Scalable Sensor Scheduling for Continuous-Discrete Kalman Filtering via Information-Form Surrogate Dynamics

TL;DR

This paper introduces a scalable, information-form surrogate method for sensor scheduling in continuous-discrete Kalman filtering with Poisson measurements, improving computational efficiency while maintaining performance bounds.

Contribution

It proposes a novel information-form surrogate that simplifies the nonlinear optimization problem and provides performance bounds, enhancing scalability for multi-sensor systems.

Findings

Substantial computational savings in many-sensor scenarios.

Comparable Monte Carlo performance with the new surrogate.

Provision of two-sided performance bounds for sensor schedules.

Abstract

We study sensor scheduling for continuous-discrete Kalman filtering with Poisson measurement arrivals and propose an information-form deterministic surrogate for scalable offline design. Unlike the covariance-form surrogate, the sensing rates enter through sensor-specific additive information increments, eliminating mixed state-input derivatives in the transcribed nonlinear program and thereby yielding a simpler derivative structure. We further show that, together with the covariance-form surrogate, the proposed surrogate provides computable two-sided performance bounds for a given schedule under stochastic measurement arrivals. Numerical experiments demonstrate substantial computational savings, especially in many-sensor settings, while retaining comparable realized Monte Carlo performance and providing computable two-sided performance bounds for the returned schedule.