Extended Kalman Filter with Reduced Computational Demands for Systems with Non-Linear Measurement Models
1Military University of Technology, ul. gen. S. Kaliskiego 2, 00-908 Warszawa, Poland.
Sensors (Basel, Switzerland)
|March 18, 2020
Summary
This study introduces a method to reduce computational load in Extended Kalman Filters for non-linear systems. By selectively linearizing models based on Jacobian variability, significant computational savings are achieved without compromising estimation accuracy.
Area of Science:
- Signal Processing
- Control Systems Engineering
- Navigation Systems
Background:
- Extended Kalman Filters (EKFs) are crucial for radio-location and radio-navigation.
- Non-linear measurement models necessitate complex EKFs, increasing computational cost.
- Linearization via Jacobian matrices in EKFs is computationally intensive.
Purpose of the Study:
- To propose a method for reducing computational complexity in EKFs for systems with non-linear measurement models.
- To optimize EKF performance by minimizing unnecessary linearization computations.
- To maintain estimation accuracy while reducing computational burden.
Main Methods:
- Analyzing the variability of Jacobian matrices within the EKF loop.
- Implementing a threshold-based linearization strategy.
- Developing a simulation-based method for threshold determination.
Main Results:
- Significant reduction in computational burden achieved through conditional linearization.
- Negligible increase in estimation errors demonstrated for optimized threshold values.
- Validation of the method using simplified radar system models.
Conclusions:
- The proposed threshold-based Jacobian analysis offers an effective approach to EKF computational complexity reduction.
- This method provides a practical solution for enhancing the efficiency of navigation and location systems.
- The technique balances computational savings with reliable estimation accuracy.
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