Scalable Sensor Scheduling for Continuous-Discrete Kalman Filtering via Information-Form Surrogate Dynamics
For researchers designing sensor schedules in continuous-discrete systems, this work offers a computationally cheaper method with performance guarantees, though it is an incremental improvement over existing covariance-form surrogates.
The paper proposes an information-form deterministic surrogate for sensor scheduling in continuous-discrete Kalman filtering with Poisson measurements, which simplifies the derivative structure and yields substantial computational savings in many-sensor settings while retaining comparable performance and providing two-sided bounds.
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.