随机变化信道参数放大-前向继电器下的Cubature Kalman融合滤波

IF 15.3 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Jiaxing Li;Zidong Wang;Jun Hu;Hongli Dong;Hongjian Liu
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引用次数: 0

摘要

研究了放大前向(AaF)继电器下多传感器系统的延时卡尔曼融合滤波(CKFF)问题。为了方便数据传输,AaF中继被用来调节传感器和滤波器之间的信号通信。这里,随机变化的信道参数由一组随机变量表示,这些随机变量的发生概率允许表现出有界的不确定性。利用球-径向培养原理,初步构造了AaF继电器下的局部滤波器。这种结构通过设计适当的滤波器增益来保证并最小化滤波误差协方差的上界。然后,应用协方差交集融合规则对局部滤波器进行融合。进一步,通过建立一定的充分条件,研究了滤波误差协方差上界的一致有界性。通过集中在三相感应电机上的仿真实验,最终验证了所提出的CKFF方案的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cubature Kalman Fusion Filtering Under Amplify- and-Forward Relays With Randomly Varying Channel Parameters
In this paper, the problem of cubature Kalman fusion filtering (CKFF) is addressed for multi-sensor systems under amplify-and-forward (AaF) relays. For the purpose of facilitating data transmission, AaF relays are utilized to regulate signal communication between sensors and filters. Here, the randomly varying channel parameters are represented by a set of stochastic variables whose occurring probabilities are permitted to exhibit bounded uncertainty. Employing the spherical-radial cubature principle, a local filter under AaF relays is initially constructed. This construction ensures and minimizes an upper bound of the filtering error covariance by designing an appropriate filter gain. Subsequently, the local filters are fused through the application of the covariance intersection fusion rule. Furthermore, the uniform boundedness of the filtering error covariance's upper bound is investigated through establishing certain sufficient conditions. The effectiveness of the proposed CKFF scheme is ultimately validated via a simulation experiment concentrating on a three-phase induction machine.
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来源期刊
Ieee-Caa Journal of Automatica Sinica
Ieee-Caa Journal of Automatica Sinica Engineering-Control and Systems Engineering
CiteScore
23.50
自引率
11.00%
发文量
880
期刊介绍: The IEEE/CAA Journal of Automatica Sinica is a reputable journal that publishes high-quality papers in English on original theoretical/experimental research and development in the field of automation. The journal covers a wide range of topics including automatic control, artificial intelligence and intelligent control, systems theory and engineering, pattern recognition and intelligent systems, automation engineering and applications, information processing and information systems, network-based automation, robotics, sensing and measurement, and navigation, guidance, and control. Additionally, the journal is abstracted/indexed in several prominent databases including SCIE (Science Citation Index Expanded), EI (Engineering Index), Inspec, Scopus, SCImago, DBLP, CNKI (China National Knowledge Infrastructure), CSCD (Chinese Science Citation Database), and IEEE Xplore.
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