Switching kalman filters for prediction and tracking in an adaptive meteorological sensing network
We consider the problem of configuring sen- sors in an adaptive sensor network being used to monitor meteorological features. One way to decide future sensor configurations is to base them on information currently being collected. For instance, if a meteorological sensor network is being used to monitor storms in Oklahoma, then the sensors could be dynamically configured based on the predicted storm locations. While Kalman filters and their extensions are commonly used for prediction and tracking, they have been primarily applied to objects with known or fixed dynamics such as missiles or people. We explore the advantages and limitations of using Kalman filters to track objects with nonstationary dynamics (e.g., a storm can grow in size). In particular, we focus on tracking meteorological features over time with the objective of using this information to determine where radars should focus their sensing. We present results for tracking storm cells comparing least-squares regression with Kalman filter and switching Kalman filter methods. Our results show that on average the Kalman filter methods better predict the future location of a storm centroid than does a least-squares regression algorithm currently in use for meteorological storm tracking.