Iterative Interpolation for Parameter Estimation of LFM Signal Based on Fractional Fourier Transform

This study introduces two iterative interpolation algorithms for the parameter estimation of linear frequency modulation (LFM) signal using fractional Fourier transform (FrFT). The estimated parameter of an LFM signal can be obtained by locating the peak of the periodogram in the FrFT domain. Two interpolation algorithms were proposed to improve the accuracy of parameter estimation by employing the FrFT coefficients relative to the true parameters and applying interpolation algorithms iteratively to refine the parameter estimation approach. The proposed algorithms can utilize more information from FrFT results, thereby achieving improvements in either accuracy or efficiency. Moreover, the simulation results revealed the validity and advantage of the proposed approach.     

大线性调频斜率的估计方法研究

离散多项式相位变换分析法是线性频率调制(LFM)信号参数估计方法中的常用方法,但其缺点是参数估计范围较小,不利于工程实现。文中以线性迭代预测(ILP)方法为基础,介绍了ILP方法的基本原理,提出了一种LFM信号参数估计范围的改进方法,给出了LFM信号参数估计范围改进的基本思想和实现步骤,该方法在保证估计精度的前提下能大大提高估计范围。最后仿真验证了该方法的有效性。     

Pilot-aided data detection in DS-CDMA communication systems

The paper deals with the performance evaluation of a pilot-aided channel parameter estimation method suitable for applications in direct-sequence code-division multiple-access (DS-CDMA) communication systems. Differently from previous approaches, a suitable selective interference cancellation algorithm is used in order to reduce multipath interference on the channel parameter estimation and the detection of the information-bearing signals due to pilot signal replicas. Simulation results demonstrate the good channel parameter estimation capability of the proposed method in the case of uplink communications and different types of receiving schemes. In order to highlight the good behavior of the proposed approach, performance comparisons are shown with the ideal case of perfect knowledge of the channel parameter values, as well as with the classical implementation of the considered channel parameter estimation algorithm.     

Noise Estimation in Magnitude MR Datasets

Estimating the noise parameter in magnitude magnetic resonance (MR) images is important in a wide range of applications. We propose an automatic noise estimation method that does not rely on a substantial proportion of voxels being from the background. Specifically, we model the magnitude of the observed signal as a mixture of Rice distributions with common noise parameter. The expectation-maximization (EM) algorithm is used to estimate all parameters, including the common noise parameter. The algorithm needs initializing values for which we provide some strategies that work well. The number of components in the mixture model also needs to be estimated en route to noise estimation and we provide a novel approach to doing so. Our methodology performs very well on a range of simulation experiments and physical phantom data. Finally, the methodology is demonstrated on four clinical datasets.     

A Decoupled Approach for Near-Field Source Localization Using a Single Acoustic Vector Sensor

This paper considers the problem of three-dimensional (3-D, azimuth, elevation, and range) localization of a single source in the near-field using a single acoustic vector sensor (AVS). The existing multiple signal classification (MUSIC) or maximum likelihood estimation (MLE) methods, which require a 3-D search over the location parameter space, are computationally very expensive. A computationally simple method previously developed by Wu and Wong (IEEE Trans. Aerosp. Electron. Syst. 48(1):159–169, 2012), which we refer to as Eigen-value decomposition and Received Signal strength Indicator-based method (Eigen-RSSI), was able to estimate 3-D location parameters of a single source efficiently. However, it can only be applied to an extended AVS which consists of a pressure sensor separated from the velocity sensors by a certain distance. In this paper, we propose a uni-AVS MUSIC (U-MUSIC) approach for 3-D location parameter estimation based on a compact AVS structure. We decouple the 3-D localization problem into step-by-step estimation of azimuth, elevation, and range and derive closed-form solutions for these parameter estimates by which a complex 3-D search for the parameters can be avoided. We show that the proposed approach outperforms the existing Eigen-RSSI method when the sensor system is required to be mounted in a confined space.     

