A novel approach for DOA estimation in unknown correlated noise fields

The key of the subspace-based Direction Of Arrival （DOA） estimation lies in the estimation of signal subspace with high quality. In the case of uncorrelated signals while the signals are temporally correlated, a novel approach for the estimation of DOA in unknown correlated noise fields is proposed in this paper. The approach is based on the biorthogonality between a matrix and its Moore-Penrose pseudo inverse, and made no assumption on the spatial covariance matrix of the noise. The approach exploits the structural information of a set of spatio-temporal correlation matrices, and it can give a robust and precise estimation of signal subspace, so a precise estimation of DOA is obtained. Its performances are confirmed by computer simulation results.     

Improved Parameter Estimation for Chirp Signals Based on Cyclostationarity

The problem of concern here is parameter estimation of chirp signals in the presence of multiplicative and additive noise. An improved cyclostationarity based algorithm is proposed to estimate the phase parameters. The novelty of the proposed algorithm lies in the iterative estimation of the second-order parameters. The main characteristics of the method include reduction in error propagation effect, increase in estimation accuracy, and operation over a wider range of phase parameter values. Simulation results demonstrate that the proposed algorithm has better performance than the conventional cyclostationary estimation method.     

Estimation of time-varying channels for pilot-assisted OFDM systems

Orthogonal frequency division multiplexing (OFDM) systems encounter performance degradations because of the time-varying (TV) channels common in wireless environments. The channel variations within one OFDM symbol introduce intercarrier interference. In this case, the frequency domain channel matrix is no longer diagonal, hence the corresponding channel estimation is challenging. In this article, two novel TV channel estimation approaches are proposed for the pilot-assisted OFDM systems, where the channel is approximated by the high-order linear model or the piece-wise linear model in time domain. The least square estimation is derived for the two kinds of channel approximations. The simulation is performed based on realistic TV channels with a fairly high Doppler spread. The results show the significant decreasing of the estimation mean square error using the proposed approaches.     

Estimation of doppler centroid frequency in spaceborne ScanSAR

Doppler centroid frequency is an essential parameter in the imaging processing of the Scanning mode Synthetic Aperture Radar （ScanSAR）. Inaccurate Doppler centroid frequency will result in ghost images in imaging result. In this letter, the principle and algorithms of Doppler centroid frequency estimation are introduced. Then the echo data of ScanSAR system is analyzed. Based on the algorithms of energy balancing and correlation Doppler estimator in the estimation of Doppler centroid frequency in strip mode SAR, an improved method for Doppler centroid frequency estimation in ScanSAR is proposed. The method has improved the accuracy of Doppler centroid frequency estimation in ScanSAR by zero padding between burst data. Finally, the proposed method is validated with the processing of ENVironment SATellite Advanced Synthetic Aperture Radar （ENVISAT ASAR） wide swath raw data.     

Over-sampling basis expansion model aided channel estimation for OFDM systems with ICI

The rapid variation of channel can induce the intercarrier interference in orthogonal frequency-division multiplexing （OFDM） systems. Intercarrier interference will significantly increase the difficulty of OFDM channel estimation because too many channel coefficients need be estimated. In this article, a novel channel estimator is proposed to resolve the above problem. This estimator consists of two parts： the channel parameter estimation unit （CPEU）, which is used to estimate the number of channel taps and the multipath time delays, and the channel coefficient estimation unit （CCEU）, which is used to estimate the channel coefficients by using the estimated channel parameters provided by CPEU. In CCEU, the over-sampling basis expansion model is resorted to solve the problem that a large number of channel coefficients need to be estimated. Finally, simulation results are given to scale the performance of the proposed scheme.     

JPEG stream soft-decoding technique based on autoregressive modeling

This paper introduces a new model-based soft decoding technique to restore the widely used joint photographic expert group(JPEG) streams.The image is modeled as a two dimensional(2D) piecewise stationary autoregressive process,and the decoding task is formulated as a constrained optimization problem.All the constraints are given by the quantization intervals which available at the decoder freely.The autoregressive model serves as an important regularization term of the objective function of the optimization,and the model parameters are solved on the decoded image locally using a weighted total least square method.In addition,a novel bilateral dualside weighting scheme is proposed to minimize the influence of the blocking artifact on the accuracy of parameter estimation.Extensive experimental results suggest that the proposed algorithm systematically improves the quality of JPEG images and also outperforms existing JPEG postprocessing algorithms in a wide bit-rate range both in terms of peak signal-to-noise ratio(PSNR) and subjective quality     

ADAPTIVE ALGORITHM FOR CONSISTENTMA PARAMETER ESTIMATION VIATHIRD ORDER CUMULANT

This paper addresses the problem of adaptive,consistent parameter estimation for a MA model from the 3rd order cumulant of the system output. The proposed adaptive algorithm is derived by using the new linear equation system (J. K. Tugnait, 1990), which is proved to have unique solution,and hence guarantees the consistence of the MA parameters. Simulation results are provided to show the performance of the new algorithm.     

