Estimating random amplitude polynomial phase signals: a cyclostationary approach

Shamsunder, S.; Giannakis, Georgios B.; Friedlander, B.

doi:10.1109/78.348131

Cited by 95 publications

(31 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Results in the literature on estimation of PPS parameters are limited to the monocomponent case such as fast maximum likelihood (ML) [1], fast instantaneous frequency (IF) estimation [18], the dechirping algorithm [7], the nonlinear instantaneous least squares (NILS) [3], the well-known high ambiguity function (HAF) [19], [21]- [24], and its numerous extensions to timevarying and random amplitudes [8], [10], [11], [15], [16], [29], [31]. Other issues relating to monocomponent polynomial phase signal (mono-PPS) analysis include aliasing [2], nonuniform sampling schemes [13] and bootstrap model selection [32].…”

mentioning

confidence: 99%

Analysis of Multicomponent Polynomial Phase Signals

Pham

Zoubir

2007

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

Abstract-While the theory of estimation of monocomponent polynomial phase signals is well established, the theoretical and methodical treatment of multicomponent polynomial phase signals (mc-PPSs) is limited. In this paper, we investigate several aspects of parameter estimation for mc-PPSs and derive the Cramér-Rao bound. We show the limits of existing techniques and then propose a nonlinear least squares (NLS) approach. We also motivate the use the Nelder-Mead simplex algorithm for minimizing the nonlinear cost function. The slight increase in computational complexity is a tradeoff for improved mean square error performance, which is evidenced by simulation results.Index Terms-High ambiguity function (HAF), Nelder-Mead algorithm, nonlinear least squares, nonstationary, polynomial phase signals.

show abstract

mentioning

confidence: 99%

Analysis of Multicomponent Polynomial Phase Signals

Pham

Zoubir

2007

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

show abstract

“…Parametric methods mostly based on polynomial phase modeling may be used to analyze and estimate such signals and separate them into their components; such as nonlinear least squares techniques [1,2], a maximum likelihood algorithm [3], an expectation-maximization based method [4], an array processing approach based on state estimation via an extended Kalman filter [5], a cyclic moment based method for polynomial phase signals with independent random amplitudes [6], techniques using transforms like high-order ambiguity function [7][8][9] and time-frequency (TF) Hough transform [10][11][12], and an approach for chirplet approximation [13], among other such methods.…”

Section: Introductionmentioning

confidence: 99%

Filtering in the joint time/chirp-rate domain for separation of quadratic and cubic phase chirp signals

Özgen

2012

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

This article investigates the possibility and convenience of a filtering operation in the joint time/chirp-rate (TCR) domain, and proposes a novel linear TCR filter for decomposing multicomponent signals into their quadratic and/ or cubic phase chirp components with monotonic instantaneous chirp-rate (ICR) laws only. The TCR domain mask of the filter is selected on a display of a TCR representation of an input signal to isolate the desired chirp component. Projecting the input signal onto the phase signal associated with the TCR mask and approximating the phase difference in this projection operation in terms of ICR values result in the proposed TCR filter that recovers the selected component. Simulations illustrate the proposed filtering in recovery of undersampled cubic phase signals and in resolving back-to-back objects from in-line holograms for which cases it is easier to design filter masks in the TCR domain than in the time-frequency domain.

show abstract

“…Similar signal models for which parameter estimation techniques have been developed include random amplitude sinusoids [9], constant amplitude polynomial phase signals [6] and deterministic time-varying amplitude frequency modulated signals [3]. Estimation procedures for the problem we are considering here have been reported in [1,4,8]. In [1], two estimation algorithms were considered.…”

Section: Introductionmentioning

confidence: 99%

“…In [4], Ghogho et al proposed a technique based on the maximum likelihood criterion which is asymptotically efficient when the random interferences are white Gaussian processes. We consider the estimation procedure proposed in [8] which uses the cyclic moments of the observed signal to estimate the phase parameters. The subject of this paper is a statistical accuracy analysis of the phase parameter estimators obtained from the cyclic moments for the case where the signal is modelled by (1).…”

Section: Introductionmentioning

confidence: 99%

Statistical analysis of phase parameter estimates of random amplitude linear FM signals

Morelande

Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics. SPW-HOS '99

View full text Add to dashboard Cite

The use of cyclic moments for estimating the phase parameters of random amplitude linear FM signals in additive noise is investigated. In particular, the covariance matrix of the phase parameter estimates obtained from the cyclic moments is derived. It is shown that, in general, the accuracy of the phase parameter estimates is dependent on the lag parameter chosen for the cyclic correlation. The analysis also reveals that, under certain conditions, the variances of the lower order phase parameter estimates are independent of the lag parameter.

show abstract

Estimating random amplitude polynomial phase signals: a cyclostationary approach

Cited by 95 publications

References 23 publications

Analysis of Multicomponent Polynomial Phase Signals

Analysis of Multicomponent Polynomial Phase Signals

Filtering in the joint time/chirp-rate domain for separation of quadratic and cubic phase chirp signals

Statistical analysis of phase parameter estimates of random amplitude linear FM signals

Contact Info

Product

Resources

About