Extending the Performance of the Cubic Phase Function Algorithm

Abstract

This paper details an algorithm for estimating the parameters of cubic phase signals embedded in additive white Gaussian noise. The new algorithm is an extension of the cubic phase (CP) function algorithm, with the extension enabling performance at lower signal-to-noise ratios (SNRs). This improvement in the SNR performance is achieved by coherently integrating the CP function over a compact interval in the two-dimensional CP function space. The computation of the new algorithm is quite moderate, especially when compared to the maximum-likelihood (ML) technique. Above threshold, the algorithm's parameter estimates are asymptotically efficient. A threshold analysis of the algorithm is presented and is supported by simulation results. A method for extending the capability of this algorithm to process higher degree phase signals is also presented. Furthermore, the algorithm is applied to a real data signal.