Jiawen Bian

An Efficient and Fast Algorithm for Estimating the Frequencies of 2-D Superimposed Exponential Signals in Presence of Multiplicative and Additive Noise

In this paper, we consider the two-dimensional (2-D) superimposed exponential signals in independently and identically distributed (i.i.d.) multiplicative and additive noise. We use a three step iterative(TSI) algorithm to estimate the frequencies of the considered model. It is observed that the estimator is consistent and works quite well in terms of biases and mean squared errors. Moreover, the convergence rate of the estimators attain \(O_p(M^{-3/2}N^{-1/2})\) and \(O_p(M^{-1/2}N^{-3/2})\) for each pair of frequencies. It attains the convergence rate of the least squares estimators (LSEs) in presence of only additive noise.

An adaptive and computationally efficient algorithm for parameters estimation of superimposed exponential signals with observations missing randomly

In this paper, we consider the parameters estimation of a model of superimposed exponential signals in multiplicative and additive noise when some observations are missing randomly. The least squares estimators (LSEs) and asymptotic Cramer-Rao low bound (ACRLB) for the considered model are studied and the asymptotic distributions of the LSEs for parameters of frequencies, phases and amplitudes of the considered model are also derived and obtained. An adaptive and computationally efficient iterative algorithm is proposed to estimate the frequencies of the considered model. It can be seen that the iterative algorithm works quite well in terms of biases and mean squared errors and the refined estimators by three iterations are observed to be asymptotically unbiased and consistent. The statistics for iteration are designed to change adaptively according to different missing distributions of time points so as to keep the estimators of frequencies to be asymptotically unbiased. Moreover, the proposed estimators attain the same convergence rate and asymptotic distribution as those of LSEs which are used to obtain the confident intervals and coverage probabilities of the frequencies for finite sample. Since the iterative algorithm needs only three iterations to work, it saves much computation time. So the proposed estimators are LSEs equivalent while avoid the heavy computation cost of LSEs. Finally, several simulation experiments are performed to verify the effectiveness of the proposed algorithm. To examine the robustness of the proposed algorithm, we also test the algorithm on the dual tone multi-frequency (DTMF) signal with observations missing in block and symmetric α-stable (SaS) noise condition, as well as on sinusoidal frequency modulated signals.

Statistical Analysis of Non Linear Least Squares Estimation for Harmonic Signals in Multiplicative and Additive Noise

In this paper we consider the problem of parameter estimation for the multicomponent harmonic signals in multiplicative and additive noise. The nonlinear least squares (NLLS) estimators, NLLS1 and NLLS2 proposed by Ghogho et al. (1999b) to estimate the coherent model parameters for single-component harmonic signal, are generalized to the multicomponent harmonic signals for the cases of nonzero- and zero-mean multiplicative noise, respectively. By statistical analysis, some asymptotic results of the NLLS estimators are derived, including the strong consistency, the strong convergence rate and the asymptotic normality. Furthermore, the NLLS1- and NLLS2- based estimators are proposed to estimate the noncoherent model parameters for the cases of nonzero- and zero-mean multiplicative noise, respectively, meanwhile the strong consistency and the asymptotic normality of the NLLS-based estimators are also derived. Finally some numerical experiments are performed to see how the asymptotic results work for finite sample sizes.

Noise subspace approach for frequency estimation of two-dimensional harmonics in zero-mean multiplicative and additive noise

In this paper, a noise subspace (NS) method is proposed for estimating frequencies of two-dimensional harmonics in the presence of zero-mean multiplicative and additive noise. The proposed method is based on a special structured data matrix, the singular vectors of the matrix and a special basis of the NS which is generated by some of these vectors. It is observed that the estimators are consistent and work quite well in terms of biases and mean square errors. It is also observed that the method can be used to estimate accurately the frequencies of the evanescent component of texture.

A Computationally Efficient Iterative Algorithm for Estimating the Parameter of Chirp Signal Model

The parameter estimation of Chirp signal model in additive noises when all the noises are independently and identically distributed (i.i.d.) has been considered. A novel iterative algorithm is proposed to estimate the frequency rate of the considered model by constructing the iterative statistics with one-lag and multilag differential signals. It is observed that the estimator for the iterative algorithm is consistent and works quite well in terms of biases and mean squared errors. Moreover, the convergence rate of the estimator is improved from of the initial estimator to for one-lag differential signal condition and from of the initial estimator to for multilag differential signal condition, respectively, by only three iterations. The range of the lag is discussed and the optimal lag is obtained for the multilag differential signal condition when the lag is of order . The estimator of frequency rate with optimal lag is very close to Cramer-Rao lower bound (CRLB) as well as the asymptotic variance of least-squares estimator (LSE) at moderate signal-to-noise ratio (SNR). Finally, simulation experiments are performed to verify the effectiveness of the algorithm.

Statistical Analysis of Parameter Estimation for 2-D Harmonics in Multiplicative and Additive Noise

This article considers the problem of parameter estimation for two dimensional (2-D) multi-component harmonics in non zero-mean multiplicative and additive noise. The least squares estimators (LSEs) are proposed to estimate the coherent model parameters, and some statistical results of the LSEs are obtained, including strong consistency, strong convergence rate, and asymptotic normality. Furthermore, the LSEs-based estimators are proposed to estimate the noncoherent model parameters, and the strong consistency and the asymptotic normality are also proved. Finally, some numerical experiments are performed to see how the asymptotic results work for finite sample sizes.

An efficient algorithm for estimating the parameters of superimposed exponential signals in multiplicative and additive noise

This paper considers parameter estimation of superimposed exponential signals in multiplicative and additive noise which are all independent and identically distributed. A modified Newton-Raphson algorithm is used to estimate the frequencies of the considered model, which is further used to estimate other linear parameters. It is proved that the modified Newton- Raphson algorithm is robust and the corresponding estimators of frequencies attain the same convergence rate with Least Squares Estimators (LSEs) under the same noise conditions, but it outperforms LSEs in terms of the mean squared errors. Finally, the effectiveness of the algorithm is verified by some numerical experiments.