Distance and Velocity Prediction by Extended Kalman Filter in Mobile Molecular Communication

Distance and velocity estimation is challenging in mobile molecular communication, since estimates may be stale by the time the terminals gather the required information. To address both the difficulty and the delay of estimation, we propose an extended Kalman filter to predict both the distance between transmitter and receiver, and the drift velocity of the receiver, in a realistic blood-vessel-type flow regime. The extended Kalman filter is appropriate for the problem, as it can be used to predict quadratic quantities such as distance, and has manageable computational complexity. We derive the extended Kalman filter and show that it delivers excellent performance in predicting both distance and velocity in the presence of nonuniform flow.