scholarly journals Two equivalent multi-sensor Kalman filters with variable delays and intermittent measurements

2020 ◽  
Author(s):  
Babak Tavassoli ◽  
Parisa Joshaghani
Keyword(s):  
Kalman Filtering ◽  
Kalman Filters ◽  
Measurement Data ◽  
Upper Bounds ◽  
Floating Point ◽  
Time Varying ◽  
Missing Measurements ◽  
Multiple Sensors ◽  
Variable Delays ◽  
Intermittent Measurements

Kalman filtering of measurement data from multiple sensors with time-varying delays and missing measurements is considered in this work. Two existing approaches to Kalman filtering with delays are extended by removing some assumptions in order to have equivalent filtering methods and making comparisons between them. The computational loads of the two methods are compared in terms of the average number of floating point operations required by each method for different system dimensionalities and delay upper bounds. The results show that the superiority of the methods over each other depends on the comparison conditions.

scholarly journals Two equivalent multi-sensor Kalman filters with variable delays and intermittent measurements

2020 ◽  
Author(s):  
Babak Tavassoli ◽  
Parisa Joshaghani
Keyword(s):  
Kalman Filtering ◽  
Kalman Filters ◽  
Measurement Data ◽  
Upper Bounds ◽  
Floating Point ◽  
Time Varying ◽  
Missing Measurements ◽  
Multiple Sensors ◽  
Variable Delays ◽  
Intermittent Measurements

Kalman filtering of measurement data from multiple sensors with time-varying delays and missing measurements is considered in this work. Two existing approaches to Kalman filtering with delays are extended by removing some assumptions in order to have equivalent filtering methods and making comparisons between them. The computational loads of the two methods are compared in terms of the average number of floating point operations required by each method for different system dimensionalities and delay upper bounds. The results show that the superiority of the methods over each other depends on the comparison conditions.

Covariance Intersection Fusion Robust Time-Varying Kalman Filter for Two-Sensor System with Uncertain Noise Variances

2013 ◽  
Vol 475-476 ◽  
pp. 470-475
Author(s):  
Wen Juan Qi ◽  
Peng Zhang ◽  
Gui Huan Nie ◽  
Zi Li Deng
Keyword(s):  
Kalman Filter ◽  
Kalman Filters ◽  
Tracking System ◽  
System Simulation ◽  
Lyapunov Equation ◽  
Conservative System ◽  
Upper Bounds ◽  
Time Varying ◽  
Worst Case ◽  
Covariance Intersection

This paper investigates the problem of designing covariance intersection fusion robust time-varying Kalman filter for two-sensor time-varying system with uncertain noise variances. Using the minimax robust estimation principle, the local and covariance intersection (CI) fusion robust time-varying Kalman filters are presented based on the worst-case conservative system with the conservative upper bounds of noise variances. Their robustness is proved based on the proposed Lyapunov equation, and the robust accuracy of time-varying CI fuser is higher than that of each local robust time-varying Kalman filter. A two-sensor tracking system simulation verifies the robustness and robust accuracy relations.

A Comparison of H-Infinity With Kalman Filtering in Parameter Identification

2005 ◽  
Author(s):  
Kiriakos Kiriakidis ◽  
Richard T. O’Brien
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Kalman Filters ◽  
Tuning Parameter ◽  
Time Varying ◽  
H Infinity ◽  
New Approach ◽  
Wide Range ◽  
Parameter Values ◽  
Tolerance Method

The authors perform a thorough investigation of the extended H-infinity and Kalman filters based on results from stiffness estimation experiments. The paper uses the time-varying tolerance method to tune the H-infinity filter. Exploring similarities in the associated Riccati equations, the authors also propose a new approach for tuning the Kalman filter. The experimental results show that the H-infinity outperforms the Kalman filter along a wide range of tuning parameter values.

Robust integrated covariance intersection fusion Kalman estimators for networked mixed uncertain time-varying systems

2020 ◽  
Author(s):  
Yuan Gao ◽  
Zili Deng
Keyword(s):  
Kalman Filtering ◽  
Estimation Error ◽  
Original System ◽  
Lyapunov Equation ◽  
Upper Bounds ◽  
Time Varying ◽  
Worst Case ◽  
Covariance Intersection ◽  
Integrated Covariance ◽  
White Noises

Abstract For the multisensor time-varying networked mixed uncertain systems with random one-step sensor delays and uncertain-variance multiplicative and linearly dependent additive white noises, a new augmented state method with fictitious noises is presented, by which the original system is transformed into a standard system without delays and with uncertain-variance fictitious white noises. According to the minimax robust estimation principle and the Kalman filtering theory, based on the worst-case system with the conservative upper bounds of uncertain noise variances, the local and integrated covariance intersection (ICI) fused robust time-varying Kalman estimators (filter, predictor and smoother) are presented respectively in the sense that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. Their robustness is proved by the extended Lyapunov equation method, and their accuracy relations are compared based on the traces of the variance matrices and the covariance ellipsoids, respectively. Specially, a universal ICI fusion robust Kalman filtering method of integrating the local robust estimators and their conservative cross-covariances is presented. It overcomes the drawbacks of the original covariance intersection (CI) fusion method and improves robust accuracy of the original CI fuser. A simulation example applied to two-mass spring system shows the effectiveness of the proposed methods and results.

scholarly journals State Estimation for a Class of Distributed Parameter Systems with Time-Varying Delay over Mobile Sensor–Actuator Networks with Missing Measurements

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 661
Author(s):  
Huansen Fu ◽  
Baotong Cui ◽  
Bo Zhuang ◽  
Jianzhong Zhang
Keyword(s):  
State Estimation ◽  
Distributed Parameter Systems ◽  
Operator Semigroup ◽  
Distributed Parameter ◽  
Time Varying ◽  
Mobile Sensor ◽  
Missing Measurements ◽  
Time Varying Delay ◽  
Sensor Actuator ◽  
Varying Delay

