Vladimir Shin

Mobile Node Localization Using Fusion Prediction-Based Interacting Multiple Model in Cricket Sensor Network

This paper addresses an approach to estimating the location of a mobile node based on the range measurements of Cricket sensor network (CSN), where the coordinates of the mobile node are calculated via the method of trilateration. There are, in general, two kinds of obstacles to be tackled and overcome in CSN: One is noisy distance measurements, and the other is the low data rates of Cricket sensors. To overcome these problems, we propose a fusion prediction-based interacting multiple model (FPB-IMM) algorithm. The FPB-IMM algorithm utilizes multiple position measurements produced by trilateration and a self-tuning algorithm; it takes advantage of these multiple measurements to minimize the effect of noisy measurements and the low data rates by modifying a cycle of IMM with fusion prediction. The experimental results demonstrate that the proposed algorithm outperforms existing algorithms such as the Kalman filter and the conventional IMM.

Asymptotically minimax bias estimation of the correlation coefficient for bivariate independent component distributions

For bivariate independent component distributions, the asymptotic bias of the correlation coefficient estimators based on principal component variances is derived. This result allows to design an asymptotically minimax bias (in the Huber sense) estimator of the correlation coefficient, namely, the trimmed correlation coefficient, for contaminated bivariate normal distributions. The limit cases of this estimator are the sample, median and MAD correlation coefficients, the last two simultaneously being the most B- and V-robust estimators. In contaminated normal models, the proposed estimators dominate both in bias and in efficiency over the sample correlation coefficient on small and large samples.

Distributed Estimation Fusion With Application to a Multisensory Vehicle Suspension System With Time Delays

A new distributed fusion filtering algorithm for linear multiple time-delayed systems is proposed. The multisensory distributed fusion filter is formed by the summation of local Kalman filters having time delays (LKFTDs) in both the system and measurement models. The proposed distributed filter has a parallel structure that enables processing of multisensory measurements; thereby, it is more reliable than the centralized version if some sensors turn faulty. The key contribution of this paper is the derivation of recursive error cross-covariance equations between the LKFTDs to compute the optimal matrix fusion weights. In the particular case of multisensory dynamic systems having time delays in only the measurement model, the obtained results coincide with the previous work of Sun. The high accuracy and efficiency of the proposed distributed filter are then demonstrated through its implementation on a vehicle suspension system.

Classification of road surface status using a 94 GHz dual-channel polarimetric radiometer

In this article, we classify road surface statuses using a Bayesian classification method. This article uses principal component analysis (PCA) that combines a 94 GHz dual-channel polarimetric radiometer. The radiometer is used to investigate the behaviour of the brightness temperature (BT) of different road surface statuses in an open-air laboratory. The aim of this investigation is to characterize four different road surface classes (dry, wet, snowy and icy). Here, the BT (radiothermal emission) characteristics are measured at horizontal and vertical polarizations. For a given database of weather information (including BT, road surface temperature, wind speed, etc.), a PCA subspace is constructed, and the score vectors are classified by solving the Bayesian classification method. As a result, the road surface statuses were found to be well classified by the proposed method in real time.

Multisensory prediction fusion of nonlinear functions of the state vector in discrete-time systems

We propose two new multisensory fusion predictors for an arbitrary nonlinear function of the state vector in a discrete-time linear dynamic system. Nonlinear function of the state (NFS) represents a nonlinear multivariate functional of state variables, which can indicate useful information of the target system for automatic control. To estimate the NFS using multisensory information, we propose centralized and decentralized predictors. For multivariate polynomial NFS, we propose an effective closed-form computation procedure for the predictor design. For general NFS, the most popular procedure for the predictor design is based on the unscented transformation. We demonstrate the effectiveness and estimation accuracy of the fusion predictors on theoretical and numerical examples in multisensory environment.

Estimation fusion of nonlinear cost functions with application to multisensory Kalman filtering

Abstract This paper focuses on four fusion algorithms for the estimation of nonlinear cost function (NCF) in a multisensory environment. In multisensory filtering and control problems, NCF represents a nonlinear multivariate functional of state variables, which can indicate useful information of the target systems for automatic control. To estimate the NCF using multisensory information, we propose one centralized and three decentralized estimation fusion algorithms. For multivariate polynomial NCFs, we propose a simple closed-form computation procedure. For general NCFs, the most popular procedure for the evaluation of their estimates is based on the unscented transformation. The effectiveness and estimation accuracy of the proposed fusion algorithms are demonstrated with theoretical and numerical examples.

Efficient multisensor fusion with sliding window Kalman filtering for discrete-time uncertain systems with delays

In this study, we provide two computationally effective multisensory fusion filtering algorithms for discrete-time linear uncertain systems with state and observation time-delays. The first algorithm is shaped by algebraic forms for multirate sensor systems, and then we propose a matrix form of filtering equations using block matrices. The second algorithm is based on exact cross-covariance matrix equations. These equations are useful to compute matrix weights for fusion estimation in a multidimensional-multisensor environment. Furthermore, our proposed filtering algorithms are based on the sliding window strategy in order to achieve high estimation accuracy and stability under parametric uncertainties. The authors demonstrate the low computational complexities of the proposed fusion filtering algorithms and how the proposed algorithms robust against dynamic model uncertainties comparing with Kalman filtering with time delays.

Distributed Fusion Filter on Images with Time Delays

This paper focuses on a distributed image fusion filtering algorithm and fusion formulas for time delayed multiple pixels received from multiple sensors (cameras). Since local cross-covariances between images are important values to implement fusion formulas, we present exact formulas for cross-covariances which are a vital factor for calculating matrix weights in image processing. Subsequent analysis of the proposed fusion algorithm is presented through a typical example demonstrating the effectiveness of the proposed fusion algorithm.

Optimal and Suboptimal Estimation of Quadratic Functionals of the State Vector in Linear Stochastic Systems

ABSTRACT This paper focuses on estimation of a quadratic functional (QF) of a random signal in dynamic systems described by a linear stochastic differential equations. The QF represents a quadratic form of state variables, which can indicate useful information of a target system for control. The optimal (in mean square sense) and suboptimal estimators of a QF represent a function of the Kalman estimate and error covariance. The proposed estimation algorithms have a closed-form estimation procedure. The quadratic estimators are studied in detail, including derivation of the exact formulas for mean square errors. The obtained results we demonstrate on practical example, and comparison analysis between optimal and suboptimal estimators is presented. Research highlights ▸ An optimal mean square estimator for an arbitrary QF in linear stochastic systems is derived.▸ The proposed estimator is a comprehensively investigated, including derivation of matrix equation for its mean square error.▸ Performance of the optimal and suboptimal estimators is illustrated on theoretical and practical examples for real QFs.

Decentralized Fusion Filtering with Different Sensor Memories in Dynamics System with Uncertainties

This paper is concerned with the decentralized sliding window Kalman filter for multisensor discrete-time dynamic linear systems having different sensor memories. The proposed filtering is presented that combines the Kalman filter and sliding window strategy. A decentralized fusion with the weighted sum structure is applied to the local sliding window Kalman filters (LSWKFs) having different sensor memory size. The proposed decentralized algorithm has a parallel structure and allows parallel processing of observations, thereby it more reliable than the centralized version if some sensors become faulty. Moreover, the choice of sliding window strategy makes the proposed algorithm robust against dynamic model uncertainties. The derivation of the error cross-covariances between the LSWKFs is the key idea of this paper. The application of the proposed decentralized filer to linear discrete-time dynamic systems within a multisensor environment demonstrates its high accuracy and computational efficiency.