Maree Farquharson

A computationally efficient technique for estimating the parameters of polynomial-phase signals from noisy observations

Many real-world applications are characterized by the presence of polynomial phase signals embedded in noise. These applications include radar, sonar, telemetry, communications, and power systems. In a significant number of these applications, it is highly desirable to be able to accurately estimate the polynomial phase signal parameters. This correspondence presents a computationally efficient method for estimating any or all of the parameters of polynomial-phase signals in white Gaussian noise and provides a first-order statistical analysis of the technique. Simulations are also presented to support the theoretical analysis.

Extending the Performance of the Cubic Phase Function Algorithm

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 algorithms 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.

An algorithm for estimating time-varying Doppler at low SNR

This paper presents an algorithm for estimating the time-varying Doppler return signal from a target which has a time-varying acceleration, with this changing acceleration being modeled as a linear function of time. The method involves modeling the return signal as the sum of a number of constant amplitude cubic phase signals embedded in noise. An algorithm is presented for estimating the phase parameters and subsequently the time-varying Doppler (under the assumption that the Doppler shift is small compared with the center frequency of the irradiation). Simulations are presented to confirm the effectiveness of this algorithm.