A Novel Parameter Estimation Algorithm for DSSS Signals Based on Compressed Sensing

A novel parameter estimation algorithm based on Compressed sensing (CS) for the Direct sequences spread spectrum (DSSS) signals in high dynamic environments is proposed. In this algorithm, Fractional Fourier transform (FrFT) is first employed to estimate Doppler frequency rate, followed by the quadric phase term compensation. The compensation results are divided into several segments with equal length and coherent integration is carried out within each segment respectively. A convex optimization algorithm is applied to estimate the velocity and initial range of the target simultaneously based on the sparsity of target in the code phase domain. The proposed algorithm is capable of overcoming the limitation of Doppler frequency ambiguity and obtaining the accurate parameter estimates without correcting the code phase drift. Simulation results are presented to demonstrate the validity of the proposed algorithm.