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Design of a Sequential Filtering Method Fully Equivalent to the Centralized Filter with Cross-Correlated Noise
Yanpeng Huang1, Weichang Huang1, Chenglin Wen2
1College of Automation, Guangdong University of Petrochemical Technology, Maoming 525000, China.
Sensors (Basel, Switzerland)
|August 13, 2026
Summary
This study introduces a sequential fusion Kalman filter for multi-sensor systems with correlated noises. The novel method achieves optimal fusion estimation, matching centralized filter performance.
Area of Science:
- Control Systems Engineering
- Signal Processing
- Estimation Theory
Background:
- Multi-sensor systems often face challenges with correlated measurement and process noises.
- Traditional fusion methods can be computationally intensive or suboptimal with complex noise structures.
Purpose of the Study:
- To develop an optimal fusion estimation algorithm for multi-sensor systems with cross-correlated noises.
- To establish a sequential fusion Kalman filter that is computationally efficient and maintains high accuracy.
Main Methods:
- Utilizing the Gram-Schmidt orthogonalization principle to create an orthogonal innovation sequence.
- Developing a sequential fusion Kalman filter based on the orthogonalized innovations.
- Proving the equivalence in estimation performance between the sequential and centralized fusion Kalman filters.
Main Results:
- A novel sequential fusion Kalman filter was successfully designed.
- The proposed filter demonstrated rigorous equivalence in estimation performance to the centralized fusion Kalman filter.
- Simulations confirmed the algorithm's effectiveness in a target tracking scenario.
Conclusions:
- The sequential fusion Kalman filter provides an effective and accurate solution for optimal fusion estimation in multi-sensor systems with correlated noises.
- This approach offers a computationally advantageous alternative to centralized methods without sacrificing performance.
- The algorithm is validated for practical applications such as target tracking.
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