Networked Kalman filtering with combined constraints of bandwidth and random delay
Networked Kalman filtering with combined constraints of bandwidth and random delay
复制标题
具有带宽和随机延迟组合约束的网络卡尔曼滤波
DOI：
10.1007/s11432-012-4624-5
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发表时间：
2012-08
期刊：
影响因子：
--
通讯作者：
2 State Key Laboratory of Industrial Control Techn
中科院分区：
文献类型：
--
作者：
GE QuanBo 1,2,WEN ChengLin 1 & XU TingLiang 1 1 Sc;2 State Key Laboratory of Industrial Control Techn
卡尔曼滤波是一种有效的无线传感器网络目标跟踪和自定位方法。研究了带宽和随机时延联合约束下网络系统的滤波器设计问题，针对给定的线性定常（LTI）或定参数（DP）系统，即线性定参数（LDP）系统，提出了一种通用的网络Kalman估计器。首先，给出了比特量化误差向量的两种建模方法，并建立了比特量化后的LDP系统。其次，我们介绍了一个等效的加权求和形式的常规线性卡尔曼滤波器，它充分利用了两个基本特性，即离线计算的性质从LDP系统和线性加权求和性质从线性最小均方误差估计。最后，基于加权求和滤波器，采用替换和新息补偿操作，提出了一种集成的网络化估计器，该估计器包含两种对比特量化误差实际建模方式不同的估计算法。本文提出的通用网络估计算法具有适用范围广、算法结构简洁、估计精度高、运行性能好等突出优点。通过几个算例验证了所提出的网络估计算法的有效性。
Kalman filter is a powerful tool in target tracking and self-localization across wireless sensor networks with many constraints. This paper considers the filters design for networked systems with combined constraints of bandwidth and random delay, and proposes a kind of universal networked Kalman estimator for given linear time invariant (LTI) or determinate parameters (DP) systems, which are also called linear and determinate parameters (LDP) systems jointly. Firstly, two modeling ways to treat bit quantization error vector are given and the corresponding LDP systems taken after bit quantization are established. Secondly, we introduce an equivalent weighted summation form of the conventional linear Kalman filter, which fully uses two essential characteristics, namely offline computation property from the LDP systems and linear weighted summation property from the linear minimum mean square error estimate. Finally, by adopting the replacement and innovation compensation operations, a kind of integrated networked estimator, involving two estimation algorithms with different modeling ways to bit quantization error actually, is presented based on the weighted summation filter for random delay systems. The proposed universal networked estimation algorithms have some outstanding advantages such as extensive application, concise algorithm structure, high estimate accuracy, and good running performance. Several examples are demonstrated to validate the proposed networked estimation algorithms.
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