An algorithm to track multiple sharply maneuvering targets without prior knowledge about new target birth is proposed. These targets are capable of achieving sharp maneuvers within a short period of time, such as drones and agile missiles. The probability hypothesis density (PHD) filter, which propagates only the first-order statistical moment of the full target posterior, has been shown to be a computationally efficient solution to multitarget tracking problems. However, the standard PHD filter operates on the single dynamic model and requires prior information about target birth distribution, which leads to many limitations in terms of practical applications. In this paper, we introduce a nonzero mean, white noise turn rate dynamic model and generalize jump Markov systems to multitarget case to accommodate sharply maneuvering dynamics. Moreover, to adaptively estimate newborn targets' information, a measurement-driven method based on the recursive random sampling consensus (RANSAC) algorithm is proposed. Simulation results demonstrate that the proposed method achieves significant improvement in tracking multiple sharply maneuvering targets with adaptive birth estimation.

This paper addresses a pursuer tracking problem where the pursuer's acceleration is given by a proportional navigation (PN) guidance law with a time-varying navigation ratio which varies with the relative range between the pursuer and its target. Based on a motion model that exactly describes the relative motion and the PN guidance law, a novel filter for tracking such a pursuer is designed using interactive multiple model (IMM) algorithm and unscented Kalman filtering (UKF) technique. This filter is able to accurately estimate the relative range, relative velocity, and the acceleration of pursuer even if the pursuer adopts a PN guidance law with time-varying navigation ratio. The proposed tracking method is evaluated in extensive Monte Carlo simulations. It is shown that accurate estimation results have been obtained, and the model probabilities in the IMM UKF filter are consistent with real situations.

An active defense scheme in which a ballistic missile launches a defending missile to intercept the incoming intercepting missile is considered. Compared with traditional passive evasion strategies, the active defense scheme can greatly increase the ballistic missile’s survival rate. Different from many terminal guidance laws using the line-of-sight nullifying strategy, a triangle-based guidance strategy by which the defender tries to keep itself on the line-of-sight connecting the target and the incoming intercepting missile is used to design a guidance law. With such a strategy, it is proved that the interceptor’s composite acceleration can be very small due to the acceleration cancellation in the target–interceptor line-of-sight frame and the defender–interceptor line-of-sight frame, meaning that the defender can catch the interceptor with small maneuverability. Based on this strategy, an adaptive nonsingular terminal sliding mode based cooperative guidance law is proposed treating the interceptor’s acceleration as an unknown bounded external disturbance. Simulation results show the high performance of the proposed guidance law.