Results 161 to 170 of about 2,929 (197)

Variants of Cubature Kalman Filter

2018
Cubature Kalman filter (CKF) discussed in the last chapter deals with nonlinear systems with single set of sensors and with Gaussian noise. In this chapter, variants of CKF, namely the cubature information filter (CIF), cubature \(\mathcal{H}_{\infty }\) filter (C\(\mathcal{H}_{\infty }\)F) and cubature \(\mathcal{H}_{\infty }\) information filter (C\(\
Kumar Pakki Bharani Chandra, Da-Wei Gu
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Higher degree cubature quadrature kalman filter

International Journal of Control, Automation and Systems, 2015
In this paper, an algorithm has been developed to solve the nonlinear estimation problems. The intractable integrals, appeared during the estimation, have been approximately evaluated using any arbitrary but odd degree spherical cubature and higher order Gauss-Laguerre quadrature rule.
Abhinoy Kumar Singh, Shovan Bhaumik
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Event Triggered Cubature Kalman Filter

2020
The Event-triggered state estimation problem has been at the forefront of systems research for several decades and has seen multiple successful applications in diverse areas such as signal processing, target tracking, and navigation systems. Event-triggered state estimation offers a promising solution to data traffic congestion, in which information ...
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Cubature-based Kalman filters for positioning

2010 7th Workshop on Positioning, Navigation and Communication, 2010
We review a family of nonlinear filtering methods that includes unscented filters and cubature Kalman filters. These methods approximate the integrals occurring in the Bayesian formulation of the filtering problem by a sum of weighted integrand evaluations calculated at prescribed nodes.
H. Pesonen, R. Piche
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A Particle Filtering Algorithm Based on Cubature Kalman Filter

2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2021
Based on the Cubature Kalman filter algorithm (CKF) algorithm, we present a new particle filtering algorithm. To construct the importance density of samples, the importance density function is generated by a new framework, in which the state of each particle is predicted according to the concept of CKF.
Hongbo Yu, Qian Cao, Juntian Bo, Shuncheng Tan   +3 more
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Convergence analysis of cubature Kalman filter

2014 European Control Conference (ECC), 2014
This paper investigates the stability analysis of cubature Kalman filter (CKF) for nonlinear systems with linear measurement. The certain conditions to ensure that the estimation error of CKF remains bounded are proved. Then, the effect of process noise covariance is investigated and an adaptive process noise covariance is proposed to deal with large ...
Zarei, Jafar, Shokri, Ehsan, KARIMI, HAMID REZA   +2 more
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Iterated cubature Kalman filter and its application

2011 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, 2011
We present the novel iterated cubature Kalman filter (ICKF) in which the measurement update of square root of cubature Kalman filter (SR-CKF) is refined to iterate process for fully exploiting the latest measurement so as to achieve the high accuracy of state estimation. The ICKF is implemented easily and inherits the virtues of SR-CKF.
Jing Mu, Yuan-li Cai
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Adaptive radial rule based cubature Kalman filter

2015 American Control Conference (ACC), 2015
In this paper, a new adaptive cubature Kalman filter (ACKF) is proposed to improve the performance of the conventional cubature Kalman filter. The ACKF uses a new cubature rule that combines the third-degree spherical rule with the higher degree radial rule along the directions of larger uncertainty.
Bin Jia, Ming Xin
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Performance Comparison of Adaptive Cubature Kalman Filters

2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC), 2015
The performances of three classical adaptive cubature Kalman filters (ACKFs) for nonlinear stochastic discrete-time system with unknown process noise are investigated. First, filtering theories of the ACKFs are discussed, and then stability analysis and accuracy comparison simulations are performed.
Sisi Wang, Guoqing Qi, Quanbo Ge
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Cubature Kalman Filter based Localization and Mapping

IFAC Proceedings Volumes, 2011
Abstract Simultaneous Localization and Mapping (SLAM) is the process of simultaneously building a map and localizing in it, and can be used for autonomous navigation. SLAM deals with estimation of vehicle states and landmarks. Most SLAM algorithms are based on extended Kalman filters (EKFs).
Kumar Pakki. Bharani Chandra, Da-Wei Gu, Ian Postlethwaite   +2 more
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