Optimal Sequential Fusion Kalman Filter for Multi-Sensor Linear Systems with Noise Cross-Correlated
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a globally optimal sequential fusion Kalman filter for multi-sensor linear systems with correlated noise. The new filter achieves strict equivalence to centralized fusion, solving a long-standing problem in state estimation.
Area Of Science
- Control Systems Engineering
- Signal Processing
- Estimation Theory
Background
- State estimation in multi-sensor linear systems is challenged by correlated process and measurement noise.
- Existing sequential fusion Kalman filters lack strict equivalence to centralized fusion for these systems.
- Directly decorrelating process and measurement noise is a significant hurdle.
Purpose Of The Study
- To design a truly globally optimal sequential fusion Kalman filter for linear systems with cross-correlated noise.
- To achieve strict equivalence between sequential and centralized fusion Kalman filters.
- To address the unsolved problem of sequential fusion for systems with mutually correlated measurement noises.
Main Methods
- An innovative indirect decorrelation method for process and measurement noise.
- Rewriting measurement equations using the Gram-Schmidt orthogonalization principle to achieve noise independence.
- Establishing a sequential fusion Kalman filter based on the transformed measurement equations.
Main Results
- A novel sequential fusion Kalman filter is developed, achieving global optimality.
- Theoretical proof rigorously establishes the equivalence between the proposed sequential filter and centralized fusion.
- The filter's effectiveness is demonstrated through a constant velocity target tracking simulation.
Conclusions
- The proposed method successfully designs a globally optimal sequential fusion Kalman filter for complex noise scenarios.
- The established equivalence provides a practical and efficient alternative to centralized fusion for state estimation.
- This work offers a significant advancement in sequential data fusion for multi-sensor systems.
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