To improve detection performance for a reconnaissance receiver, which is designed to detect the non-cooperative MIMO-LFM radar signal under low SNR condition, this letter proposed a novel signal detection method. This method is based on Fractional Fourier Transform with entropy weight (FRFTE) and autocorrelation algorithm. In addition, the flow chart and feasibility of the proposed algorithm are analyzed. Finally, applying our method to Wigner Hough Transform (WHT), we demonstrate the superiority of this method by simulation results.

A novel Nyquist Folding Receiver (NYFR) based passive localization algorithm with Sparse Bayesian Learning (SBL) is proposed to estimate the position of a spaceborne Synthetic Aperture Radar (SAR).Taking the geometry and kinematics of a satellite into consideration, this paper presents a surveillance geometry model, which formulates the localization problem into a sparse vector recovery problem. A NYFR technology is utilized to intercept the SAR signal. Then, a convergence algorithm with SBL is introduced to recover the sparse vector. Furthermore, simulation results demonstrate the availability and performance of our algorithm.