The Kalman Evolve: Closing the Gap in Kalman Filtering via Interpretable Algorithm Discovery
arXiv:2605.26830v1 Announce Type: new Abstract: State estimation is a fundamental problem in control and signal processing, for which the Kalman Filter provides an optimal solution under linear dynamics, Gaussian noise, and known noise covariances. However, these assumptions often fail in realistic sensing settings such as Doppler radar and LiDAR. In these cases, the optimal estimator is inherently nonlinear, which leads to systematic performance degradation. This creates a performance gap that cannot be eliminated by tuning the noise covariance parameters (i.e., the process and measurement no
The proliferation of advanced sensing technologies like Doppler radar and LiDAR in autonomous systems and robotics necessitates more robust and adaptive state estimation techniques beyond the limitations of traditional Kalman Filters.
Improved state estimation through interpretable algorithm discovery directly enhances the reliability and performance of AI systems in critical applications, reducing errors in navigation, control, and object tracking for both commercial and defense sectors.
This research suggests a pathway to more accurate and robust real-world AI applications by bridging the performance gap in state estimation for non-linear, non-Gaussian scenarios, moving towards more autonomous and reliable systems.
- · AI/ML researchers
- · Robotics companies
- · Autonomous vehicle developers
- · Defense contractors
- · Developers reliant solely on traditional Kalman Filters
More accurate and reliable AI-driven systems in dynamic environments become feasible.
Accelerated development and adoption of autonomous technologies across various industries due to enhanced foundational capabilities.
Reduced need for human oversight in complex operational settings as AI systems gain greater situational awareness and prediction capabilities.
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Read at arXiv cs.LG