Optimal Discrete-Time Distributed Kalman Filter With Reduced Communication

被引:1
作者
Battilotti, Stefano [1 ]
Borri, Alessandro [2 ,3 ]
Cacace, Filippo [4 ]
d'Angelo, Massimiliano [2 ,3 ]
机构
[1] Sapienza Univ Roma, Dipartimento Ingn Informat Automat & Gestionale, I-00185 Rome, Italy
[2] Natl Res Council Italy CNR, Ist Anal Sistemi & Informat Antonio Ruberti, IASI, I-00185 Rome, Italy
[3] Via Giosue Carducci, I-67100 Laquila, Italy
[4] Univ Campus Biomed Roma, I-00128 Rome, Italy
关键词
Kalman filters; Vectors; Filtering algorithms; Covariance matrices; Symmetric matrices; Information filters; Estimation error; Sensors; Noise; Mathematical models; Distributed filtering; network analysis; stochastic systems; WIRELESS SENSOR NETWORKS; STATE ESTIMATION; CONVERGENCE; CONSENSUS; AVERAGE;
D O I
10.1109/TAC.2024.3496577
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This article proposes and analyzes a distributed filter where the consensus term is a virtual output rather than the local state estimate. This feature allows for reducing the data transmitted among nodes at each intermediate step, namely, instead of exchanging a vector of the dimension of the state, nodes exchange a vector of the dimension of the rank of the total output matrix. The main finding is that the convergence to the performance of the centralized Kalman filter and mean-square boundedness of the estimation error are not lost despite an increase in the number of consensus steps. Simulations show that the total communication overhead is reduced without performance degradation with respect to the original distributed filter, where nodes exchange local state estimates.
引用
收藏
页码:2754 / 2761
页数:8
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