This paper addresses the problem of voice activity detection (VAD) in noisy environments. The VAD method proposed in this paper is based on a statistical model approach, and estimates statistical models sequentially without a priori knowledge of noise. Namely, the proposed method constructs a clean speech / silence state transition model beforehand, and sequentially adapts the model to the noisy environment by using a switching Kalman filter when a signal is observed. In this paper, we carried out two evaluations. In the first, we observed that the proposed method significantly outperforms conventional methods as regards voice activity detection accuracy in simulated noise environments. Second, we evaluated the proposed method on a VAD evaluation framework, CENSREC-1-C. The evaluation results revealed that the proposed method significantly outperforms the baseline results of CENSREC-1-C as regards VAD accuracy in real environments. In addition, we confirmed that the proposed method helps to improve the accuracy of concatenated speech recognition in real environments.

We propose a noise suppression algorithm with the Kalman filter theory. The algorithm aims to achieve robust noise suppression for the additive white and colored disturbance from the canonical state space models with (i) a state equation composed of the speech signal and (ii) an observation equation composed of the speech signal and additive noise. The remarkable features of the proposed algorithm are (1) applied to adaptive white and colored noises where the additive colored noise uses babble noise, (2) realization of high performance noise suppression without sacrificing high quality of the speech signal despite simple noise suppression using only the Kalman filter algorithm, while many conventional methods based on the Kalman filter theory usually perform the noise suppression using the parameter estimation algorithm of AR (auto-regressive) system and the Kalman filter algorithm. We show the effectiveness of the proposed method, which utilizes the Kalman filter theory for the proposed canonical state space model with the colored driving source, using numerical results and subjective evaluation results.

This paper presents a study to estimate the composition of an electric load, i.e. to determine the amount of each load class by the direct measurements of the total electric current waveform from instrument reading. Kalman filter algorithm is applied to estimate the electric load composition on a consumer side of a distributed power system. The electric load supplied from the different voltage level by using a non-ideal delta-wye transformer is also studied with consideration of the practical environment for a distributed power system.

In this paper, we address the issue of mobile positioning and tracking after measurements have been made on the distances and possibly directions between an MS (mobile station) and its nearby base stations (BS's). The measurements can come from the time of arrival (TOA), the time sum of arrival (TSOA), the time difference of arrival (TDOA), and the angle of arrival (AOA). They are in general corrupted with measurement noise and NLOS (non-line-of-sight) error. The NLOS error is the dominant factor that degrades the accuracy of mobile positioning. Assuming specific statistic models for the NLOS error, however, we propose a scheme that significantly reduces its effect. Regardless of which of the first three measurement types (i.e. TOA, TSOA, or TDOA) is used, the proposed scheme computes the MS location in a mathematically unified way. We also propose a method to identify the TOA measurements that are not or only slightly corrupted with NLOS errors. We call them nearly NLOS-error-free TOA measurements. From the signals associated with TOA measurements, AOA information can be obtained and used to aid the MS positioning. Finally, by combining the proposed MS positioning method with Kalman filtering, we propose a scheme to track the movement of the MS.

The extened Kalman filter (EKF) and unscented Kalman filter (UKF) have been successively applied in particle filter framework to generate proposal distributions, and shown significantly improving performance of the generic particle filter that uses transition prior, i.e., the system state transition prior distribution, as the proposal distribution. In this paper we propose to use the Gauss-Newton EKF/UKF to replace EKF/UKF for generating proposal distribution in a particle filter. The Gauss-Newton EKF/UKF that uses iterated measurement update can approximate the optimal proposal distribution more closer than EKF/UKF, especially in the case of significant nonlinearity in the measurement function. As a result, the Gauss-Newton EKF/UKF is able to generate and propagate the proposal distribution for each particle much better than EKF/UKF, thus further improving the performance of state estimation. Simulation results for a nonlinear/non-Gaussian time-series demonstrate the superior estimation accuracy of our method compared with state-of-the-art filters.

