Kalman-Bucy Filtering with Randomized Sensing: Fundamental Limits and Sensor Network Design for Field Estimation
The authors analyzed Kalman filtering when sensor locations and measurements are random in continuous time. They derived a closed-form upper bound on estimation error covariance and used this to establish fundamental limits for estimating spatiotemporal fields from noisy, randomly placed sensors. This framework provides efficient guidelines for designing optimal sensor networks before deployment.