Abstract
The set-membership information fusion problem is studied for general multi-sensor dynamic systems. Based on set-membership theory, three centralized state fusion estimation algorithms in the presence of bounded disturbances are proposed, namely augmented algorithm, combined measurement filtering algorithm and pseudo-sequential filtering algorithm. Theoretical discussions on the convergence and boundedness of the proposed fusion algorithms are provided and their stability is proved. The estimate accuracy, computational complexity and flexibility of these three fusion algorithms are compared through theoretical analysis and simulation. And their exchanging property of measurement update order is discussed. Results show that these algorithms are functionally equivalent in terms of the estimation accuracy and the exchangeability of the measurement update order can be guaranteed as long as the parameters satisfy certain conditions. Meanwhile the simulation results prove the role of the proposed algorithms in improving state estimation accuracy. In addition, the combined measurement filtering algorithm has the highest calculation speed due to lower dimension. But it is less flexible because the sensor measurement matrices need to satisfy some additional conditions. These conclusions are valuable in applications.
Highlights
In recent years, the functional requirements of the large complicated systems are rapidly improving, especially the high performance estimation requirements for the system state
Based on the set membership (SM) theory, three ellipsoidal outer-bounding state fusion estimation algorithms with centralized structure have been proposed, i.e., the augmented algorithm, the pseudo-sequential filtering algorithm, and the combined measurement filtering algorithm. This paper presents both theoretical and simulation results on the comparison of these three algorithms and the exchangeability of the measurement update order
The three fusion algorithms are functionally equivalent if the parameters meet certain conditions, as in Theorem 5 and 6
Summary
The functional requirements of the large complicated systems are rapidly improving, especially the high performance estimation requirements for the system state. Q. Shen et al.: Centralized Fusion Methods for Multi-Sensor System With Bounded Disturbances practical situations, and this may lead to poor performance for the state estimation [16], [17]. It is interesting to find out that whether the conclusions and properties 1)∼3) can be maintained in bounded setting These facts motivate us to further research the SM centralized fusion problems for VOLUME 7, 2019 multi-sensor dynamic systems with bounded disturbances. The following objects are focused on: 1) To design three centralized fusion algorithms for multisensor system with bounded disturbances based on ellipsoidal bounding estimation; 2) To analyze the properties of the proposed algorithms, including the stability, convergence, computation complexity, the exchangeability of measurement update order and the equivalence between different algorithms.
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