For Doppler parameter estimation of forward-looking SAR, the third-order Doppler parameter can not be neglected. In this paper, the azimuth signal of the transmitter fixed bistatic forward-looking SAR is modeled as a cubic polynomial phase signal (CPPS) and multiple time-overlapped CPPSs, and the modified cubic phase function is presented to estimate the third-order Doppler parameter. By combining the cubic phase function (CPF) with Radon transform, the method can give an accurate estimation of the third-order Doppler parameter. Simulations validate the effectiveness of the algorithm.

A signal model and weighted-average based estimation techniques are proposed to estimate the angle-of-arrival (AOA) parameters of multiple clusters for a low data rate ultrawide band (LR-UWB) based wireless positioning system. The optimal AOA estimation techniques for the LR-UWB wireless positioning system according to the cluster condition are introduced and it is shown that the proposed techniques are superior to the conventional technique from the standpoint of performance.

A novel algorithm is presented for near-field source localization with a symmetric uniform linear array (ULA) consisting of an even number of sensors. Based on element reordering of a symmetric ULA, the steering vector is factorised with respect to the range-independent bearing parameters and range-relevant 2-D location parameters, which allows the range-independent bearing estimation with rank-reduction idea. With the estimated bearing, the range estimation for each source is then obtained by defining the 1-D MUSIC spectrum. Simulation results are presented to validate the performance of the proposed algorithm.

We describe a parameter estimation method for a target object in an area that sensors monitor. The parameters to be estimated are the perimeter length, size, and parameter determined by the interior angles of the target object. The estimation method does not use sensor location information, only the binary information on whether each sensor detects the target object. First, the sensing area of each sensor is assumed to be line-segment-shaped, which is a model of an infrared distance measurement sensor. Second, based on the analytical results of assuming line-segment-shaped sensing areas, we developed a unified equation that works with general sensing areas and general target-object shapes to estimate the parameters of the target objects. Numerical examples using computer simulation show that our method yields accurate results.

A hidden Markov model (HMM)-based parameter estimation scheme is proposed for wideband speech recovery. In each Markov state, the estimation efficiency is improved using a new mapping function derived from the weighted least squares of vector deviations. The experimental results reveal that the performance of the proposed scheme is superior to that combining the HMM and Gaussian mixture model (GMM).

This paper proposes a dynamic capture-recapture (DCR) model to estimate not only the total number of software faults but also quantitative software reliability from observed data. Compared to conventional static capture-recapture (SCR) model and usual software reliability models (SRMs) in the past literature, the DCR model can handle dynamic behavior of software fault-detection processes and can evaluate quantitative software reliability based on capture-recapture sampling of software fault data. This is regarded as a unified modeling framework of SCR and SRM with the Bayesian estimation. Simulation experiments under some plausible testing scenarios show that our models are superior to SCR and SRMs in terms of estimation accuracy.

This paper proposes joint maximum a posteriori (MAP) detection and spatial filtering for MIMO-OFDM mobile communications; it offers excellent receiver performance even over interference-limited channels. The proposed joint processor consists of a log likelihood generator and a MAP equalizer. The log likelihood generator suppresses cochannel interference by spatially filtering received signals and provides branch metrics of transmitted signal candidates. Using the branch metrics, the MAP equalizer generates log likelihood ratios of coded bits and performs channel decoding based on the MAP criterion. In the first stage, the log likelihood generator performs spatio-temporal filtering (STF) of the received signals prior to the fast Fourier transform (FFT) and is referred to as preFFT-type STF. Estimation of parameters including tap coefficients of the spatio-temporal filters and equivalent channel impulse responses of desired signals is based on the eigenvalue decomposition of an autocorrelation matrix of both the received and transmitted signals. For further improvement, in the second stage, the generator performs spatial filtering (SF) of the FFT output and is referred to as postFFT-type SF. Estimation of both tap coefficients of the spatial filters and channel impulse responses employs the recursive least squares (RLS) with smoothing. The reason for switching from preFFT-type STF into postFFT-type SF is that preFFT-type STF outperforms postFFT-type SF with a limited number of preamble symbols while postFFT-type SF outperforms preFFT-type STF when data symbols can be reliably detected and used for the parameter estimation. Note that there are two major differences between the proposed and conventional schemes: one is that the proposed scheme performs the two-stage processing of preFFT-type STF and postFFT-type SF, while the other is that the smoothing algorithm is applied to the parameter estimation of the proposed scheme. Computer simulations demonstrate that the proposed scheme can achieve excellent PER performance under interference-limited channel conditions and that it can outperform the conventional joint processing of preFFT-type STF and the MAP equalizer.

