This paper proposes a new recursive least-squares (RLS) estimation algorithm for an impulse response function in linear continuous-time wide-sense stationary stochastic systems. It is assumed that the input signal to ...
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This paper proposes a new recursive least-squares (RLS) estimation algorithm for an impulse response function in linear continuous-time wide-sense stationary stochastic systems. It is assumed that the input signal to the unknown impulse response function is contaminated by additive white Gaussian observation noise. The output signal from the system related with the impulse response function is observed with additive white Gaussian noise. The impulse response function is estimated recursively in terms of the variance of the white Gaussian observation noise included in the input signal, the autocovariance function of the process before the observation noise is added to the input signal, the crosscovariance function between the output observed value and the input observed value, concerning the system based on the unknown impulse response function. (C) 2002 Elsevier Science Inc. All rights reserved.
A fast constrained recursive identification (CRI) algorithm is proposed to estimate intersection origin-destination (O-D) matrices dynamically. The basic idea of the CRI algorithm is to estimate intersection O-D matri...
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A fast constrained recursive identification (CRI) algorithm is proposed to estimate intersection origin-destination (O-D) matrices dynamically. The basic idea of the CRI algorithm is to estimate intersection O-D matrices based on equality-constrained optimization and then to adjust them by Bell's correction (Bell, M.G.H., 1991a. The estimation of origin-destination matrices by constrained generalized least squares. Transporation Research 25B, 13-22;Bell, M.G.H., 1991b. The real-time estimation of origin-destination flows in the presence of platoon dispersion. Transportation Research 25B, 115-125.) for inequality constraints. Numerical results show that the accuracy of estimates by the CRI algorithm is fairly good-the solutions obtained by the CRI are optimal in majority of the cases, while the computational efforts are very limited-increment mainly lies on the evaluation of an inverse for an mxm matrix (m = 4 for a typical intersection) compared with the ordinary recursive least squares method. These results mean that a properly designed recursive algorithm can indeed avoid iterative procedure in each time step to obtain highly accurate on-line estimates for intersection O-D matrices. Therefore, the CRI algorithm with its reasonable balance between accuracy and computational simplicity is very suitable for practical use. (C) 1999 Elsevier Science Ltd. All rights reserved.
Vehicle time headway is an important traffic parameter. It affects roadway safety, capacity, and level of service. Single inductive loop detectors are widely deployed in road networks, supplying a wealth of informatio...
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Vehicle time headway is an important traffic parameter. It affects roadway safety, capacity, and level of service. Single inductive loop detectors are widely deployed in road networks, supplying a wealth of information on the current status of traffic flow. In this paper, we perform Bayesian analysis to online estimate average vehicle time headway using the data collected from a single inductive loop detector. We consider three different scenarios, i.e. light, congested, and disturbed traffic conditions, and have developed a set of unified recursive estimation equations that can be applied to all three scenarios. The computational overhead of updating the estimate is kept to a minimum. The developed recursive method provides an efficient way for the online monitoring of roadway safety and level of service. The method is illustrated using a simulation study and real traffic data. (C) 2011 Elsevier Ltd. All rights reserved.
Gaussian Processes (GPs) are powerful kernelized methods for non-parametric regression used in many applications. However, their use is limited to a few thousand of training samples due to their cubic time complexity....
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Gaussian Processes (GPs) are powerful kernelized methods for non-parametric regression used in many applications. However, their use is limited to a few thousand of training samples due to their cubic time complexity. In order to scale GPs to larger datasets, several sparse approximations based on so-called inducing points have been proposed in the literature. In this work we investigate the connection between a general class of sparse inducing point GP regression methods and Bayesian recursive estimation which enables Kalman Filter like updating for online learning. The majority of previous work has focused on the batch setting, in particular for learning the model parameters and the position of the inducing points, here instead we focus on training with mini-batches. By exploiting the Kalman filter formulation, we propose a novel approach that estimates such parameters by recursively propagating the analytical gradients of the posterior over mini-batches of the data. Compared to state of the art methods, our method keeps analytic updates for the mean and covariance of the posterior, thus reducing drastically the size of the optimization problem. We show that our method achieves faster convergence and superior performance compared to state of the art sequential Gaussian Process regression on synthetic GP as well as real-world data with up to a million of data samples. (C) 2020 Elsevier Ltd. All rights reserved.
In the various biomedical microfluidic devices the target biomolecules are delivered by activating electroosmotic flows. The zeta potential of a microchannel wall, which determines the strength of the electroosmotic f...
