Optimal Sensor Scheduling and Selection for Continuous-Discrete Kalman Filtering with Auxiliary Dynamics

2025/07/15 by Mohamad Al Ahdab, John Leth, Ahdab, Mohamad Al +4 · 1 citation
Computer Science · Engineering · #Distributed Control Multi-Agent Systems #Distributed Sensor Networks and Detection Algorithms #FOS: Electrical engineering #Systems and Control (eess.SY) #Target Tracking and Data Fusion in Sensor Networks #cs.SY #eess.SY #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2507.11240

published as Proceedings of the 42nd International Conference on Machine Learning (ICML 2025), PMLR 267:703-729, 2025 · Accepted to ICML 2025

arxiv created 2025/07/15 · openalex publication_date 2025/07/15 · openalex created_date 2025/10/08 · arxiv updated 2026/07/30 · openalex updated_date 2026/08/02

Abstract

We study the Continuous-Discrete Kalman Filter (CD-KF) for State-Space Models (SSMs) where continuous-time dynamics are observed via multiple sensors with discrete, irregularly timed measurements. Our focus extends to scenarios in which the measurement process is coupled with the states of an auxiliary SSM. For instance, higher measurement rates may increase energy consumption or heat generation, while a sensor's accuracy can depend on its own spatial trajectory or that of the measured target. Each sensor thus carries distinct costs and constraints associated with its measurement rate and additional constraints and costs on the auxiliary state. We model measurement occurrences as independent Poisson processes with sensor-specific rates and derive an upper bound on the mean posterior covariance matrix of the CD-KF along the mean auxiliary state. The bound is continuously differentiable with respect to the measurement rates, which enables efficient gradient-based optimization. Exploiting this bound, we propose a finite-horizon optimal control framework to optimize measurement rates and auxiliary-state dynamics jointly. We further introduce a deterministic method for scheduling measurement times from the optimized rates. Empirical results in state-space filtering and dynamic temporal Gaussian process regression demonstrate that our approach achieves improved trade-offs between resource usage and estimation accuracy.

Citations

• Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation
• Control Barrier Functions: Theory and Applications
• Stability of Kalman Filtering with a Random Measurement Equation: Application to Sensor Scheduling with Intermittent Observations
• Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyond
• Practical Bayesian Optimization of Machine Learning Algorithms
• Scheduling Kalman Filters in Continuous Time
• On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming

Cited by

• Kalman-Bucy Filtering with Randomized Sensing: Fundamental Limits and Sensor Network Design for Field Estimation

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