A Trajectory Inference Driven Matched Filtering Method for Maneuvering Target Refocusing.

The imaging of maneuvering targets may be defocused by the range cell and Doppler frequency migrations (DFMs) initiated by the targets' complex motions in a long coherent processing interval (CPI). To settle these problems, a generalized matched filtering method driven by the Bayesian motion trajectory inference (BMTI) is presented in this article. Analyses show that any motion trajectory evolutions caused by targets' movements can be described by the state equations derived from a polynomial prediction model. It is then demonstrated the uncertainties of the targets entering/leaving the radar beam coverage area will make the state equations into a state labeled multi-Bernoulli random finite set (LMBRFS). With the scale-transformation-based processing pipeline, a measurement random finite set (RFS) taking both the statistical properties of the maneuvering target with random appearance and disappearance and the noise/clutter into consideration is coined for the state LMBRFS. In this way, a state-space model for describing the target instantaneous motions in a CPI is established. Once the target motion trajectories are inferred by a modified Bayesian filter, it is shown that the matched filter bank designed by the inferred trajectories can refocus the echo energies. Numerical simulation results verify the correctness of our analytical ones, while it is illustrated that our model and method are universal for indicating high-speed and maneuvering targets with any complex and/or even unknown motion forms in a CPI.