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2018
The cubature Kalman filter (CKF) is the closest approximation known so far to the Bayesian filter that could be designed in a nonlinear setting under the Gaussian assumption. Unlike the extended Kalman filter (EKF), CKF does not require evaluation of Jacobians during the estimation process, while in EKF the nonlinear functions are approximated by their
Kumar Pakki Bharani Chandra, Da-Wei Gu
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The cubature Kalman filter (CKF) is the closest approximation known so far to the Bayesian filter that could be designed in a nonlinear setting under the Gaussian assumption. Unlike the extended Kalman filter (EKF), CKF does not require evaluation of Jacobians during the estimation process, while in EKF the nonlinear functions are approximated by their
Kumar Pakki Bharani Chandra, Da-Wei Gu
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Variants of Cubature Kalman Filter
2018Cubature 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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Cubature Kalman filters: Derivation and extension
Chinese Physics B, 2013This paper focuses on the cubature Kalman filters (CKFs) for the nonlinear dynamic systems with additive process and measurement noise. As is well known, the heart of the CKF is the third-degree spherical—radial cubature rule which makes it possible to compute the integrals encountered in nonlinear filtering problems.
Xin-Chun Zhang, Cheng-Jun Guo
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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), 2021Based 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 +3 more
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Composite embedded cubature Kalman filter
International Journal of Adaptive Control and Signal Processing, 2017SummaryThe embedded cubature Kalman filter (ECKF) is proven as a kind of algorithm that has higher precision than cubature Kalman filter. Based on the ECKF, a new algorithm, named composite ECKF (CECKF), is presented in this paper. The new CECKF can increase filter precision by means of reusing the embedded cubature rule in the process of numerical ...
Meng, Dong, Miao, Lingjuan, Shao, Haijun
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A cubature Kalman filter with uncompensated biases
2013 6th International Congress on Image and Signal Processing (CISP), 2013An improved nonlinear filter is proposed in the framework of the cubature Kalman filter (CKF) with uncompensated biases. This filter can be applied for the nonlinear system with unknown random biases, which are unable to be modeled in practical situations.
Xia Ning, Jian Yang
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Distributed object tracking based on cubature Kalman filter
2013 Asilomar Conference on Signals, Systems and Computers, 2013In this work, we propose the cubature Kalman filter (CKF) based distributed object tracking algorithm in a visual sensor network (VSN). A VSN consists of several distributed smart cameras having the ability to process and analyze the retrieved data locally. The first objective is to optimize the tracking process within the VSN through the CKF.
Venkata Pathuri Bhuvana +3 more
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Stability analysis of the discrete-time cubature Kalman filter
2015 54th IEEE Conference on Decision and Control (CDC), 2015This study analyses the estimation error behaviour of the discrete-time cubature Kalman filter (CKF) for general nonlinear systems with nonlinear measurements. First, we show that, under certain conditions, estimation error of the CKF remains bounded.
Thumeera R. Wanasinghe +2 more
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Continuous discrete cubature quadrature Kalman filter
Asian Journal of Control, 2021AbstractIn this paper, cubature quadrature Kalman filter algorithm has been reformulated for nonlinear state space model of continuous‐discrete nature. The developed method is named as continuous‐discrete cubature quadrature Kalman filter (CD‐CQKF). The formulated algorithm has been applied to track the trajectory of an aircraft.
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Event Triggered Cubature Kalman Filter
2020The 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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