In this paper, we discuss noise reduction approaches to improving range images using a nonlinear 2D Kalman filter. First, we propose the nonlinear 2D Kalman filter, which can reduce noise in the range image using an estimated edge vector and a nonlinear function that does not distort sharp edges. Second, we evaluate reduction of the additive noise in a test range image using the mean square error (MSE). Third, we discuss the detection rate and the number of false detections in the estimated range image. Fourth, a simulation example demonstrating the performance of the proposed 2D Kalman filter for a real range image having abrupt changes is presented. Finally, simulation results are presented which show that the estimated image of the nonlinear 2D Kalman filter is effective in reducing the amount of noise, while causing minimal smoothing of the abrupt changes.

Phase-based methods for estimating the frequency of a sinusoid have typically suffered from a threshold effect, where for signal to noise ratio (SNR) below the threshold, the mean squared error of the estimate rapidly increases. Furthermore, it is a significant problem that the threshold is considerably high and strongly depends on frequency. To overcome the difficulties, a Kalman-based sinusoidal estimator bank (KSEB) is proposed. In the derivation of the KSEB, a four-channel filter bank and decimation technique are effectively used. The computer simulation also demonstrates the superiority of the KSEB to the other frequency estimators.

A new speech enhancement technique is proposed assuming that a speech signal is represented in terms of a linear probabilistic process and that a noise signal is represented in terms of a stationary random process. Since the target signal, i.e., speech, cannot be represented by a stationary random process, a Wiener filter does not yield an optimum solution to this problem regarding the minimum mean variance. Instead, a Kalman filter may provide a suitable solution in this case. In the Kalman filter, a signal is represented as a sequence of varying state vectors, and the transition is dominated by transition matrices. Our proposal is to construct the state vectors as well as the transition matrices based on time-frequency pattern of signals calculated by a wavelet transformation (WT). Computer simulations verify that the proposed technique has a high potential to suppress noise signals.

This paper proposes an algorithm that adaptively estimates time-varying noise variance used in Kalman filtering for real-time speech signal enhancement. In the speech signal contaminated by white noise, the spectral components except dominant ones in high frequency band are expected to reflect the noise energy. Our approach is first to find the dominant energy bands over speech spectrum using LPC. We then calculate the average value of the actual spectral components over the high frequency region excluding the dominant energy bands and use it as the noise variance. The resulting noise variance estimate is then applied to Kalman filtering to suppress the background noise. Experimental results indicate that the proposed approach achieves a significant improvement in terms of speech enhancement over those of the conventional Kalman filtering that uses the average noise power over silence interval only. As a refinement of our results, we employ multiple-Kalman filtering with multiple noise models and improve the intelligibility.

Since for recognition tasks it is known that planar invariants are more easily obtained than others, decomposing a scene in terms of planar parts becomes very interresting. This paper presents a new approach to find the projections of planar surfaces in a pair of images. For this task we introduce the facet concept defined by linked edges (chains) and corners. We use collineations as projective information to match and verify their planarity. Our contribution consists in obtaining from an uncalibrated stereo pair of images a match of "planar" chains based on matched corners. Collineations are constrained by the fundamental matrix information and a Kalman filter approach is used to refine its computation.

Tracking many targets simultaneously using a search radar has been one of the major research areas in radar signal processing. The primary difficulty in this problem arises from the noise characteristics of the incoming data. Hence it is crucial to obtain an accurate association between targets and noisy measurements in multi-target tracking. We introduce a new scheme for optimal data association, based on a MAP approach, and thereby derive an efficient energy function. Unlike the previous approaches, the new constraints between targets and measurements can manage the cases of target missing and false alarm. Presently, most algorithms need heuristic adjustments of the parameters. Instead, this paper suggests a mechanism that determines the parameters in an automated manner. Experimental results, including PDA and NNF, show that the proposed method reduces position errors in crossing trajectories by 32.8% on the average compared to NNF.

A new algorithm is proposed for improving the convergence of backpropagtion networks. This algorithm is obtained by combining the conjugate gradient method and the Kalman filter algorithm. Simulation results show that the proposed algorithm can perform satisfactorily in all cases considered.

A nonlinear multiple complex sinusoidal estimator (NMSE) is proposed, as an extended and improved version with system noise of the single sinusoidal estimator previously presented by the author, for extracting multiple complex sinusoids in white noise. This estimator is derived by applying an extended complex Kalman filter (ECKF) to a noisy multiple complex sinusoidal model with state-representation, where the model becomes a nonlinear stochastic system. Proof of the stability is given by using a structure of the state-space signal model and Lyapunov techniques. Also, computer simulations demonstrate the effectiveness of the NMSE from various points of view.