This work proposes a state estimation strategy over mobile sensor–actuator networks with missing measurements for a class of distributed parameter systems (DPSs) with time-varying delay. Initially, taking advantage of the abstract development equation theory and operator semigroup method, this kind of delayed DPSs described by partial differential equations (PDEs) is derived for evolution equations. Subsequently, the distributed state estimators including consistency component and gain component are designed; the purpose is to estimate the original state distribution of the delayed DPSs with missing measurements. Then, a delay-dependent guidance approach is presented in the form of mobile control forces by constructing an appropriate Lyapunov function candidate. Furthermore, by applying Lyapunov stability theorem, operator semigroup theory, and a stochastic analysis approach, the estimation error systems have been proved asymptotically stable in the mean square sense, which indicates the estimators can approximate the original system states effectively when this kind of DPS has time-delay and the mobile sensors occur missing measurements. Finally, the correctness of control strategy is illustrated by numerical simulation results.

Simulating and analyzing artificial nonstationary earthquake ground motions

1982 ◽  
Vol 72 (2) ◽  
pp. 615-636
Author(s):  
Robert F. Nau ◽  
Robert M. Oliver ◽  
Karl S. Pister
Keyword(s):  
Difference Equations ◽  
Kalman Filters ◽  
Ground Motions ◽  
Structural Response ◽  
Model Parameters ◽  
Time Varying ◽  
Nonparametric Method ◽  
Linear Difference Equations ◽  
Earthquake Ground Motions ◽  
Earthquake Accelerograms

Abstract This paper describes models used to simulate earthquake accelerograms and analyses of these artificial accelerogram records for use in structural response studies. The artificial accelerogram records are generated by a class of linear linear difference equations which have been previously identified as suitable for describing ground motions. The major contributions of the paper are the use of Kalman filters for estimating time-varying model parameters, and the development of an effective nonparametric method for estimating the variance envelopes of the accelerogram records.

Analysis of Operating Deflection Shapes Based on Subspace Method

1997 ◽  
Author(s):  
Nobutaka Tsujiuchi ◽  
Yuichi Matsumura ◽  
Takayuki Koizumi
Keyword(s):  
Experimental Data ◽  
Time Domain ◽  
State Space Model ◽  
Measurement Data ◽  
Time Varying ◽  
Subspace Method ◽  
Identification Scheme ◽  
Space Model ◽  
Time Varying Systems ◽  
Frequency Components

Abstract In this paper, we propose the new method to identify the Operating Deflection Shapes (ODSs) from the measurement data of time domain. At first, we present the identification scheme of ODSs based on a state-space model. Then the scheme is extended to identify the ODSs adaptively for the time-varying systems by using the URV Decomposition (URVD). Proposed scheme is able to decompose the deformation of a structure under operating condition into the underlying superposition of well excited frequency components. This paper introduces the algorithm and shows the effectiveness of our proposed scheme applyed for both synthesized and experimental data.

Estimation of Track Irregularity Based on Genetic Algorithm and Unscented Kalman Filtering

2012 ◽  
Author(s):  
Hongmei Shi ◽  
Zujun Yu
Keyword(s):  
Genetic Algorithm ◽  
Kalman Filtering ◽  
High Speed ◽  
Measurement Data ◽  
Estimation Method ◽  
Coupling Model ◽  
Dynamic Responses ◽  
The Real ◽  
True Value ◽  
Track Irregularity

Track irregularity is the main excitation source of wheel-track interaction. Due to the difference of speed, axle load and suspension parameters between track inspection train and the operating trains, the data acquired from the inspection car cannot completely reflect the real status of track irregularity when the operating trains go through the rail. In this paper, an estimation method of track irregularity is proposed using genetic algorithm and Unscented Kalman Filtering. Firstly, a vehicle-track vertical coupling model is established, in which the high-speed vehicle is assumed as a rigid body with two layers of spring and damping system and the track is viewed as an elastic system with three layers. Then, the static track irregularity is estimated by genetic algorithm using the vibration data of vehicle and dynamic track irregularity which are acquired from the inspection car. And the dynamic responses of vehicle and track can be solved if the static track irregularity is known. So combining with vehicle track coupling model of different operating train, the potential dynamic track irregularity is solved by simulation, which the operating train could goes through. To get a better estimation result, Unscented Kalman Filtering (UKF) algorithm is employed to optimize the dynamic responses of rail using measurement data of vehicle vibration. The simulation results show that the estimated static track irregularity and the vibration responses of vehicle track system can go well with the true value. It can be realized to estimate the real rail status when different trains go through the rail by this method.

scholarly journals The analysis of some special results of a Lasota–Wazewska model with mixed variable delays

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Ramazan Yazgan ◽  
Osman Tunç
Keyword(s):  
Fixed Point ◽  
Fixed Point Theorem ◽  
Existence And Uniqueness ◽  
Banach Fixed Point Theorem ◽  
Time Varying ◽  
Differential Inequalities ◽  
Almost Periodic ◽  
Variable Delays ◽  
Pseudo Almost Periodic ◽  
Mixed Variable

AbstractThis study is about getting some conditions that guarantee the existence and uniqueness of the weighted pseudo almost periodic (WPAP) solutions of a Lasota–Wazewska model with time-varying delays. Some adequate conditions have been obtained for the existence and uniqueness of the WPAP solutions of the Lasota–Wazewska model, which we dealt with using some differential inequalities, the WPAP theory, and the Banach fixed point theorem. Besides, an application is given to demonstrate the accuracy of the conditions of our main results.

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