Abrupt moving target tracking based on quantum enhanced particle filter

Abrupt-motion tracking is challenging due to the target's unpredictable action. Although particle filter (PF) is suitable for target tracking of nonlinear non-Gaussian systems, it suffers from the problems of particle impoverishment and sample-size dependency. This paper proposed a quantum-inspired particle filter for abrupt-motion tracking. We apply the concept of quantum superposition to transform classical particles into quantum particles. Quantum representation and corresponding quantum operations are addressed to utilize quantum particles. The superposition property of quantum particles avoids the concerns of particle impoverishment and sample-size dependency. The proposed diversity-preserving quantum-enhanced particle filter (DQPF) obtains better accuracy and stability with fewer particles. A smaller sample size also helps to reduce computational complexity. Moreover, it has significant advantages for abrupt-motion tracking. The quantum particles are propagated at the prediction stage. They will exist at possible places when abrupt motion occurs, which reduces the tracking delay and enhances the tracking accuracy. This paper conducted experiments compared to state-of-the-art particle filter algorithms. The numerical results demonstrate that the DQPF is not susceptible to motion mode and particle number. Meanwhile, DQPF maintains excellent accuracy and stability.