Distributed Estimation With Adaptive Cluster Learning Over Asynchronous Data Fusion

In the electronic information era, the wireless sensor network (WSN) has always been an essential foundation for information collection, processing, and communication. In WSN with multitask 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 multitask 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.