标题： 分布式噪声协方差矩阵估计在传感器网络中 显示英文标题

标题： Distributed Noise Covariance Matrices Estimation in Sensor Networks

摘要： 基于网络内处理的自适应算法对于预测控制和机器学习领域的历史数据（例如，噪声协方差）在线参数估计是有用的。 本文专注于多传感器线性时不变（LTI）系统的分布式噪声协方差矩阵估计问题。 传统的噪声协方差估计方法，例如自协方差最小二乘（ALS）方法，由于缺乏传感器的历史测量值而存在不足，从而导致ALS估计的方差较高。 为了解决这个问题，我们通过扩大来自邻居的创新值，基于批处理协方差交叉（BCI）方法提出了分布式自协方差最小二乘（D-ALS）算法。 给出了D-ALS算法的准确性分析，以展示D-ALS估计方差的减少。 展示了静态和移动传感器网络中协作目标跟踪任务的数值结果，以显示所提出的D-ALS算法的可行性和优越性。 显示英文摘要

摘要： 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.