Estimation of fractional Brownian motion embedded in a noisyenvironment using nonorthogonal wavelets

We show that nonorthogonal wavelets can characterize the fractional Brownian motion (fBm) that is in white noise. We demonstrate the point that discriminating the parameter of fBm from that of noise is equivalent to discriminating the composite singularity formed by superimposing a peak singularity on a Dirac singularity. We characterize the composite singularity by formalizing this problem as a nonlinear optimization problem. This yields our parameter estimation algorithm. For fractal signal estimation, Wiener filtering is explicitly formulated as a function of the signal and noise parameters and the wavelets. We show that the estimated signal is a 1/f process. Comparative studies through numerical simulations of our methods with those of Wornell and Oppenheim (1992) are presented     

基于极化电磁波的飞行器姿态角估计新方法

提出使用单矢量传感器进行飞行器姿态角估计的新方法,安装在飞行器平台的单电磁 矢量传感器接收来自基站的极化电磁波信号,用MUSIC算法进行信号DOA和极化参数估计,得 到机体坐标系到地理坐标系的转换矩阵即飞行器姿态矩阵,最终估计飞行器姿态角。Matlab 仿真验证了该方法的有效性,姿态角估计误差在15°内,满足飞行器控制的要求。     

Asymptotic performance of second-order algorithms

This paper re-examines the asymptotic performance analysis of second-order methods for parameter estimation in a general context. It provides a unifying framework to investigate the asymptotic performance of second-order methods under the stochastic model assumption in which both the waveforms and noise signals are possibly temporally correlated, possibly non-Gaussian, real, or complex (possibly noncircular) random processes. Thanks to a functional approach and a matrix-valued reformulated central limit theorem about the sample covariance matrix, the conditions under which the asymptotic covariance of a parameter estimator are dependent or independent of the distribution of the signal involved are specified. Finally, we demonstrate the application of our general results to direction of arrival (DOA) estimation, identification of finite impulse response models, sinusoidal frequency estimation for mixed spectra time series, and frequency estimation of sinusoidal signal with very lowpass envelope     

Minimax MSE-ratio estimation with signal covariance uncertainties

In continuation to an earlier work, we further consider the problem of robust estimation of a random vector (or signal), with an uncertain covariance matrix, that is observed through a known linear transformation and corrupted by additive noise with a known covariance matrix. While, in the earlier work, we developed and proposed a competitive minimax approach of minimizing the worst-case mean-squared error (MSE) difference regret criterion, here, we study, in the same spirit, the minimum worst-case MSE ratio regret criterion, namely, the worst-case ratio (rather than difference) between the MSE attainable using a linear estimator, ignorant of the exact signal covariance, and the minimum MSE (MMSE) attainable by optimum linear estimation with a known signal covariance. We present the optimal linear estimator, under this criterion, in two ways: The first is as a solution to a certain semidefinite programming (SDP) problem, and the second is as an expression that is of closed form up to a single parameter whose value can be found by a simple line search procedure. We then show that the linear minimax ratio regret estimator can also be interpreted as the MMSE estimator that minimizes the MSE for a certain choice of signal covariance that depends on the uncertainty region. We demonstrate that in applications, the proposed minimax MSE ratio regret approach may outperform the well-known minimax MSE approach, the minimax MSE difference regret approach, and the "plug-in" approach, where in the latter, one uses the MMSE estimator with an estimated covariance matrix replacing the true unknown covariance.     

Parameter Estimation Approach of FSK/PSK Radar Signal

A parameter estimation approach of reconnaissance hybrid radar signal combined frequency-shift keying(FSK) and phase-shift keying(PSK) is presented.Firstly,the multi-phase difference is adopted to calculate the instantaneous frequency(IF) of FSK/PSK,then the frequency points of FSK are estimated from the histogram of IF.The code rate of PSK is extracted from the locations of phase discontinuity.Finally,the multi-phase difference of the square of the received signal is computed to estimate the code rate of FSK.The presented algorithm has higher accuracy of parameter estimation when the signal-to-noise ratio(SNR) is above 11 dB.     

Nonlinear filtering methods for harmonic retrieval and model orderselection in Gaussian and non-Gaussian noise

This paper addresses the problem of high-resolution parameter estimation (harmonic retrieval) and model-order selection for superimposed sinusoids. The harmonic retrieval problem is analyzed using a nonlinear parameter estimation approach. Estimation is performed using several nonlinear estimators with signals embedded in white and colored Gaussian noise. Simulation results demonstrate that the nonlinear filters perform close to the Cramer-Rao bound. Model order selection is performed in Gaussian and non-Gaussian noise. The problem is formulated using a multiple hypothesis testing approach with assumed known a priori probabilities for each hypothesis. Parameter estimation is performed using the extended Kalman filter when the noise is Gaussian. The extended high-order filter (EHOF) is implemented in non-Gaussian noise. Simulation results demonstrate excellent performance in selecting the correct model order and estimating the signal parameters     