A nonlinear harmonic estimator (NHE) is proposed for extracting a harmonic signal and its fundamental frequency in the presence of white noise. This estimator is derived by applying an extended complex Kalman filter (ECKF) to a multiple sinusoidal model with state-representation and then efficiently specializing it for the case of harmonic estimation. The effectiveness of the NHE is verified using computer simulations.

One of the system greatly affecting the performance of a database server is the size-division of buffer pools. This letter proposes an adaptive control method of the buffer pool sizes. This method obtains the nearly optimal division using only observed response times in a comparatively short duration.

A new receiver structure that combines the constant modulus algorithm (CMA) and the Kalman filter (KF) is investigated to exploit the advantages of both algorithms; simple implementation of blind algorithms, and excellent tracking ability, respectively. The proposed scheme achieves faster convergence and adaptability to the channel variation, which is verified through comparative simulations in doubly-selective (time- and frequency-selective) fading channels.

A new Kalman carrier synchronization algorithm is developed for high-order QAM transmission to reduce complexity compared to the conventional Kalman approach. The state model in the proposed algorithm employs only phase, instead of both phase and frequency, as in the conventional method. A reduced-observation model is also introduced to eliminate matrix operations in the Kalman recursions. Simulations results show that the one-state Kalman algorithm has better performance and lower complexity than the two-state Kalman algorithm. The cable modem downstream system is applied to demonstrate the effectiveness of the proposed algorithm.

Movement of the thighs is an important factor for studying gait cycle. In this paper, a hybrid hidden Markov model (HMM)/Kalman filter (KF) scheme is proposed to track the hip angle during gait cycles. Within such a framework, HMM and KF work in parallel to estimate the hip angle and detect major gait events. This approach has been applied to study gait features of different subjects and compared with video based approach. Experimental results indicate that 1.) the swing angle of the hip can be detected with simple hardware configuration using biaxial accelerometers and 2.) the hip angle can be tracked for different subjects within the error range of -5°+5°.

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).

In our previous work, the hyper H∞ filter is developed for tracking of unknown time-varying systems. Additionally, a fast algorithm, called the fast H∞ filter, of the hyper H∞ filter is derived on condition that the observation matrix has a shifting property. This algorithm has a computational complexity of O(N) where N is the dimension of the state vector. However, there still remains a possibility of deriving alternative forms of the hyper H∞ filter. In this work, a fast J-unitary form of the hyper H∞ filter is derived, providing a new H∞ fast algorithm, called the J-fast H∞ filter. The J-fast H∞ filter possesses a computational complexity of O(N), and the resulting algorithm is very amenable to parallel processing. The validity and performance of the derived algorithm are confirmed by computer simulations.

We present a new speech enhancement algorithm in a car environment with two microphones. The car audio signals and other background noises are the target noises to be suppressed. Our algorithm is composed of two main parts, i.e., the spatial and the temporal processes. The multi-channel blind deconvolution (MBD) is applied to the spatial process while the Kalman filter with a second-order high pass filter, for the temporal one. For the fast convergence, the MBD is newly expressed in frequency-domain with a normalization matrix. The final performance evaluated with the severely car noise corrupted speech shows that our algorithm produces noticeably enhanced speech.

This paper considers the problem of recursive filtering for linear discrete-time systems with uncertain observation. A new approximate adaptive filter with a parallel structure is herein proposed. It is based on the optimal mean square combination of arbitrary number of correlated estimates which is also derived. The equation for error covariance characterizing the mean-square accuracy of the new filter is derived. In consequence of parallel structure of the filtering equations the parallel computers can be used for their design. It is shown that this filter is very effective for multisensor systems containing different types of sensors. A practical implementation issue to consider this filter is also addressed. Example demonstrates the accuracy and efficiency of the proposed filter.

High speed core networks with optical fibers have spread widely, but it is still difficult to access the core networks from many rural areas. Synchronous CDMA systems with GEO satellite links are attractive to solve this problem, since they have wide service areas and are suitable for packet-based networks due to their statistically multiplexing effects. Additionally, the synchronous CDMA systems have more effective frequency utilization and power efficiency than asynchronous ones. In the synchronous CDMA systems, transmitted signals from fixed earth stations are required to achieve synchronization with each other. The broadband systems require extremely precise timing control as their bit rates increase. In this paper, we propose a synchronization method for a synchronous CDMA communication system using a GEO satellite and verify the feasibility of Gigachip rate synchronous CDMA systems.

