Computationally efficient coherent detection and parameter estimation algorithm for maneuvering target

Abstract In this paper, a computationally efficient coherent detection and parameter estimation algorithm via symmetric autocorrelation function (SAF) and scaled Fourier transform (i.e., SAF-SFT) is proposed, involving range cell migration (RCM) and Doppler spread (DS) within the coherent integration (CI) time. In particular, the first SAF and SFT operations are applied to achieve the range and velocity estimations after the generalized keystone transform. With the estimations, the remaining RCM induced by target’s velocity could be removed and the target signal could be extracted along the range cell. Then the second SAF and SFT operations are performed on the extracted signal, where the target energy could be coherent integrated and the acceleration estimation can be obtained. Cross term of SAF-SFT is also analyzed and its characteristic indicates the applicability in the scenario of multi-targets. Detailed comparisons of SAF-SFT with several typical algorithms with respect to computational cost, detection probability and parameter estimation ability show that the SAF-SFT could strike a balance between computational cost and detection probability as well as the estimation performance. Simulation results and real test experiment are given to verify the SAF-SFT based approach.