Li Xinyu, Shi Qing, Xiao Shuangyi, Duan Shukai, Chen Feng
College of Artificial Intelligence, 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). 2019 May 21;19(10):2339. doi: 10.3390/s19102339.
Distributed estimation over sensor networks has attracted much attention due to its various applications. The mean-square error (MSE) criterion is one of the most popular cost functions used in distributed estimation, which achieves its optimality only under Gaussian noise. However, impulsive noise also widely exists in real-world sensor networks. Thus, the distributed estimation algorithm based on the minimum kernel risk-sensitive loss (MKRSL) criterion is proposed in this paper to deal with non-Gaussian noise, particularly for impulsive noise. Furthermore, multiple tasks estimation problems in sensor networks are considered. Differing from a conventional single-task, the unknown parameters (tasks) can be different for different nodes in the multitask problem. Another important issue we focus on is the impact of the task similarity among nodes on multitask estimation performance. Besides, the performance of mean and mean square are analyzed theoretically. Simulation results verify a superior performance of the proposed algorithm compared with other related algorithms.
由于其各种应用，传感器网络上的分布式估计受到了广泛关注。均方误差（MSE）准则是分布式估计中最常用的代价函数之一，它仅在高斯噪声下实现最优性。然而，脉冲噪声在现实世界的传感器网络中也广泛存在。因此，本文提出了基于最小核风险敏感损失（MKRSL）准则的分布式估计算法来处理非高斯噪声，特别是脉冲噪声。此外，还考虑了传感器网络中的多任务估计问题。与传统的单任务不同，多任务问题中不同节点的未知参数（任务）可能不同。我们关注的另一个重要问题是节点间任务相似性对多任务估计性能的影响。此外，从理论上分析了均值和均方的性能。仿真结果验证了所提算法与其他相关算法相比具有优越的性能。
