Bat-inspired adaptive design of waveform and trajectory for radar

We propose to design jointly the waveform and trajectory of a radar mounted on a moving platform, in order to improve the system performance for tracking maneuvering targets. Inspired by bats, we develop an adaptive algorithm that chooses the optimal pulse repetition interval (PRI) and path of the radar. Our method automatically schedules a low PRI when it recognizes that the target executes a maneuvering action. Simultaneously, it selects the radar trajectory which provides the best estimation of the target parameters. We derive our approach under a framework of sequential Bayesian filtering and implement it with a particle filter. We consider a library of target state models associated with different PRI values and use multiple model to schedule the optimal PRI. We apply the posterior Cramer-Rao bound to measure the system performance and decide on the optimal radar path.We demonstrate the advantages of the adaptive radar scheme using numerical examples.