Detection of UAV Target with Multiple Motion Models Based on Keystone Transform and Short Time Matched Filtering Processing

Abstract

In the signal processing of the moving unmanned aerial vehicle (UAV) target, we find that the UAV target contains multiple motion models within the coherent integration period, which caused by the UAV’s high maneuverability. Afterwards the range migration (RM) and Doppler frequency migration (DFM) will occur during every motion model. Therefore, the traditional processing method that of dealing with the target with single motion model will be invalidated. In this paper, we present a novel signal processing method, known as keystone transform-short time matched filtering processing (KT-STMFP), to complete the energy focus of the UAV target with multiple motion models. Firstly, applying keystone transform to the beat signal, which operation will eliminate the RM caused by the radial velocity. Then, preforming the processing of short time matched filter to compensate the DFM and estimate the parameters during each motion model. Applying these parameters and the relationship of the between current motion model and next one to construct the phase compensation function. Finally, the coherent integration of the UAV target with multiple motion models is achieved via two-dimensional (2-D) fast Fourier transform (FFT) with respect to the fast-time and the slow-time. Thus, it can be seen that the KT-STMFP method can eliminate the effects of the both RM and DFM within the coherent integration time. What’s more, both the numerical simulation and the experimental results demonstrate the validity of the proposed algorithm and show that KT-STMFP-based method could attain superior focusing performance.