Establishment of Design Theory for Optimally Integrating Information from Multiple Sensors with Different Sampling Rates
Associate Professor Hiroshi Okajima of Kumamoto University has established a design theory for multirate steady-state Kalman filters that optimally integrate information from multiple sensors with different sampling periods. This theory solves mathematical problems previously intractable with conventional methods by using an optimization approach based on Linear Matrix Inequalities (LMI). It has achieved approximately double the estimation accuracy in automotive navigation compared to GPS alone and is expected to be applied in various engineering fields like autonomous driving, robotics, and IoT.
📋 Article Processing Timeline
- 📰 Published: April 6, 2026 at 18:46
- 🔍 Collected: April 6, 2026 at 10:31
- 🤖 AI Analyzed: April 17, 2026 at 13:20 (266h 48m after Collected)
(Points)
- Established a design theory for Kalman filters that optimally integrate information from multiple sensors with different sampling periods.
- Solved a mathematical problem (positive semidefinite noise covariance) that could not be resolved by conventional standard methods through optimization using Linear Matrix Inequalities (LMI).
- In a verification assuming automotive navigation, achieved approximately twice the estimation accuracy (±0.56 m*) compared to the accuracy of GPS alone (±1 m).
-[1][2] Expected to be applied to a wide range of engineering fields using multiple sensors, such as autonomous driving, robotics, and IoT.
*[1][2]Note: The original Japanese text contains a typo "±56 m"; based on the research paper, the correct value is ±0.56 m.
(Overview)
Associate Professor Hiroshi Okajima of the Graduate School of Science and Technology, Kumamoto University, has developed a design theory for multirate steady-state Kalman filters that optimally integrate information from systems equipped with multiple sensors having different sampling periods to estimate internal states. This theory solves problems that were mathematically intractable with conventional design methods by using an optimization approach based on Linear Matrix Inequalities (LMI).
(Development)
The design theory constructed in this research is a versatile framework applicable to any linear system where sampling periods are known and repeat periodically. Beyond sensor fusion for autonomous vehicles and robots, it is expected to find applications in a wide range of engineering fields with sensors of different periods, such as chemical plant control, power system monitoring, and sensor networks. MATLAB and Python implementation codes for the design are available on GitHub, allowing researchers and engineers to utilize them immediately. Future work will focus on robust design for cases with uncertainty in system parameters and expansion to nonlinear systems.
Paper Title: LMI Optimization-Based Multirate Steady-State Kalman Filter Design
Author: Hiroshi Okajima
Journal: IEEE ACCESS (Open Access)
URL: https://ieeexplore.ieee.org/document/11460152
DOI: 10.1109/ACCESS.2026.3679647
Supplementary Materials:
Blog Post
https://blog.control-theory.com/entry/multirate-kalman-filter-lmi
Implementation Code (GitHub)
https://github.com/Hiroshi-Okajima/multirate-kalman-filter
[Details] Press Release (PDF 390KB)
[Contact Information]
Graduate School of Science and Technology (Engineering), Kumamoto University
Contact: Hiroshi Okajima (Associate Professor)
Phone: 096-342-3603
e-mail: okajima [at] cs.kumamoto-u.ac.jp
(Please replace [at] with @)
https://www.control-theory.com
- Established a design theory for Kalman filters that optimally integrate information from multiple sensors with different sampling periods.
- Solved a mathematical problem (positive semidefinite noise covariance) that could not be resolved by conventional standard methods through optimization using Linear Matrix Inequalities (LMI).
- In a verification assuming automotive navigation, achieved approximately twice the estimation accuracy (±0.56 m*) compared to the accuracy of GPS alone (±1 m).
-[1][2] Expected to be applied to a wide range of engineering fields using multiple sensors, such as autonomous driving, robotics, and IoT.
*[1][2]Note: The original Japanese text contains a typo "±56 m"; based on the research paper, the correct value is ±0.56 m.
(Overview)
Associate Professor Hiroshi Okajima of the Graduate School of Science and Technology, Kumamoto University, has developed a design theory for multirate steady-state Kalman filters that optimally integrate information from systems equipped with multiple sensors having different sampling periods to estimate internal states. This theory solves problems that were mathematically intractable with conventional design methods by using an optimization approach based on Linear Matrix Inequalities (LMI).
(Development)
The design theory constructed in this research is a versatile framework applicable to any linear system where sampling periods are known and repeat periodically. Beyond sensor fusion for autonomous vehicles and robots, it is expected to find applications in a wide range of engineering fields with sensors of different periods, such as chemical plant control, power system monitoring, and sensor networks. MATLAB and Python implementation codes for the design are available on GitHub, allowing researchers and engineers to utilize them immediately. Future work will focus on robust design for cases with uncertainty in system parameters and expansion to nonlinear systems.
Paper Title: LMI Optimization-Based Multirate Steady-State Kalman Filter Design
Author: Hiroshi Okajima
Journal: IEEE ACCESS (Open Access)
URL: https://ieeexplore.ieee.org/document/11460152
DOI: 10.1109/ACCESS.2026.3679647
Supplementary Materials:
Blog Post
https://blog.control-theory.com/entry/multirate-kalman-filter-lmi
Implementation Code (GitHub)
https://github.com/Hiroshi-Okajima/multirate-kalman-filter
[Details] Press Release (PDF 390KB)
[Contact Information]
Graduate School of Science and Technology (Engineering), Kumamoto University
Contact: Hiroshi Okajima (Associate Professor)
Phone: 096-342-3603
e-mail: okajima [at] cs.kumamoto-u.ac.jp
(Please replace [at] with @)
https://www.control-theory.com