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In the various biomedical microfluidic devices the target biomolecules are delivered by activating electroosmotic flows. The zeta potential of a microchannel wall, which determines the strength of the electroosmotic flow, is apt to change due to the adhesion of biomolecules such as DNA or protein especially around the microchannel turns. The resulting transient inhomogeneous profile of zeta potential alters flow pattern, volumetric flow rate and the band broading of solutes. In the present work, we have developed a method for the recursive estimation of transient inhomogeneous zeta potential in microchannel turns using velocity measurements. For the real time implementation of the present method, a compact and accurate reduced-order model is derived using the Karhunen-LoSve Galerkin method and the Helmholtz-Smoluchowski slip velocity. The present scheme of recursive estimation is an important prerequisite to the real time control of microfluidic devices.
We consider recursive estimation of images modeled by non-Gaussian autoregressive (AR) models and corrupted by spatially white Gaussian noise. The goal is to find a recursive algorithm to compute a near minimum mean s...
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We consider recursive estimation of images modeled by non-Gaussian autoregressive (AR) models and corrupted by spatially white Gaussian noise. The goal is to find a recursive algorithm to compute a near minimum mean square error (MMSE) estimate of each pixel of the scene using a fixed lookahead of D rows and D columns of the observations. Our method is based on a simple approximation that makes possible the development of a useful suboptimal nonlinear estimator. The algorithm is first developed for a non-Gaussian AR time-series and then generalized to two dimensions, In the process, we draw on the well-known reduced update Kalman filter (KF) technique of Woods and Radewan [1] to circumvent computational load problems, Several examples demonstrate the non-Gaussian nature of residuals for AR image models and that our algorithm compares favorably with the Kalman filtering techniques in such cases.
This paper presents the application of well known recursive estimation techniques to the important problem of power system harmonics in a noisy environment. on-line estimation of harmonic amplitudes and phases is perf...
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This paper presents the application of well known recursive estimation techniques to the important problem of power system harmonics in a noisy environment. on-line estimation of harmonic amplitudes and phases is performed using several variants of recursive least square (RLS) algorithms, known for their simplicity of computation and good convergence properties. The estimates are updated recursively as samples of the harmonic signals are received. A noisy harmonic signal from the AC bus of a six pulse rectifier is used as a test signal in the simulation. The various RLS algorithms are evaluated under different signal to noise ratios (SNR) and are shown to produce good harmonic magnitude and phase estimates even for a 0 dB SNR. Due to their simplicity, these algorithms are appropriate for on-line implementation in polluted power systems. (C) 1998 Elsevier Science S.A. All rights reserved.
In this paper, the problem of recursive estimation is studied for a class of descriptor systems with multiple packet dropouts and correlated noises. The multiple packet dropouts phenomenon is considered to be random a...
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In this paper, the problem of recursive estimation is studied for a class of descriptor systems with multiple packet dropouts and correlated noises. The multiple packet dropouts phenomenon is considered to be random and described by a binary switching sequence that obeys a conditional probability distribution. The autocorrelated measurement noise is characterized by the covariances between different time instants. The descriptor system is transformed into a regular line system with an algebraic constraint. By using an innovation analysis method and the orthogonal projection theorem, recursive estimators including filter, predictor and smoother are developed for each subsystem and the process noise. Further, the recursive filter, predictor and smoother are obtained for the original descriptor system with possible multiple packet dropouts phenomenon and correlated noises. Simulation results are provided to demonstrate the effectiveness of the proposed approaches. (C) 2013 Elsevier Masson SAS. All rights reserved.
We present a formulation for recursive recovery of motion, pointwise structure, and focal length from feature correspondences tracked through an image sequence. In addition to adding focal length to the state vector, ...
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We present a formulation for recursive recovery of motion, pointwise structure, and focal length from feature correspondences tracked through an image sequence. In addition to adding focal length to the state vector, several representational improvements are made over earlier structure from motion formulations, yielding a stable and accurate estimation framework which applies uniformly to both true perspective and orthographic projection. Results on synthetic and real imagery illustrate the performance of the estimator.
A hidden Markov regime is a Markov process that governs the time or space dependent distributions of an observed stochastic process. recursive algorithms can be used to estimate parameters in mixed distributions gover...
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A hidden Markov regime is a Markov process that governs the time or space dependent distributions of an observed stochastic process. recursive algorithms can be used to estimate parameters in mixed distributions governed by a Markov regime. Here we derive a recursive algorithm for estimation of parameters in a Markov-modulated Poisson process also called a Cox point process. By this we mean a doubly stochastic Poisson process with a time dependent intensity that can take on a finite number of different values. The intensity switches randomly between the possible values according to a Markov process. We consider two different ways to observe the Markov-modulated Poisson process: in the first model the observations consist of the observed time intervals between events, and in the second model we use the total number of events in successive intervals of fixed length. We derive an algorithm for recursive estimation of the Poisson intensities and the switch intensities between the two states and illustrate the algorithm in a simulation study. The estimates of the switch intensities are based on the observed conditional switch probabilities.
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