Establishment of Design Theory for Optimally Integrating Information from Multiple Sensors with Different Sampling Rates

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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.