A Novel Coherent Integration Algorithm for Maneuvering Target Detection Based on Symmetric Instantaneous Autocorrelation Function

Abstract

Detection and parameter estimation of maneuvering targets having a jerking motion are some of the challenging problems for modern radar systems. Such targets usually introduce range migration (RM) and Doppler frequency migration (DFM) problems leading to serious performance degradation in detection. To address these problems, a novel coherent integration (CI) algorithm is proposed based on a new symmetric instantaneous autocorrelation function (NSIAF), which can be utilized to reduce the order on the slow time and to eliminate the linear range migration (LRM) first. Then, the jerk and acceleration of the target are estimated after applying the keystone transform (KT) and the scaled Fourier transform (SFT); both of these are then used to construct the reference function for matched filtering. Finally, CI and target detection can be accomplished by the scaled inverse Fourier transform (SCIFT) after matched filtering. Both simulation data (this work) and practical radar experiment data (data set of others) were processed to validate the proposed algorithm. Compared with other representative algorithms, our algorithm can achieve a good balance between computational complexity and detection performance.