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This paper considers the state estimation of Markovian jump linear systems with random parameters and estimate feedback. The state estimate at the previous epoch is introduced into the dynamical model to depict some phenomena that the system evolvement may depend on the most recent estimate. Then, the linear minimum mean square error estimator is derived for the considered system. A filtering framework...
Considering the convergence rate is a very important issue as distributed sensors networks usually consist of low-powered wireless devices and speeding up the consensus convergence rate is also important to reduce the number of messages exchanged among neighbors, a new adaptive method for weight assignment of communication links between sensor nodes is proposed based on the dynamic network topology...
A problem of state estimation with destination constraint is considered in this paper. An anti-radiation missile (ARM) often moves towards the target along a trajectory which is almost linear in the X-Y plane. The linear constraint for trajectory and target position are known as priori and can be used to enhance the performance of a tracking filter. In this paper, a destination constrained Kalman...
In this paper, we consider different approaches in reducing the amount of data transfer in a distributed Kalman filtering based on noisy linear observations. The observations are either compressed using equivalent measurements, or transmitted only if their values change more than a specified value. The objective is to reduce sensor data traffic with relatively small estimation performance degradation...
A fault detection, identification, estimation and state estimation (FDIESE) problem involves joint decision and estimation (JDE). Decision contains detection and identification, while estimation is for fault severeness and system state. Both detection and identification are highly coupled with estimation and a fault is identified after detection. To solve this problem, an approach named nested joint...
Tracking single or multiple maneuvering targets is an urgent need for defense. In order to meet the military requirement, we propose a modified clustering-based Rao-Blackwellized particle filter (CBRBPF) to track single or multiple maneuvering targets with observations received by single or multiple sensors. The modified RBPF is basing on the clustering-based data association method. We partition...
The paper deals with the nonlinear state estimation of stochastic dynamic systems with a special focus on coping with outliers appearing in the system. A new stochastic integration Student's-t filter is developed based on the generic Student's-t filter and assuming the density of random variables present in the model and the conditional density of the state be Student's-t distributed. For evaluation...
The controllability of the cascade leader-follower formation system for multi-robots is studied based on the controllability rank condition for general nonlinear system. For the cascade robot formation system, bearing-only unscented Kalman filter (UKF) is used for the state estimation of the leader and the follower robots at all levels, which enables the real-time movement control of the follower...
This paper compares the tracking performance that can be achieved when using a nonlinear drag model for a helicopter, a constant drag motion model, and a baseline constant acceleration model. A particle filter is used for state estimation to address problems associated with nonlinear drag and nonlinear measurements of helicopter pose. We demonstrate that the inclusion of this nonlinear kinematic effect...
The Gaussian inverse Wishart (GIW) filter is a promising filter for extended target tracking and draws tremendous attention in recent years. The Gaussian and the inverse Wishart distributions are used to describe the target's kinematical and extended states, respectively. However, the filter for estimating the extended state contains predicting position error and causes large error of the extended...
As communication bandwidth and resources are limited in network-based control systems, in order to reduce superfluous waste, it is necessary to design an event-triggered communication mechanism. In this paper, the problem of event-triggered state estimation is studied for fusion of multiple sensors with correlated noise. The noise of different sensors are cross-correlated and coupled with the system...
Multiple-source localization problem based on acoustic energy measurements is investigated by set-membership estimation theory. When the probability density function of measurement noise is unknown-but-bounded, multiple-source localization is a difficult problem since not only the acoustic energy measurement is a complicated nonlinear function of multiple sources, but also the multiple sources bring...
The paper deals with the state estimation of nonlinear stochastic dynamic systems. The stress is laid on the assessment of the estimate error, which is caused by the violation of the estimator design assumptions. The assessment is based on measures comparing estimators actual working conditions and the assumptions under which the estimators have been proposed. In particular, the measures of nonlinearity...
The estimation fusion problem and posterior Cramer-Rao bound (PCRB) are presented for multi-sensor nonlinear systems with uncertain observations. In order to effectively deal with the difficulties caused by uncertainty, a novel method is proposed by introducing 0–1 latent variables. It has two nice properties. Firstly, the derived estimation fusion method can take full advantage of the character of...
The problem of fixed-lag smoothing with linear equality constraint (LEC) is considered. Fixed-lag smoothing algorithms are developed by applying the state-augmentation approach to several popular constrained filtering methods, including model reduction, pseudo measurement, estimate projection and other two LEC filtering methods based on conversion or direct elimination method. After a briefly review,...
Traffic control and vehicle route planning require accurate estimates of the traffic state in order to be successfully implemented. This estimation problem can be solved by using particle filters in conjunction with macroscopic traffic models such as the stochastic compositional model. The accuracy of the estimates can be decreased for road segments where there are no measurements available. However,...
The paper introduces a novel approach to an estimator design, the cooperative filter design, for state estimation of nonlinear systems. The approach is based on the idea of combining estimates of several different approximate (and thus sub-optimal) nonlinear filters, which are configured to perform the same task. Within the concept, two strategies are proposed, namely the cooperative estimation and...
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