Enhanced Sequential Covariance Intersection Fusion

TL;DR

This paper introduces an enhanced sequential covariance intersection fusion method that guarantees unbiased, consistent, and structure-independent fusion estimates, improving practical robustness and performance in sensor fusion applications.

Contribution

It develops an improved sequential CI fusion approach with proven unbiasedness, consistency, and structure-independence, along with a new weighting criterion for optimal fusion performance.

Findings

Fusion estimates are unbiased and consistent.

Fusion results are independent of fusion structure.

Simulation confirms improved performance and robustness.

Abstract

This paper is concerned with the sequential covariance intersection (CI) fusion problem that the fusion result is independent of fusion structure including the fusion order and the number of estimates fused in each sequential fusion. An enhanced sequential CI fusion is first developed to better meet the practical requirements as compared with the existing batch and sequential CI fusion. Meanwhile, it is proved that the enhanced sequential CI fusion ensures the fusion estimate and covariance are unbiased and consistent. Notice that the fusion structure of the enhanced sequential CI fusion is unpredictable in practice, which may have negative impacts on the fusion performance. To this end, a weighting fusion criterion with analytical form is further proposed, and can be depicted by different formulas when choosing different performance indexes. For this criterion, it is proved that the fusion results are not affected by the fusion structure, and thus the fusion performance can be guaranteed. Finally, simulation examples are utilized to demonstrate the effectiveness and advantages of the proposed methods.