In this paper, the Linear Exponential Quadratic Gaussian with Loop Transfer Recovery (LEQG/LTR) methodology is employed for the design of high performance induction motor servo systems. In addition, we design a speed sensorless induction motor vector controlled driver with both the extended Kalman filter and the LEQG/LTR algorithm. The experimental realization of an induction servo system is given. Compared with the traditional PI and LQG/LTR methods, it can be seen that the system output sensitivity for parameter variations and the rising time for larger command input of the proposed method can be significantly reduced.

Modeling error is the major concerning issue in the trajectory estimation. This paper formulates the dynamic model of a reentry vehicle in reentry phase for identification with an unmodeled acceleration input covering possible model errors. Moreover, this work presents a novel on-line estimation approach, adaptive filter, to identify the trajectory of a reentry vehicle from a single radar measured data. This proposed approach combines the extended Kalman filter and the recursive least-squares estimator of input with the hypothetical testing scheme. The recursive least-squares estimator is provided not only to extract the magnitude of the unmodeled input but to offer a testing criterion to detect the onset and presence of the input. Numerical simulation demonstrates the superior capabilities in accuracy and robustness of the proposed method. In real flight analysis, the adaptive filter also performs an excellent estimation and prediction performances. The recommended trajectory estimation method can support defense and tactical operations for anti-tactical ballistic missile warfare.

In this paper, we propose an idea for intramedia synchronization control using a method of end-to-end delay monitoring to estimate future delay in delay compensation protocol. The estimated value by Kalman filtering at the presentation site is used for feedback control to adjust the retrieval schedule at the source according to the network conditions. The proposed approach is applicable for the real time retrieving application where `tightness' of temporal synchronization is required. The retrieval schedule adjustment is achieved by two resynchronization mechanisms-retrieval offset adjustment and data unit skipping. The retrieval offset adjustment is performed along with a buffer level check in order to compensate for the change in delay jitter, while the data unit skipping control is performed to accelerate the recovery of unsynchronization period under severe conditions. Simulations are performed to verify the effectiveness of the proposed scheme. It is found that with a limited buffer size and tolerable latency in initial presentation, using a higher efficient delay estimator in our proposed resynchronization scheme, the synchronization performance can be improved particularly in the critically congested network condition. In the study, Kalman filtering is shown to perform better than the existing estimation methods using the previous measured jitter or the average value as an estimate.

A new method is proposed for estimating a single complex sinusoid and its parameters (frequency and amplitude) from measurements corrupted by white noise. This method is called the ECKF-SVD method, which is derived by applying an extended complex Kalman filter (ECKF) to a nonlinear stochastic system whose state variables consist of the AR coefficient (a function of frequency) and a sample of the original signal. Proof of the stability is given in the case of a single sinusoid. Simulations demonstrate that the proposed ECKF-SVD method is effective for estimating a single complex sinusoid and its frequency under a low signal-to-noise ratio (SNR). In addition, the amplitude estimation by means of the ECKF-SVD method is also discussed.

The asynchronous transfer mode (ATM) provides efficient switching capability for various kinds of communication services. To guarantee the minimum quality of services in the ATM networks, the bandwidth allocation setup procedure between the network nodes and users is very important. However, most of call admission control (CAC) methods which have been proposed so far are not fully appropriate to apply to real environments in terms of the complexity of the hardware implementation or the accuracy of assumptions about the cell-arrival processes. In addition, the success of broad bandwidth applications in the future multimedia environments will largely depend on the degree to which the efficiency in communication systems can be achieved, so that establishing high-speed CAC schemes in the ATM networks is an indispensable subject. This paper proposes a new cell-loss rate estimation method for the real time CAC in ATM networks. A neural network model using the Kalman filter algorithm was employed to improve the error minimizing process for the cell-loss estimation problem. In the process of optimizing the three-layer perceptron, the average, the variance, and the 3rd central moment of the number of cell arrivals were calculated, and cell-loss rate date based on the non-parametric method were adopted for outputs of the neural network. Evaluation results concerned with the convergence using the sum of square errors of outputs were also discussed in this paper. Using this algorithm, ATM cell-loss rates can be easily derived from the average and peak of cells rates coming from users. Results for the cell-loss estimation process suggest that the proposed method will be useful for high-speed ATM CAC in multimedia traffic environments.

This paper discusses the problems in designing virtual-path (VP) networks and underlying transmission-path (TP) networks using the "self-sizing" capability. Self-sizing implies an autonomous adjustment mechanism for VP bandwidths based on traffic conditions observed in real time. The notion of "bandwidth demand" has been introduced to overcome some of the problems with VP bandwidth sizing, e.g., complex traffic statistics and diverse quality of service requirements. Using the bandwidth demand concept, a VP-bandwidth-sizing procedure is proposed in which real-time estimates of VP bandwidth demand and successive VP bandwidth allocation are jointly utilized. Next, TP bandwidth demand, including extra capacity to cover single-link failures, is defined and used to measure the congestion level of the TP. Finally, a TP provisioning method is proposed that uses TP "lifetime" analysis.

