New developments in state estimation for nonlinear systems

Nørgaard, Magnus; Poulsen, Niels Kjølstad; Ravn, Ole

doi:10.1016/s0005-1098(00)00089-3

Cited by 898 publications

(587 citation statements)

References 4 publications

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“…In order to achieve higher estimation accuracy with reasonable computational load, several nonlinear filters have been introduced. These include the extended Kalman filter (EKF) [6], the unscented Kalman filter (UKF) [7] and its variants [8], the cubature Kalman filter (CKF) [9,10], the Gauss-Hermite filter (GHF) [11,12], the sparse-grid Gauss-Hermite filter (SGHF) [13], the central difference filter (CDF) [14], the divided difference filter (DDF) [15] etc.…”

Section: Introductionmentioning

confidence: 99%

Quadrature filters for one-step randomly delayed measurements

Singh

Bhaumik

Date

2016

Applied Mathematical Modelling

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In this paper, two existing quadrature filters, viz., the Gauss-Hermite filter (GHF) and the sparse-grid Gauss-Hermite filter (SGHF) are extended to solve nonlinear filtering problems with one step randomly delayed measurements. The developed filters are applied to solve a maneuvering target tracking problem with one step randomly delayed measurements.Simulation results demonstrate the enhanced accuracy of the proposed delayed filters compared to the delayed cubature Kalman filter and delayed unscented Kalman filter.

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Section: Introductionmentioning

confidence: 99%

Quadrature filters for one-step randomly delayed measurements

Singh

Bhaumik

Date

2016

Applied Mathematical Modelling

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“…The LocalDetector provides the functionalities of decentralized anomaly detection running on the local sensor nodes. In current version of SecMAS, we have implemented the the second-order divided difference filtering (DDF-2) [11] state estimation algorithm, the sequential probability ratio testing (SPRT) [12] based decision strategy. The GlobalDetector provides the functionalities of centralized anomaly detection.…”

Section: Anomaly Detection Based Security Guaranteementioning

confidence: 99%

SecMAS: Security Enhanced Monitoring and Analysis Systems for Wireless Sensor Networks

Ding

Yang

2016

ITM Web Conf.

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Abstract. The monitoring, control, and security guarantee for the communication in the wireless sensor networks (WSNs) are currently treated as three independent issues and addressed separately through specialized tools. However, most cases of WSNs applications requires the network administrator change the network configuration in a very short time to response to the change of observed phenomenon with security guarantee. To meet this requirement, we propose a security enhanced monitoring and control platform named SecMAS for WSNs, which provides the real-time visualization about network states and online reconfiguration of the network properties and behaviours in a resource-efficient way. Besides, basic cryptographic primitives and part of the anomaly detection functionalities are implemented in SecMAS to enabling the secure communication in WSNs. Furthermore, we conduct experiments to evaluate the performance of SecMAS in terms of the latency, throughput, communication overhead, and the security capacity. The experimental results demonstrate that the SecMAS system achieves stable, efficient and secure data collection with lightweight quick-response network control.

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“…In the past three decades, numerous successful applications of the EKF have been reported in the literature, but some intractable difficulties have also been encountered. For example, the use of EKF was reported in some applications to lead to biased estimates or even divergence, due to stepwise linearization (Julier & Uhlmann, 2004;Norgaard, Poulsen, & Ravn, 2000;Romanenko & Castro, 2004), inappropriate initial state estimates (Glielmo, Setola, & Vasca, 1999;Ljung, 1979;Reif, Sonnemann, & Unbehauen, 1998), unknown covariance matrices of involved noise, or even the Gaussianity assumption of involved noise (Arulampalam, Maskell, Gordon, & Clapp, 2002;Chen, Morris, & Martin, 2005), etc. To address such problems, some other nonlinear filtering methods, especially unscented Kalman filtering (UKF) (Julier & Uhlmann, 2004) and particle filtering (PF) (Arulampalam et al, 2002), have been tested for a variety of state estimation applications, gaining increasing popularity.…”

Section: Conclusive Remarksmentioning

confidence: 99%

“…Since 1994 he has been a Professor at the Technical University of Crete, Chania, Greece. He was a Visiting Professor at the Politecnico di Milano, Italy (1982), at the Ecole Nationale des Ponts et Chaussées, Paris (1985-1987, and at MIT, Cambridge (1997, 2000; and a Visiting Scholar at the University of Minnesota (1991Minnesota ( , 1993, the University of Southern California (1993) and the University of California, Berkeley (1993Berkeley ( , 1997Berkeley ( , 2000.…”

Section: Conclusive Remarksmentioning

confidence: 99%

An adaptive freeway traffic state estimator

Wang

Papageorgiou

Meßmer³

et al. 2009

Automatica

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a b s t r a c tReal-data testing results of a real-time nonlinear freeway traffic state estimator are presented with a particular focus on its adaptive features. The pursued general approach to the real-time adaptive estimation of complete traffic state in freeway stretches or networks is based on stochastic nonlinear macroscopic traffic flow modeling and extended Kalman filtering. One major innovative aspect of the estimator is the real-time joint estimation of traffic flow variables (flows, mean speeds, and densities) and some important model parameters (free speed, critical density, and capacity), which leads to four significant features of the traffic state estimator: (i) avoidance of prior model calibration; (ii) automatic adaptation to changing external conditions (e.g. weather and lighting conditions, traffic composition, control measures); (iii) enabling of incident alarms; (iv) enabling of detector fault alarms. The purpose of the reported real-data testing is, first, to demonstrate feature (i) by investigating some basic properties of the estimator and, second, to explore some adaptive capabilities of the estimator that enable features (ii)-(iv). The achieved testing results are quite satisfactory and promising for further work and field applications.

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New developments in state estimation for nonlinear systems

Cited by 898 publications

References 4 publications

Quadrature filters for one-step randomly delayed measurements

Quadrature filters for one-step randomly delayed measurements

SecMAS: Security Enhanced Monitoring and Analysis Systems for Wireless Sensor Networks

An adaptive freeway traffic state estimator

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