The combination of GPS measurements with the dynamic model via a Kalman filter or an extended Kalman filter, also known as GPS based reduced dynamic orbit determination (RDOD) techniques, have been widely used for accurate and real time navigation of satellites in low earth orbit (LEO). In previous studies, the GPS measurement noise variance is empirically taken as a constant, which is not reasonable because of insufficient prior information or dynamic environment. An improper estimate of the measurement noise may lead to poor performance or even divergence of the filter. In this letter, an adaptive extended Kalman filter (AEKF)-based approach using GPS dual-frequency pseudo-range measurements is presented, where the GPS pseudo-range measurement noise variance is adaptively estimated by the Carrier to Noise Ratio (C/N0) from the tracking loop of GPS receiver. The simulation results show that the proposed AEKF approach can achieve apparent improvements of the position accuracy and almost brings no extra computational burdens for satellite borne processor.

In this paper a nonlinearity compensation algorithm based on the extended Kalman filter is proposed to improve the measurement accuracy of a heterodyne laser interferometer. The heterodyne laser interferometer is used for ultra-precision measurements such as those used in semiconductor manufacturing. However the periodical nonlinearity property caused by frequency-mixing restricts the accuracy of the nanometric measurements. In order to minimize the effect of the nonlinearity, the measurement process of the laser interferometer is modeled as a state equation and the extended Kalman filtering approach is applied to the process. The effectiveness of our proposed algorithm is demonstrated by comparing the results of the algorithm with experimental results for the laser system.

A novel method is proposed to track the position of MS in the mixed line-of-sight/non-line-of-sight (LOS/NLOS) conditions in cellular network. A first-order markov model is employed to describe the dynamic transition of LOS/NLOS conditions, which is hidden in the measurement data. This method firstly uses modified EKF banks to jointly estimate both mobile state (position and velocity) and the hidden sight state based on the the data collected by a single BS. A Bayesian data fusion algorithm is then applied to achieve a high estimation accuracy. Simulation results show that the location errors of the proposed method are all significantly smaller than that of the FCC requirement in different LOS/NLOS conditions. In addition, the method is robust in the parameter mismodeling test. Complexity experiments suggest that it supports real-time application. Moreover, this algorithm is flexible enough to support different types of measurement methods and asynchronous or synchronous observations data, which is especially suitable for the future cooperative location systems.

In this paper, a new methodology to estimate the number of competing stations in an IEEE 802.11 network, is proposed. Due to the nonlinear nature of the measurement model, an iterative nonlinear filtering algorithm, called the Scaled Unscented Filter (SUF), is employed. The SUF can provide a superior alternative to nonlinear filtering than the conventional Extended Kalman Filter (EKF), since it avoids errors associated with linearization. This approach demonstrates both high accuracy in addition to prompt reactivity to changes in the network occupancy status. In particular, the proposed algorithm shows superior performance in non saturated conditions when compared to the EKF. Numerical results demonstrate that the proposed algorithm provides a more viable method for estimation of the number of competing stations in an IEEE 802.11 network, than estimators based on the EKF.

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.

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 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.

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.

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.

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.

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.