Peter O'Shea

Bio: Peter O'Shea is an academic researcher from Queensland University of Technology. The author has contributed to research in topics: Estimation theory & Signal processing. The author has an hindex of 31, co-authored 164 publications receiving 3358 citations. Previous affiliations of Peter O'Shea include RMIT University & Melbourne Institute of Technology.

A fast algorithm for estimating the parameters of a quadratic FM signal

Abstract: This paper describes a fast algorithm that can be used for estimating the parameters of a quadratic frequency modulated (FM) signal. The proposed algorithm is fast in that it requires only one-dimensional (1-D) maximizations. The optimal maximum likelihood method, by contrast, requires a three-dimensional (3-D) maximization, which can only be realized with an exhaustive 3-D grid search. Asymptotic statistical results are derived for all the estimated parameters. The amplitude estimate is seen to be optimal, whereas the phase parameters are, in general, suboptimal. Of the four phase parameter estimates, two approach optimality as the signal-to-noise ratio (SNR) tends to infinity. The other two have mean-square errors that are within 50% of the theoretical lower bounds for high SNR. Simulations are provided to support the theoretical results. Extensions to multiple components and higher order FM signals are also discussed.

A fast algorithm for estimating the parameters of a quadratic FM signal

Abstract: This paper describes a fast algorithm that can be used for estimating the parameters of a quadratic frequency modulated (FM) signal. The proposed algorithm is fast in that it requires only one-dimensional (1-D) maximizations. The optimal maximum likelihood method, by contrast, requires a three-dimensional (3-D) maximization, which can only be realized with an exhaustive 3-D grid search. Asymptotic statistical results are derived for all the estimated parameters. The amplitude estimate is seen to be optimal, whereas the phase parameters are, in general, suboptimal. Of the four phase parameter estimates, two approach optimality as the signal-to-noise ratio (SNR) tends to infinity. The other two have mean-square errors that are within 50% of the theoretical lower bounds for high SNR. Simulations are provided to support the theoretical results. Extensions to multiple components and higher order FM signals are also discussed.

Polynomial Wigner-Ville distributions and their relationship to time-varying higher order spectra

Abstract: The Wigner-Ville distribution (WVD) has optimal energy concentration for linear frequency modulated (FM) signals. This paper presents a generalization of the WVD in order to effectively process nonlinear polynomial FM signals. A class of polynomial WVD's (PWVD's) that give optimal concentration in the time-frequency plane for FM signals with a modulation law of arbitrary polynomial form are defined. A class of polynomial time-frequency distributions (PTFD's) are also defined, based on the class of PWVD's. The optimal energy concentration of the PWVD enables it to be used for estimation of the instantaneous frequency (IF) of polynomial FM signals. Finally, a link between PWVD's and time-varying higher order spectra (TVHOS) is established. Just as the expected value of the WVD of a nonstationary random signal is the time-varying power spectrum, the expected values of the PWVD's have interpretations as reduced TVHOS. >

A new technique for instantaneous frequency rate estimation

Abstract: This letter introduces a two-dimensional bilinear mapping operator referred to as the cubic phase (CP) function. For first-, second-, or third-order polynomial phase signals, the energy of the CP function is concentrated along the frequency rate law of the signal. The function, thus, has an interpretation as a time-frequency rate representation. The peaks of the CP function yield unbiased estimates of the instantaneous (angular) frequency rate (IFR) and, hence, can be used as the basis for an IFR estimation algorithm. The letter defines an IFR estimation algorithm and theoretically analyzes it. The estimation is seen to be asymptotically optimal at the center of the data record for high signal-to-noise ratios. Simulations are provided to verify the theoretical claims.

A new technique for instantaneous frequency rate estimation

Abstract: This letter introduces a two-dimensional bilinear mapping operator referred to as the cubic phase (CP) function. For first-, second-, or third-order polynomial phase signals, the energy of the CP function is concentrated along the frequency rate law of the signal. The function, thus, has an interpretation as a time-frequency rate representation. The peaks of the CP function yield unbiased estimates of the instantaneous (angular) frequency rate (IFR) and, hence, can be used as the basis for an IFR estimation algorithm. The letter defines an IFR estimation algorithm and theoretically analyzes it. The estimation is seen to be asymptotically optimal at the center of the data record for high signal-to-noise ratios. Simulations are provided to verify the theoretical claims.

Time-frequency distributions-a review

Abstract: A review and tutorial of the fundamental ideas and methods of joint time-frequency distributions is presented. The objective of the field is to describe how the spectral content of a signal changes in time and to develop the physical and mathematical ideas needed to understand what a time-varying spectrum is. The basic gal is to devise a distribution that represents the energy or intensity of a signal simultaneously in time and frequency. Although the basic notions have been developing steadily over the last 40 years, there have recently been significant advances. This review is intended to be understandable to the nonspecialist with emphasis on the diversity of concepts and motivations that have gone into the formation of the field. >

Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals

Abstract: The concept of instantaneous frequency (IF), its definitions, and the correspondence between the various mathematical models formulated for representation of IF are discussed. The extent to which the IF corresponds to the intuitive expectation of reality is also considered. A historical review of the successive attempts to define the IF is presented. The relationships between the IF and the group-delay, analytic signal, and bandwidth-time (BT) product are explored, as well as the relationship with time-frequency distributions. The notions of monocomponent and multicomponent signals and instantaneous bandwidth are discussed. It is shown that these notions are well described in the context of the theory presented. >

Linear and quadratic time-frequency signal representations

Abstract: A tutorial review of both linear and quadratic representations is given. The linear representations discussed are the short-time Fourier transform and the wavelet transform. The discussion of quadratic representations concentrates on the Wigner distribution, the ambiguity function, smoothed versions of the Wigner distribution, and various classes of quadratic time-frequency representations. Examples of the application of these representations to typical problems encountered in time-varying signal processing are provided. >