多相编码雷达信号参数快速估计方法

针对Wigner-Ville分布、Radon-Wigner变换估计多相编码雷达信号参数存在运算量大、估计精度低等问题,本文提出了基于Radon-ambiguity变换和分数阶傅里叶变换(FRFT)联合分析的参数快速估计方法。该方法采用Radon-ambiguity变换估计信号调制斜率,采用分数阶傅里叶变换估计载频、周期等,通过两次一维搜索峰值来估计信号的参数,与Wigner变换、Radon-Wigner变换的二维搜索峰值相比,运算量大大降低,且提高了参数估计精度。仿真结果证明了该方法的有效性。     

Estimating signal parameters using the nonlinear instantaneousleast squares approach

A novel method for signal parameter estimation is presented, termed the nonlinear instantaneous least squares (NILS) estimator. The basic idea is to use the observations in a sliding window to compute an instantaneous (short-term) estimate of the amplitude used in the separated nonlinear least squares (NLLS) criterion. The effect is a significant improvement of the numerical properties in the criterion function, which becomes well-suited for a signal parameter search. For small-sized sliding windows, the global minimum in the NLIS criterion function is wide and becomes easy to find. For maximum size windows, the NILS is equivalent to the NLLS estimator, which implies statistical efficiency for Gaussian noise. A “blind” signal parameter search algorithm that does not use any a priori information is proposed. The NILS estimator can be interpreted as a signal-subspace projection-based algorithm. Moreover, the NILS estimator can be interpreted as an estimator based on the prediction error of a (structured) linear predictor. Hereby, a link is established between NLLS, signal-subspace fitting, and linear prediction-based estimation approaches. The NILS approach is primarily applicable to deterministic signal models. Specifically, polynomial-phase signals are studied, and the NILS approach is evaluated and compared with other approaches. Simulations show that the signal-to-noise ratio (SNR) threshold is significantly lower than that of the other methods, and it is confirmed that the estimates are statistically efficient. Just as the NLLS approach, the NILS estimator can be applied to nonuniformly sampled data     

A parameter estimation method for time‐frequency‐overlapped frequency hopping signals based on sparse linear regression and quadratic envelope optimization

The frequency hopping (FH) signals have well‐documented merits for commercial and military fields due to near‐far resistance and robustness to jamming. Therefore, the parameter estimation of FH signals is an important task for subsequent information acquisition and autonomous electronic countermeasure or attack. However, under the complex electromagnetic environment, there always exist overlaps in the time‐frequency domain among multiple signals, which bring poor signal sparsity and make the estimation more challenging. In this paper, a novel parameter estimation approach is developed for the time‐frequency‐overlapped FH signals under single‐channel reception. The exact solution is mainly composed of the sparse linear regression‐based matrix optimization (SLR‐MO) and quadratic envelope optimization (QEO). SLR‐MO highlights the removal of noise and distortion features for improving the overall sparsity and time‐frequency resolution. QEO further eliminates parts of the interfering signal features and outliers and then extracts and optimizes the average time‐frequency ridge to complete the parameter estimation (hopping instants, period, and carriers). Simulation results demonstrate that the developed estimator outperforms the traditional methods in the scope of application, estimation accuracy, and the robustness under low signal‐to‐noise ratio (SNR).     

The stability of nonlinear least squares problems and the Cramer-Rao bound

A number of problems of interest in signal processing can be reduced to nonlinear parameter estimation problems. The traditional approach to studying the stability of these estimation problems is to demonstrate finiteness of the Cramer-Rao bound (CRB) for a given noise distribution. We review an alternate, deterministic notion of stability for the associated nonlinear least squares (NLS) problem from the realm of nonlinear programming (i.e., that the global minimizer of the least squares problem exists and varies smoothly with the noise). Furthermore, we show that under mild conditions, identifiability of the parameters along with a finite CRB for the case of Gaussian noise is equivalent to the deterministic stability of the NLS problem. Finally, we demonstrate the application of our result, which is general, to the problems of multichannel blind deconvolution and sinusoid retrieval to generate new stability results for these problems with little additional effort.     

