Square root cubature information filter

Chandra, Bharani, Gu, Da-Wei and Postlethwaite, Ian (2013) Square root cubature information filter. IEEE Sensors Journal, 13 (2). pp. 750-758. ISSN 1530-437X

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Official URL: http://dx.doi.org/10.1109/JSEN.2012.2226441


Nonlinear state estimation plays a major role in many real-life applications. Recently, some sigma-point filters, such as the unscented Kalman filter, the particle filter, or the cubature Kalman filter have been proposed as promising substitutes for the conventional extended Kalman filter. For multisensor fusion, the information form of the Kalman filter is preferred over standard covariance filters due to its simpler measurement update stage. This paper presents a new state estimation algorithm called the square root cubature information filter (SRCIF) for nonlinear systems. The cubature information filter is first derived from an extended information filter and a recently developed cubature Kalman filter. For numerical accuracy, its square root version is then developed. Unlike the extended Kalman or extended information filters, the proposed filter does not require the evaluation of Jacobians during state estimation. The proposed approach is further extended for use in multisensor state estimation. The efficacy of the SRCIF is demonstrated by a simulation example of a permanent magnet synchronous motor.

Item Type: Article
Uncontrolled Keywords: cubature information filter, cubature Kalman filter and square root filters, nonlinear state estimation
Subjects: H100 General Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Becky Skoyles
Date Deposited: 04 Oct 2013 15:00
Last Modified: 13 Oct 2019 00:33
URI: http://nrl.northumbria.ac.uk/id/eprint/13766

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