This letter proposes a map-matching method for automotive navigation systems. The proposed method utilizes the innovation of the Kalman filter algorithm and can achieve more accurate positioning than the correlation method which is generally used for the navigation systems. In this letter, the performance of the proposed algorithm is verified by some simulations.

This paper considers the estimation of speech parameters and their enhancement using an approach based on the estimation-maximization (EM) algorithm, when only noisy speech data is available. The distribution of the excitation source for the speech signal is assumed as a mixture of two Gaussian probability distribution functions with differing variances. This mixture assumption is experimentally valid for removing the residual excitation signal. The assumption also is found to be effective in enhancing noise-corrupted speech. We adaptively estimate the speech parameters and analyze the characteristics of its excitation source in a sequential manner. In the maximum likelihood estimation scheme we utilize the EM algorithm, and employ a detection and an estimation step for the parameters. For speech enhancement we use Kalman filtering for the parameters obtained from the above estimation procedure. The estimation and maximization procedures are closely coupled. Simulation results using synthetic and real speech vindicate the improved performance of our algorithm in noisy situations, with an increase of about 3 dB in terms of output SNR compared to conventional Gaussian assumption. The proposed algorithm also may be noteworthy in that it needs no voiced/unvoiced decision logic, due to the use of the residual approach.

The technique for estimating the parameters of multiple waves provides a convenient tool for analysis of multiple wave-fields and eventually for actual applications to mobile communications. Several algorithms have been proposed for those purposes. However, the best tactics to resolve multiple wave-fields are still imperfectly understood at present. This paper proposes a new method for estimating the angles and power levels of arrival waves based on the extended Kalman filter. A space-variable model which we call a spatial state equation is derived using array element locations and incident angles. It has been shown that by means of the model, the estimation of incident waves can be transformed into the problem of parameter identification in linear system which can be carried out by the extended Kalman filter conveniently. The algorithm is initiated directly by the signal received at each array element. The detailed procedure of an extended Kalman filter approach is given in the paper. The performance of the proposed approach is examined by a simulation study with two signals model. The simulation results show a good estimate performance, even in the case that two waves arrive from close directions.

A novel adaptive pitch-synchronous analysis method is proposed to estimate simultaneously vocal tract (formant/antiformant) and voice source parameters from speech waveforms. We use the parametric Rosenberg-Klatt (RK) model to generate a glottal waveform and an autoregressive-exogenous (ARX) model to represent voiced speech production process. The Kalman filter algorithm is used to estimate the formant/antiformant parameters from the coefficient of the ARX model, and the simulated annealing method is employed as a nonlinear optimization approach to estimate the voice source parameters. The two approaches work together in a system identification procedure to find the best set of the parameters of both the models. The new method has been compared using synthetic speech with some other approaches in terms of accuracy of estimated parameter values and has been proved to be superior. We also show that the proposed method can estimate accurately the parameters from natural speech sounds. A major application of the analysis method lies in a concatenative formant synthesizer which allows us to make flexible control of voice quality of synthetic speech.

In a display system with a line-of-gaze (LOG) controller, it is difficult to make the directions and motions of a LOG-controlled object coincide as closely as possible in the display with the user's intended LOG-directions and motions. This is because LOG behavior is not only smooth, but also saccadic due to the problem of involuntary eye movement. This article introduces a flexible on-line LOG-control scheme to realize nearly perfect LOG operation. Using a mesh-wise cursor pattern, the first visual experiment elucidates subjectively that a Kalman Filter (KF) for smoothing and predicting is effective in filtering out macro-saccadic changes of the LOG and in predicting sudden changes of the saccade while movement is in progress. It must be assumed that the LOG trajectory can be described by a linear position-velocity-acceleration approximation of Sklansky Model (SM). Furthermore, the second experiment uses a four-point pattern and simulations to scrutinize the two physical properties of velocity and direction-changes of the LOG in order to quantitatively and efficiently resolve "moving" and "gazing". In order to greatly reduce the number of LOG-small-position changes while gazing, the proposed Gaze-Holding algorithm (GH) with a gaze-potential function is combined with the KF. This algorithm allows the occurrence frequency of the micro-saccade to be reduced from approximately 25 Hz to 1 or 2 Hz. This great reduction in the frequency of the LOG-controlled object moves is necessary to achieve the user's desired LOG-response while gazing. Almost perfect LOG control is accomplished by the on-line SM+KF+GH scheme while either gazing or moving. A menu-selection task was conducted to verify the effectiveness of the proposed on-line LOG-control method.