Fast Implementation of Detection for Maneuvering Target with Multiple Motion Models Based on ACCF

This paper focuses on the signal accumulation and target detection of maneuvering targets with multiple motion models. Compared to traditional single model moving targets, the motion of multimodal moving targets is more complex, and the number of motion parameters that need to be estimated increases, posing significant challenges to signal processing. Considering that the Adjacent Cross Correlation Function (ACCF) algorithm exhibits high computational efficiency, it can effectively perform range cell migration (RCM) and Doppler curvature correction without requiring prior knowledge of motion parameters. This paper first employs ACCF to process the pulse compression signals of maneuvering targets, subsequently applying the Fast Fourier Transform (FFT) along the slow-time axis to achieve coherent integration of each motion model. Finally, the target accumulation gain is further enhanced through non-coherent integration across models, facilitating the detection of maneuvering targets. Furthermore, simulation data was used to demonstrate the effectiveness of the proposed method.