Image restoration based on Bayesian estimation in most previous studies has assumed that the noise accumulated in an image was independent for each pixel. However, when we take optical effects into account, it is reasonable to expect spatial correlation in the superimposed noise. In this paper, we discuss the restoration of images distorted by noise which is spatially correlated with translational symmetry in the realm of probabilistic processing. First, we assume that the original image can be produced by a Gaussian model based on only a nearest-neighbor effect and that the noise superimposed at each pixel is produced by a Gaussian model having spatial correlation characterized by translational symmetry. With this model, we can use Fourier transformation to calculate system characteristics such as the restoration error and also minimize the restoration error when the hyperparameters of the probabilistic model used in the restoration process coincides with those used in the formation process. We also discuss the characteristics of image restoration distorted by spatially correlated noise using a natural image. In addition, we estimate the hyperparameters using the maximum marginal likelihood and restore an image distorted by spatially correlated noise to evaluate this method of image restoration.

The purpose of this study is the real-time estimation of Doppler spectral moments for precipitation in the presence of ground clutter overlap. The proposed method is a frequency domain approach that uses a Gaussian model both to remove clutter spectrum and to estimate weather spectrum. The main advantage of this method is that it does not use processes like several fitting procedures and enables to estimate profiles of precipitation in a short processing time. Therefore this method is efficient for real-time radar observation with high range and time resolution. The performance of this method is evaluated based on simulation data and the observation data acquired by the Ku-band broad band radar (BBR) [1].

In this paper, a recursive Gauss-Newton (RGN) algorithm is first developed for adaptive tracking of the amplitude, frequency and phase of a real sinusoid signal in additive white noise. The derived algorithm is then simplified for computational complexity reduction as well as improved with the use of multiple forgetting factor (MFF) technique to provide a flexible way of keeping track of the parameters with different rates. The effectiveness of the simplified MFF-RGN scheme in sinusoidal parameter tracking is demonstrated via computer simulations.

Point Pattern Matching (PPM) is an essential problem in many image analysis and computer vision tasks. This paper presents a two-stage algorithm for PPM problem using ellipse fitting and dual Hilbert scans. In the first matching stage, transformation parameters are coarsely estimated by using four node points of ellipses which are fitted by Weighted Least Square Fitting (WLSF). Then, Hilbert scans are used in two aspects of the second matching stage: it is applied to the similarity measure and it is also used for search space reduction. The similarity measure named Hilbert Scanning Distance (HSD) can be computed fast by converting the 2-D coordinates of 2-D points into 1-D space information using Hilbert scan. On the other hand, the N-D search space can be converted to a 1-D search space sequence by N-D Hilbert Scan and an efficient search strategy is proposed on the 1-D search space sequence. In the experiments, we use both simulated point set data and real fingerprint images to evaluate the performance of our algorithm, and our algorithm gives satisfying results both in accuracy and efficiency.

This letter studies the tracking error in Multi-input Multi-output Feedback Error Learning (MIMO-FEL) system having insufficient excitation. It is shown that the error converges to zero exponentially even if the reference signal lacks the persistently excitation (PE) condition. Furthermore, by making full use of this fast convergence, we estimate the plant parameter while in operation based on frequency response. Simulation results show the effectiveness of the proposed method compared to a conventional approach.

Facial skin detection is an important step in facial surgical planning like as many other applications. There are many problems in facial skin detection. One of them is that the image features can be severely corrupted due to illumination, noise, and occlusion, where, shadows can cause numerous strong edges. Hence, in this paper, we present an automatic Expectation-Maximization (EM) algorithm for facial skin color segmentation that uses knowledge of chromatic space and varying illumination conditions to correct and segment frontal and lateral facial color images, simultaneously. The proposed EM algorithm leads to a method that allows for more robust and accurate segmentation of facial images. The initialization of the model parameters is very important in convergence of algorithm. For this purpose, we use a method for robust parameter estimation of Gaussian mixture components. Also, we use an additional class, which includes all pixels not modeled explicitly by Gaussian with small variance, by a uniform probability density, and amending the EM algorithm appropriately, in order to obtain significantly better results. Experimental results on facial color images show that accurate estimates of the Gaussian mixture parameters are computed. Also, other results on images presenting a wide range of variations in lighting conditions, demonstrate the efficiency of the proposed color skin segmentation algorithm compared to conventional EM algorithm.