A STATISTICAL NOISE MODEL AND MATHEMATICAL MODEL OF SPECKLE PATTERN AND ADAPTIVE FILTERING APPROACH

A statistical noise model and a mathematical model for real speckle pattern are presented in this paper, and then, in view of the models, a new adaptive suboptimal image filtering approach is proposed. The proposed approach, with the local direction features of speckle pattern, combines the characteristics of optimal linear filter with non-linear filter and is an adaptive approximation to linear minimum mean square error filter. Experimental results show that the proposed approach has fairly good edge-preserved performance, compared with other present image filters, as well as much better filtering performance and robustness for speckle pattern.     

Training sequence based channel estimation for indoor visible light communication system

Channel estimation is a key technology in indoor wireless visible light communications(VLCs).Using the training sequence(TS),this paper investigates the channel estimation in indoor wireless visible light communications.Based on the propagation and signal modulation characteristics of visible light,a link model for the indoor wireless visible light communications is established.Using the model,three channel estimation methods,i.e.,the correlation method,the least square(LS) method and the minimum mean square error(MMSE) method,are proposed.Moreover,the performances of the proposed three methods are evaluated by computer simulation.The results show that the performance of the correlation method is the worst,the LS method is suitable for higher signal to noise ratio(SNR),and the MMSE method obtains the best performance at the expense of highest complexity.     

Parametric channel modeling based OFDM channel estimation

A simplified parametric channel estimation approach was proposed for orthogonal frequency division multiplexing （OFDM） systems. Based on parametric channel model, this algorithm is composed of two parts： the estimation of channel parameters and channel interpolation. The exponentially embedded family （EEF） criterion is exploited to determine the number of channel paths as well as the multipath time delays. Consequently, the channel frequency responses is acquired via the estimated parameters. Additionally, the authors＇ scheme is computationally efficient owing to the needless of the eigenvalue decomposition or the estimation of signal parameters by the rotational invariance technique （ESPRIT）. Simulations are provided to validate the performance of this algorithm from perspectives of the probability of correct estimation and the mean square error （MSE）. It is demonstrated that this approach exhibits a superior performance over the existing algorithms.     

基于分数阶相关的模拟集成电路软故障诊断* Deng Yong(邓勇), Shi Yibing(师奕兵)and Zhang Wei(张伟)

针对模拟集成电路软故障诊断的难题,提出了基于分数阶相关的方法。首先,利用分数阶小波包将待测试电路(CUT)的Volterra级数进行分解,计算出分数阶相关函数。然后,用得到的分数阶相关函数构造出待测试电路的故障特征。通过对故障特征的比较,可以将待测试电路的各种软故障状态进行辨识并对故障实现定位。标准电路的仿真实验描述了这一方法并验证了该方法对模拟集成电路软故障诊断的有效性。     

Soft Fault Diagnosis in Analog Circuits Based on Bispectral Models

Aiming at the problem to locate soft faults in analog circuits, a new approach based on bispectral models is proposed. First, the Volterra kernels of the circuit under test (CUT) are calculated. Then, the Volterra kernels are used to construct bispectral models. By comparison with the fault features of the constructed models, soft faults of linear and weak nonlinear components in the analog circuit are identified and the faults are located. Simulations and experiments show the effectiveness of the proposed method in analog circuits.,     

A new DOA estimation approach using Volterra signal model

In this paper, we proposed a new direction-of-arrival estimation approach using Volterra signal model in spatial domain. The new technique basically uses additionally the second-order terms of Volterra series to produce augmented Volterra snapshots, the extension for higher-order case is straightforward. The resolution of the proposed method is high comparing with the standard multiple signal classification (MUSIC) algorithm and the 2q-MUSIC algorithm. Simulation results are presented to show the performance of the proposed technique.     