A new robust extended Kalman filter is proposed for the discrete-time nonlinear systems with norm-bounded parameter uncertainties. After linearization of the nonlinear systems, the uncertainties described by the energy bounded constraint can be converted into an indefinite quadratic cost function to be minimized. The solution to the minimization problem is given by the extended Kalman filter derived in a Krein space, which leads to a robust version of the extended Kalman filter. Since the resulting robust filter has the same structure as a standard extended Kalman filter, the proposed filter can be readily designed by simply including the uncertainty terms in its formulas. The results of simulations are presented to demonstrate that the proposed filter achieves the robustness against parameter variation and performs better than the standard extended Kalman filter.

For radar tracking, the α-β filter and the Kalman filter, both of which do not require large computational requirements, have been widely utilized. However these filters cannot track a maneuvering target accurately. In recent years, the IMM (Interactive Multiple Model) algorithm has been proposed. The IMM is expected to reduce tracking errors for both non-maneuvering and maneuvering target. However, the IMM requires heavy computational burden, because it utilizes multiple Kalman filters in parallel. On the other hand, the α-β filter with an acceleration term which can estimate maneuver acceleration from the past target estimated positions using the kinetic model, has been proposed. This filter is not available for tracking targets under clutter environment, since it does not calculate the covariance matrix which is needed for gate setting. In this paper, we apply the acceleration estimate to the Kalman filter, and propose the hybrid Kalman filter with a constant-velocity filter and an acceleration estimation filter, and it integrates the outputs of two filters using the normalized distance of the prediction error of each filter. The computational requirement of the proposed filter is smaller than that of the IMM since the proposed filter consists of only two Kalman based filters. The proposed method can prevent deteriorating tracking accuracy by reducing the risk of maneuver misdetection when a target maneuvers. We evaluate the performance of the proposed filter by computer simulation, and show the effectiveness of the proposed filter, comparing with the conventional Kalman filter and the two-stage Kalman filter.

This paper focuses on a global ultrasonic system for self-localization of a mobile robot. The global ultrasonic system consists of some ultrasonic generators fixed at some arbitrary position in the global coordinates and two receivers in the moving coordinates of the mobile robot. This system is used to obtain the state vector of the mobile robot in the global coordinates from the distance measurement between the ultrasonic generator and the receiver. In order to avoid the cross-talk and to synchronize the ultrasonic sensors, the sequential cuing technique using small-sized radio frequency module is adopted. An extended Kalman filter algorithm is used to process the noisy ultrasonic signal and to estimate the state vector. Computer simulations and experiments are conducted to verify the effectiveness of the proposed global ultrasonic system.

In this paper, we propose a new multistage (iterative) structure where Kalman channel estimation and parallel interference cancellation multiuser detection are conducted in every stage (iteration). The proposed scheme avoids the complexity of the decorrelator in front of Kalman channel estimator, and has better performance than the previous scheme.

GPS receivers are susceptible to jamming by interference. This paper proposes a recurrent neural network (RNN) predictor for new application in GPS anti-jamming systems. Five types of narrowband jammers, i. e. AR process, continuous wave interference (CWI), multi-tone CWI, swept CWI, and pulsed CWI, are considered in order to emulate realistic conditions. As the observation noise of received signals is highly non-Gaussian, an RNN estimator with a nonlinear structure is employed to accurately predict the narrowband signals based on a real-time learning method. The node decoupled extended Kalman filter (NDEKF) algorithm is adopted to achieve better performance in terms of convergence rate and quality of solution while requiring less computation time and memory. We analyze the computational complexity and memory requirements of the NDEKF approach and compare them to the global extended Kalman filter (GEKF) training paradigm. Simulation results show that our proposed scheme achieves a superior performance to conventional linear/nonlinear predictors in terms of SNR improvement and mean squared prediction error (MSPE) while providing inherent protection against a broad class of interference environments.