Abstract
This paper considers the parameter estimation problem under non-stationary environments in sensor networks. The unknown parameter vector is considered to be a time-varying sequence. To further promote estimation performance, this paper suggests a novel diffusion logarithm-correntropy algorithm for each node in the network. Such an algorithm can adopt both the logarithm operation and correntropy criterion to the estimation error. Moreover, if the error gets larger due to the non-stationary environments, the algorithm can respond immediately by taking relatively steeper steps. Thus, the proposed algorithm achieves smaller error in time. The tracking performance of the proposed logarithm-correntropy algorithm is analyzed. Finally, experiments verify the validity of the proposed algorithmic schemes, which are compared to other recent algorithms that have been proposed for parameter estimation.
Highlights
Sensor networks are useful tools for disaster relief management, target localization and tracking, and environment monitoring [1,2,3,4]
The performance of the proposed algorithm was verified in a non-stationary environment over sensor networks and the communication links were ideal
To solve the problem of parameter estimation in non-stationary environments over sensor networks, each node in the sensor networks was equipped with the logarithm-correntropy cost function
Summary
Sensor networks are useful tools for disaster relief management, target localization and tracking, and environment monitoring [1,2,3,4]. Distributed strategies are of great significance to solve the problem of parameter estimation in sensor networks, due to their robustness against imperfections, low complexity, and low power demands. Among these distributed schemes, in the incremental strategy [8], a cyclic path is defined over the nodes and data are processed in a cyclic manner through the network until optimization is achieved. To make the error cost function more suitable for non-stationary environments, we propose a diffusion signal processing framework with a logarithm-correntropy cost function to solve the parameter estimation problem, which can elegantly and gradually adjust the cost function in its optimization based on the error amount
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