An efficient algorithm for optimal linear estimation fusion in distributed multisensor systems

Under the assumption of independent observation noises across sensors, Bar-Shalom and Campo proposed a distributed fusion formula for two-sensor systems, whose main calculation is the inverse of submatrices of the error covariance of two local estimates instead of the inverse of the error covariance...

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Bibliographic Details
Published in:IEEE transactions on systems, man and cybernetics. Part A, Systems and humans man and cybernetics. Part A, Systems and humans, 2006-09, Vol.36 (5), p.1000-1009
Main Authors: Jie Zhou, Jie Zhou, Yunmin Zhu, Yunmin Zhu, Zhisheng You, Zhisheng You, Enbin Song, Enbin Song
Format: Article
Language:English
Subjects:
Algorithms
Computation
Computational complexity
Computer science education
Covariance
Covariance matrix
Distributed computing
Distributed estimation system
Error detection
Estimates
Feedback
Inverse
iterative algorithm
Iterative algorithms
Iterative methods
Moore-Penrose generalized inverse (MP inverse)
Multisensor systems
Noise
optimal linear estimation fusion
orthogonal projector
Sensor fusion
Sensor phenomena and characterization
Sensor systems
Sensors
Studies
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Summary:Under the assumption of independent observation noises across sensors, Bar-Shalom and Campo proposed a distributed fusion formula for two-sensor systems, whose main calculation is the inverse of submatrices of the error covariance of two local estimates instead of the inverse of the error covariance itself. However, the corresponding simple estimation fusion formula is absent in a general distributed multisensor system. In this paper, an efficient iterative algorithm for distributed multisensor estimation fusion without any restrictive assumption on the noise covariance (i.e., the assumption of independent observation noises across sensors and the two-sensor system, and the direct computation of the Moore-Penrose generalized inverse of the joint error covariance of local estimates are not necessary) is presented. At each iteration, only the inverse or generalized inverse of a matrix having the same dimension as the error covariance of a single-sensor estimate is required. In fact, the proposed algorithm is a generalization of Bar-Shalom and Campo's fusion formula and reduces the computational complexity significantly since the number of iterative steps is less than the number of sensors. An example of a three-sensor system shows how to implement the specific iterative steps and reduce the computational complexities
ISSN:1083-4427
2168-2216
1558-2426
2168-2232
DOI:10.1109/TSMCA.2006.878986