Performance bounds for polynomial phase parameter estimation with nonuniform and random sampling schemes

Copyright © 2000 IEEEEstimating the parameters of a cisoid with an unknown amplitude and polynomial phase using uniformly spaced samples can result in ambiguous estimates due to Nyquist sampling limitations. It has been shown previously that nonuniform sampling has the advantage of unambiguous estimates beyond the Nyquist frequency; however, the effect of sampling on the Cramer-Rao bounds is not well known. This paper first derives the maximum likelihood estimators and Cramer-Rao bounds for the parameters with known, arbitrary sampling times. It then outlines two methods for incorporating random sampling times into the lower variance bounds, describing one in detail. It is then shown that for a signal with additive white Gaussian noise the bounds for the estimation with nonuniform sampling tend toward those of uniform sampling. Thus, nonuniform sampling overcomes the ambiguity problems of uniform sampling without incurring the penalty of an increased variance in parameter estimation.Jonathan A. Legg and Douglas A. Gra

Classification of chirp signals using hierarchical bayesian learning and MCMC methods

This paper addresses the problem of classifying chirp signals using hierarchical Bayesian learning together with Markov chain Monte Carlo (MCMC) methods. Bayesian learning consists of estimating the distribution of the observed data conditional on each class from a set of training samples. Unfortunately, this estimation requires to evaluate intractable multidimensional integrals. This paper studies an original implementation of hierarchical Bayesian learning that estimates the class conditional probability densities using MCMC methods. The performance of this implementation is first studied via an academic example for which the class conditional densities are known. The problem of classifying chirp signals is then addressed by using a similar hierarchical Bayesian learning implementation based on a Metropolis-within-Gibbs algorithm

A time-frequency based method for the detection and tracking of multiple non-linearly modulated components with births and deaths

International audienceThe estimation of the components which contain the characteristics of a signal attracts great attention in many real world applications. In this paper, we address the problem of the tracking of multiple signal components over discrete time series. We propose an algorithm to first detect the components from a given time-frequency distribution and then to track them automatically. In the first place, the peaks corresponding to the signal components are detected using the statistical properties of the spectral estimator. Then, an original classifier is proposed to automatically track the detected peaks in order to build components over time. This classifier is based on a total divergence matrix computed from a peak-component divergence matrix that takes account of both amplitude and frequency information. The peak-component pairs are matched automatically from this divergence matrix. We propose a stochastic discrimination rule to decide upon the acceptance of the peak-component pairs. In this way, the algorithm can estimate the number, the amplitude and frequency modulation functions, and the births and the deaths of the components without any limitation on the number of components. The performance of the proposed method, a post-processing of a time-frequency distribution is validated on simulated signals under different parameter sets. The method is also applied to 4 real-world signals as a proof of its applicability. Index Terms—Time-frequency domain, multicomponent, peak detection, component tracking, amplitude and frequency modulation , nonlinear, nonstationary, births and death

A Computationally Efficient algorithm to estimate the Parameters of a Two-Dimensional Chirp Model with the product term

Chirp signal models and their generalizations have been used to model many natural and man-made phenomena in signal processing and time series literature. In recent times, several methods have been proposed for parameter estimation of these models. These methods however are either statistically sub-optimal or computationally burdensome, specially for two dimensional (2D) chirp models. In this paper, we consider the problem of parameter estimation of 2D chirp models and propose a computationally efficient estimator and establish asymptotic theoretical properties of the proposed estimators. And the proposed estimators are observed to have the same rates of convergence as the least squares estimators (LSEs). Furthermore, the proposed estimators of chirp rate parameters are shown to be asymptotically optimal. Extensive and detailed numerical simulations are conducted, which support theoretical results of the proposed estimators

Estimation rapide des paramètres d'un signal à phase polynomiale

National audiencePolynomial phase signals belong to a wide class of non-stationary signals used for modeling and engineering applications. In this paper, we take benefits of some advances in robust estimation in order to propose a new algorithm for estimating the parameters of a polynomial phase signal. The advantages of this algorithm are being fast and being robust to the shape of the noise

Parameter estimation for chirp signals with deterministic time-varying amplitude

We consider the problem of estimating the parameters of chirp signals with deterministic time-varying amplitude . A method which is computationally simpler than the Maximum Likelihood estimator is proposed . It invokes the extended invariance principle to split the minimization problem and to decouple estimation of the phase parameters from that of the amplitude parameters . In a first step, using a less detailed model for the signal, a simple scheme for estimating the phase parameters is presented . Then, amplitude parameters are obtained from least-squares minimization techniques . The overall procedure provides asymptotically efficient estimates . Numerical simulations attest to the validity of the theoretical analysis .Nous traitons dans cet article de l'estimation de signaux chirp dont l'amplitude, déterministe, varie dans le temps. Nous proposons une alternative à l'estimateur du Maximum de Vraisemblance qui est plus simple d'un point de vue calculatoire. Pour ceci, nous utilisons le principe d'invariance étendu qui permet de scinder le problème de minimisation et de découpler l'estimation des paramètres de phase de celle des paramètres d'amplitude. Dans un premier temps, en utilisant un modèle moins détaillé pour le signal, c'est-à-dire en considérant que tous les échantillons de l'amplitude sont à estimer, on obtient de manière simple les paramètres de phase. Les paramètres d'amplitude sont ensuite estimés par une technique des moindres carrés. La procédure permet d'obtenir des estimateurs asymptotiquement efficaces. Des simulations numériques viennent valider l'étude théorique

On the Analytic Wavelet Transform

An exact and general expression for the analytic wavelet transform of a real-valued signal is constructed, resolving the time-dependent effects of non-negligible amplitude and frequency modulation. The analytic signal is first locally represented as a modulated oscillation, demodulated by its own instantaneous frequency, and then Taylor-expanded at each point in time. The terms in this expansion, called the instantaneous modulation functions, are time-varying functions which quantify, at increasingly higher orders, the local departures of the signal from a uniform sinusoidal oscillation. Closed-form expressions for these functions are found in terms of Bell polynomials and derivatives of the signal's instantaneous frequency and bandwidth. The analytic wavelet transform is shown to depend upon the interaction between the signal's instantaneous modulation functions and frequency-domain derivatives of the wavelet, inducing a hierarchy of departures of the transform away from a perfect representation of the signal. The form of these deviation terms suggests a set of conditions for matching the wavelet properties to suit the variability of the signal, in which case our expressions simplify considerably. One may then quantify the time-varying bias associated with signal estimation via wavelet ridge analysis, and choose wavelets to minimize this bias

Local Sparse Reconstructions of Doppler Frequency using Chirp Atoms

The paper considers sparse reconstruction of Doppler and microDoppler time-frequency (TF) signatures of radar returns of moving targets from limited or incomplete data. The typically employed sinusoidal dictionary, relating the windowed compressed measurements to the signal local frequency contents, induces competing requirements on the window size. In this paper, we use chirp dictionary for each window position to relax this adverse window length-sparsity interlocking. It is shown that local frequency reconstruction using chirp atoms better represents the approximate piece-wise chirp behavior of most Doppler TF signatures. This enables the utilization of longer windows for accurate time-frequency representations. Simulation examples are provided demonstrating the superior performance of local chirp dictionary over its sinusoidal counterpart