Radar Detection and Motion Parameters Estimation of Maneuvering Target Based on the Extended Keystone Transform (July 2018)

This paper focuses on the range migration (RM) and Doppler frequency migration corrections of the long-time coherent integration, and a fast algorithm is proposed for maneuvering target detection and motion parameters estimation under a low signal-to-noise ratio (SNR). By utilizing the autocorrelation function with respect to the slow time, the newly defined extended keystone transform, scaled Fourier transform, and fast Fourier transform (FFT), the proposed algorithm coherently integrates the target energy into a peak in a three-dimensional (3-D) parameter space. Thereafter, the target's radial velocity and acceleration are estimated by the peak's coordinate. After compensating off the RM and DFM via the estimated motion parameters, the inverse FFT along the range frequency and azimuth FFT are used to realize the coherent integration for the target detection. The cross term characteristic is also analyzed and shows the applicability of the proposed algorithm in the scenario of multiple targets. Comparisons with representative algorithms in the computational complexity, motion parameters estimation, and target detection are presented in this paper, which leads us to conclude that the proposed algorithm can greatly reduce the computational complexity with an acceptable integration SNR gain loss. Finally, the experiments with the simulated and real measured radar data are conducted to verify the proposed algorithm.