This paper proposes a new parameter estimation method for the MIMO-OFDM MAP receiver with spatial-temporal filters. The proposed method employs eigenvalue decomposition (EVD) so as to attain precise estimates especially under interference-limited conditions in MIMO-OFDM mobile communications. Recursive EVD is introduced to reduce the computational complexity compared to the nonrecursive EVD. The spatial-temporal prewhitening is placed prior to FFT because this arrangement is superior to that of conventional prewhitening posterior to FFT in accuracy of the parameter estimation. In order to improve tracking capability to fast fading, the proposed scheme applies a decision-directed algorithm to the parameter estimation by using log-likelihood ratios of coded bits. Computer simulations demonstrate that the proposed scheme can track fast fading and reduce the complexity to 18 percents of the conventional one, and that the spatial-temporal filtering prior to FFT outperforms the conventional one posterior to FFT.

In this paper we propose a discrete program-size dependent software reliability growth model flexibly describing the software failure-occurrence phenomenon based on a discrete Weibull distribution. We also conduct model comparisons of our discrete SRGM with existing discrete SRGMs by using actual data sets. The program size is one of the important metrics of software complexity. It is known that flexible discrete software reliability growth modeling is difficult due to the mathematical manipulation under a conventional modeling-framework in which the time-dependent behavior of the cumulative number of detected faults is formulated by a difference equation. Our discrete SRGM is developed under an existing unified modeling-framework based on the concept of general order-statistics, and can incorporate the effect of the program size into software reliability assessment. Further, we discuss the method of parameter estimation, and derive software reliability assessment measures of our discrete SRGM. Finally, we show numerical examples of discrete software reliability analysis based on our discrete SRGM by using actual data.

In this paper, Integration of Fractional Gaussian Window transform (IFRGWT) is proposed for the parameter estimation of linear FM (LFM) signal; the proposal is based on the integration of the Fractional Fourier transform modified by Gaussian Window. The peak values can be detected by adjusting the standard deviation of Gaussian function and locating the optimal rotated angles. And also the parameters of the signal can be estimated well. As an application, detection and parameter estimation of multiple LFM signals are investigated in low signal-to-noise ratios (SNRs). The analytic results and simulations clearly demonstrate that the method is effective.

Consideration of manipulator dynamics and external disturbances in robot control system design can enhance the stability and performance properties of the whole system. In this paper, we present an approach to solve the control problem when the inertia parameters of robot are unknown, and at the same time robot is subjected to external force disturbances. This approach is based on simultaneous estimation of force signal and inertia parameters and utilizing them in the control law. The update laws and the control law are derived based on a single time-varying Lyapunov function, so that the global convergence of the tracking error is ensured. A theorem with a detailed proof is presented to guarantee the global uniform asymptotic stability of the whole system. Some simulations are made for a number of external forces to illustrate the effectiveness of the proposed approach.

Although the multiuser detection scheme based on Kalman filtering (K-MUD) proposed by Zhang and Wei, is referred to as a "blind" algorithm, in fact it is not really blind because it is conditioned on perfect knowledge of system parameter, power of the desired user. This paper derives an algorithm to estimate the power of the user of interest, and proposes a completely blind multiuser detection. Computer simulations show that the proposed parameter estimation scheme obtains excellent effect, and that the new detection scheme has nearly the same performance as the K-MUD, there is only slight degradation at very low input signal-to-interference ratios (SIR).

Estimating the parameters of a statistical distribution from measured sample values forms an essential part of many signal processing tasks. K-distribution has been proven to be an appropriate model for characterising the amplitude of sea clutter. In this paper, a new method for estimating the parameters of K-Distribution is proposed. The method greatly lowers the computational requirement and variance of parameter estimates when compared with the existing non-maximum likelihood methods.

Nearly 1/f processes are known as important stochastic models for scale invariant data analysis in a number of fields. In this paper, two parameter estimation methods of nearly 1/f processes based on wavelets are proposed. The conventional method based on wavelet transform with EM algorithm does not give the reliable parameter estimation value when the data length is short. Moreover, the precise parameter value is not estimated when the spectrum gap exists in 1/f processes. First, in order to improve the accuracy of the estimation when the data length is short, a parameter estimation method based on wavelet transform with complementary sampling is proposed. Next, in order to reduce the effect of spectrum gap, a parameter estimation method based on wavelet packet with EM algorithm is proposed. Simulation results are given to verify the effectiveness of the proposed methods.