论文标题
传感器网络中的分布式噪声协方差矩阵估计
Distributed Noise Covariance Matrices Estimation in Sensor Networks
论文作者
论文摘要
基于网络的网络处理的自适应算法对于预测控制和机器学习领域中的历史数据（例如噪声协方差）的在线参数估计很有用。本文重点介绍了多传感器线性时间传感器（LTI）系统的分布式噪声协方差矩阵估计问题。常规的噪声协方差估计方法，例如自动互动最小二乘（ALS）方法，缺乏传感器的历史测量值，因此产生了ALS估计值的较高差异。为了解决问题，我们通过扩大邻居的创新来提出基于批处协方差交集（BCI）方法的分布式自动共享最小二乘（D-ALS）算法。给出了D-ALS算法的准确性分析，以显示D-ALS估计值方差的下降。证明了静态和移动传感器网络中合作目标跟踪任务的数值结果，以显示拟议的D-ALS算法的可行性和优越性。
Adaptive algorithms based on in-network processing over networks are useful for online parameter estimation of historical data (e.g., noise covariance) in predictive control and machine learning areas. This paper focuses on the distributed noise covariance matrices estimation problem for multi-sensor linear time-invariant (LTI) systems. Conventional noise covariance estimation approaches, e.g., auto-covariance least squares (ALS) method, suffers from the lack of the sensor's historical measurements and thus produces high variance of the ALS estimate. To solve the problem, we propose the distributed auto-covariance least squares (D-ALS) algorithm based on the batch covariance intersection (BCI) method by enlarging the innovations from the neighbors. The accuracy analysis of D-ALS algorithm is given to show the decrease of the variance of the D-ALS estimate. The numerical results of cooperative target tracking tasks in static and mobile sensor networks are demonstrated to show the feasibility and superiority of the proposed D-ALS algorithm.
