College of Electronic and Information Engineering, School of Mathematics and Statistics, Southwest University, Chongqing 400715, China.
Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, and College of Electronic and Information Engineering, Southwest University, and Chongqing Collaborative Innovation Center for Brain Science, Chongqing 400715, China.
Sensors (Basel). 2018 Oct 10;18(10):3381. doi: 10.3390/s18103381.
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.
本文研究了传感器网络中非平稳环境下的参数估计问题。未知参数向量被视为时变序列。为了进一步提高估计性能，本文针对网络中的每个节点提出了一种新的扩散对数-协方差算法。该算法可以对数运算和协方差准则应用于估计误差。此外，如果由于非平稳环境导致误差变大，该算法可以通过采取相对陡峭的步骤立即做出响应。因此，所提出的算法在时间上实现了更小的误差。分析了所提出的对数-协方差算法的跟踪性能。最后，实验验证了所提出的算法方案的有效性，并与其他最近提出的用于参数估计的算法进行了比较。