Reduced-Dimensional Polynomial Rooting-Based Multiple Parameter Estimation for Polarization Sensitive Coprime Arrays: A Full Array Extraction Perspective

Generally, multi-dimensional spectral peak search (SPS) in parameter estimation for polarization sensitive coprime linear arrays (PS-CLAs) requires heavy computational burden. To resolve this problem, we propose a search-free algorithm for multi-parameter estimation with PS-CLAs in this paper. Specifically, different from the decomposition algorithms, we first reconstruct the total received signal of PS-CLA as the signal extracted from a large uniform linear array, which enables to offer a spectrum function only with regard to direction of arrival (DOA) by utilizing rank reduction estimator. Subsequently, we employ the polynomial root finding technique instead of one-dimensional SPS to directly calculate the DOA estimates. Furthermore, a quadratic optimization problem is established for the polarization parameters and in particular, the closed-form solutions are provided by utilizing Lagrange multiplier approach. Finally, numerical simulations illustrate that the proposed search-free algorithm can obtain improved estimation accuracy with remarkably low complexity.

     

Nonlinear autoregressive and nonlinear autoregressive moving average model parameter estimation by minimizing hypersurface distance

The least squares (LS) can be used for nonlinear autoregressive (NAR) and nonlinear autoregressive moving average (NARMA) parameter estimation. However, for nonlinear cases, the LS results in biased parameter estimation due to its assumption that the independent variables are noise free. The total least squares (TLS) is another method that can used for nonlinear parameter estimation to increase the accuracy of the LS because it specifically accounts for the fact that the independent variables are noise corrupted. TLS has its own limitations, however, mainly because it is difficult for the method to isolate noise from the signal components. We present a new method that is based on minimization of hypersurface distance for accurate parameter estimation for NAR and NARMA models. Computer simulation examples show that the new method results in far more accurate NAR and NARMA model parameter estimates than do either the LS and TLS, with noise that is either white or colored, and retains its high accuracy even when the signal-to-noise ratio (SNR) is as low as 10 dB.     

A low complexity pilot aided channel estimation method for WCDMA communication systems

This letter deals with the performance evaluation of a pilot aided method for estimating channel parameters in wideband code division multiple access (WCDMA) communication systems. Unlike previous approaches, a successive interference cancellation algorithm is used to reduce multipath and multiuser interference caused by pilot signal replicas on the channel parameter estimation, and on the detection of the information bearing signals. Comparisons with the ideal case of perfect knowledge of the channel parameters and the classical channel parameter estimation algorithm have highlighted the good performance of the proposed approach.     

多分量线性调频信号检测方法

现代雷达体制下,线形调频(LFM)信号是一种常用的脉冲压缩雷达信号。为了精确获取多分量线性调频信号中分量的数量,引入聚类方法对LFM信号的Radon-时频分析结果进行聚类分析,同时完成多个分量的检测；为了减少聚类分析的输入数据集和提高计算效率,对Radon-时频分析结果进行了近似零均值处理,并分析了不同信噪比情况下的处理结果。仿真和试验结果表明：在较低信噪比条件下,这种方法可有效地检测多分量LFM信号中分量数和进行参数估计。     

Estimation of fuzzy Gaussian mixture and unsupervised statisticalimage segmentation

This paper addresses the estimation of fuzzy Gaussian distribution mixture with applications to unsupervised statistical fuzzy image segmentation. In a general way, the fuzzy approach enriches the current statistical models by adding a fuzzy class, which has several interpretations in signal processing. One such interpretation in image segmentation is the simultaneous appearance of several thematic classes on the same site. We introduce a new procedure for estimating of fuzzy mixtures, which is an adaptation of the iterative conditional estimation (ICE) algorithm to the fuzzy framework, We first describe the blind estimation, i.e., without taking into account any spatial information, valid in any context of independent noisy observations. Then we introduce, in a manner analogous to classical hard segmentation, the spatial information by two different approaches: contextual segmentation and adaptive blind segmentation. In the first case, the spatial information is taken into account at the segmentation step level, and in the second case it is taken into account at the parameter estimation step level. The results obtained with the iterative conditional estimation algorithm are compared to those obtained with expectation-maximization (EM) and the stochastic EM algorithms, on both parameter estimation and unsupervised segmentation levels, via simulations. The methods proposed appear as complementary to the fuzzy C-means algorithms.