Wavelet-Based Transistor Parameter Estimation

In this paper a wavelet-based parameter estimation method has been proposed for the common emitter transistor amplifier circuit and compared with the least squares method. As the maximal precision of simulation requires the modeling of electronic circuits in terms of device parameters and circuit components, the Volterra model of the common emitter amplifier circuit derived using the Ebers–Moll model and perturbation technique has been used for parameter estimation. The advantage of the proposed method is a smaller data storage requirement and accurate parameter estimation as compared to the least squares method because the wavelet method is adapted to time-frequency resolution.     

Wavelet Based Transistor Parameter Estimation Using Second Order Volterra Model

In this paper, we present a wavelet based parameter estimation technique to estimate the transistor parameter in a common emitter amplifier circuit. The method uses the closed form expression of the second order Volterra model of a common emitter amplifier circuit derived using perturbation technique and the Ebers–Moll model. Simulations show that the proposed method gives more accurate parameter estimation with smaller data storage requirement as compared to the conventional least squares method because the wavelet method is adapted to time frequency resolution. Also, the parameter estimation using a nonlinear model gives an SNR improvement of approximately 2-dB compared to the parameter estimation using only a linear part of the model.     

Efficient algorithms for Volterra system identification

In this paper, nonlinear filtering and identification based on finite-support Volterra models are considered. The Volterra kernels are estimated via input-output statistics or directly in terms of input-output data. It is shown that the normal equations for a finite-support Volterra system excited by zero mean Gaussian input have a unique solution if, and only if, the power spectral process of the input signal is nonzero at least at m distinct frequencies, where m is the memory of the system. A multichannel embedding approach is introduced. A set of primary signals defined in terms of the input signal serve to map efficiently the nonlinear process to an equivalent multichannel format. Efficient algorithms for the estimation of the Volterra parameters are derived for batch, as well as for adaptive processing. An efficient order-recursive method is presented for the determination of the Volterra model structure. The proposed methods are illustrated by simulations     

Optimal Volterra kernel estimation algorithms for a nonlinearcommunication system for PSK and QAM inputs

A fifth-order Volterra kernel estimation algorithm, which is optimal in the least mean square error sense, for a bandpass nonlinear system is derived. The algorithm is based on some characteristics of i.i.d. circularly symmetric zero-mean complex-valued Gaussian random variables. The proposed algorithm can be used to identify a nonlinear system under uniformly i.i.d. rectangular M-QAM input and under uniformly i.i.d. M-PSK input (M⩾4) with modest modification. The same approach has been used to derive an optimal Volterra kernel estimation algorithm up to the third order. However, in some cases, a third-order model is not of “high enough order” to capture the nonlinear system characteristics. A simulation example is given to show the necessity of deriving a fifth-order Volterra kernel estimation algorithm and to test for the correctness of the algorithm     

BIST architecture for oscillation test of analog ICs and investigation of test hardware influence

In this article, a fully on-chip implementation of the oscillation-based test for analog and mixed-signal systems is presented. The proposed test hardware uses an on-chip reference oscillator to compensate the undesired impact of process parameter variations. Thus, this approach can be easily and effectively implemented also in nanoscale technologies. As the circuit under test (CUT), an operational amplifier designed in a 90 nm CMOS technology was used. The possible influence of additional test hardware on the CUT performance as well as the test reliability were investigated and analyzed. Finally, results obtained by this analysis are discussed.     

Modeling and Identification of Nonlinear Systems in the Short-Time Fourier Transform Domain

In this paper, we introduce a novel approach for improved nonlinear system identification in the short-time Fourier transform (STFT) domain. We first derive explicit representations of discrete-time Volterra filters in the STFT domain. Based on these representations, approximate nonlinear STFT models, which consist of parallel combinations of linear and nonlinear components, are developed. The linear components are represented by cross-band filters between subbands, while the nonlinear components are modeled by multiplicative cross-terms. We consider the identification of quadratically nonlinear systems and show that a significant reduction in computational cost as well as substantial improvement in estimation accuracy can be achieved over a time-domain Volterra model, particularly when long-memory systems are considered. Experimental results validate the theoretical derivations and demonstrate the effectiveness of the proposed approach.     

基于最大似然估计的机载双基地雷达距离模糊杂波抑制方法

针对存在距离模糊情况下机载双基地雷达杂波抑制的难题,本文分析了存在距离模糊时机载双基地雷达的杂波特性,提出了一种基于最大似然估计的机载双基地雷达距离模糊杂波抑制方法.该方法首先通过最大似然估计对训练样本所包含的各个模糊距离环的杂波幅度进行估计,然后通过非均匀采样重构待检测距离门及其模糊距离环的杂波数据,再由重构的杂波数...