yi, Gan, Hongping, Wan, Fangyi, Qing, Xinlin, Liu, Feng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; Algorithms; Asynchronous data fusion; cluster learning; Clusters; Data integration; distributed network; Machine learning; multi-task estimation; Multisensor fusion; Parameter estimation; Performance degradation; Sampling; sampling rate; Stability analysis; Time lag; Wireless sensor networks
• ISSN: 0018-9251; 1557-9603
• DOI: 10.1109/TAES.2023.3253085

In the electronic information era, wireless sensor network (WSN) has always been an essential foundation for information collection, processing and communication. In WSN with multi-task estimation, distributed cooperation estimation with cluster learning has always been an attractive topic. When the unknown estimation parameters become complex, some cluster learning algorithms may not work, and their estimation performance could degrade. In addition, the problems of time delay, caused by synchronous data fusion, and different sampling rates between different types of sensors are usually neglected in practical applications. To solve these problems, an unsupervised distributed multi-task estimation algorithm with adaptive cluster learning over asynchronous data is proposed to obtain a more accurate estimation. In the proposed algorithm, the time delay and different sampling rates are fully considered and investigated. The mean stability, mean-square convergence, and behavior of adaptive cluster learning are analyzed for the proposed algorithm with asynchronous data. Finally, simulations are provided to demonstrate the robustness and effectiveness of the proposed algorithm.