Stability analysis and application of Kalman filtering with irregularly sampled measurements

This paper analyzes the peak covariance stability properties of Kalman filtering for linear discrete-time systems with irregular time intervals for sampling of output measurements. Existing research on Kalman filtering with irregular sampling mostly builds on probabilistic description of sampling intermittence. In this work, we focus on the general case of irregular sampling without probabilistic assumptions. The obtained stability conditions show that the peak covariance stability is influenced by the eigenvalue distribution of the state matrix in the complex plane. The effectiveness of the analysis is illustrated via a simulation based study on ocean flow field estimation using submersible drogues that can measure position intermittently and acceleration incessantly.