Distributed Fusion Estimation for the Measurements with Bounded Disturbances

Qiang Shen; Can Li; Jieyu Liu; Xinsan Li; Lixin Wang

doi:10.2478/msr-2022-0035

Abstract

The information fusion problem is studied for multi-sensor systems in the presence of bounded disturbances. In this paper, a distributed fusion estimation algorithm is proposed based on the set-membership theory, which obtains the overall estimates based on multi-ellipsoids intersection. A parameter adaptive adjustment scheme is derived to guarantee the performance of the algorithm. The feedback mechanism is also introduced to enhance the estimation procedure. Through theoretical analysis and simulation, the performance of the proposed algorithm is analyzed, and some interesting properties of the proposed algorithm are proved. Results show that the proposed algorithm improves the point estimation accuracy. Compared with the algorithm without feedback, the one with feedback has better local estimation. Meanwhile, the effectiveness of the proposed algorithm in improving state estimation accuracy has been proved by the simulation results.

References

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Distributed Fusion Estimation for the Measurements with Bounded Disturbances

Abstract

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