.png){kind=icon}
返回
Distributed Recursive Filtering Over Sensor Networks With Nonlogarithmic Sensor Resolution
.png){kind=icon}
OA
AI
DOI：10.1109/TAC.2021.3115473.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Sensor resolution, which is one of the most important parameters/specifications for almost all kinds of sensors, plays an important role in any signal processing problems. This article deals with the distributed filtering problem for a class of discrete time-varying stochastic systems subject to nonlogarithmic sensor resolution and stochastic nonlinearities. The soft measurement technique is exploited in the filter design to overcome the difficulties resulting from the sensor-resolution-induced (SRI) uncertainty. The aim of the presented filtering problem is to construct the distributed filter over a sensor network such that in the presence of SRI uncertainty and stochastic nonlinearity, an upper bound on the filtering error covariance is guaranteed and subsequently minimized by appropriately designing the filer parameters at each time instant. Moreover, a matrix simplification method is utilized to tackle the difficulties stemming from the sparsity of sensor networks. Finally, a numerical example is employed to illustrate the effectiveness of the proposed filtering scheme.
Keyword:
Wireless sensor networks
Target tracking
Stochastic processes
Measurement uncertainty
Data models
Signal resolution
Kalman filters
Distributed filtering
recursive filtering
sensor resolution (SR)
stochastic nonlinearity
wireless sensor networks (WSNs)
期刊
IF:
7
论文数:
1.3W
被引数:
6.7W
机构
引用论文
Robust H∞ finite-horizon filtering with randomly occurred nonlinearities and quantization effects具有随机发生的非线性和量化效应的鲁棒h ∞ 有限水平滤波.png)
AUTOMATICA
OAAI
AUTOMATICAIF5.9
OAAI分享收藏分享收藏分享收藏Extended Kalman filtering with stochastic nonlinearities and multiple missing measurements具有随机非线性和多个缺失测量的扩展卡尔曼滤波AUTOMATICAOAAI
AUTOMATICAIF5.9
OAAI分享收藏Distributed fusion filters from uncertain measured outputs in sensor networks with random packet lossesINFORMATION FUSION
INFORMATION FUSIONIF15.5
PREAI
分享收藏
查看更多内容