Improved strong tracking square-root cubature Kalman filtering method

A Kalman filtering and square root technology, applied in the field of nonlinear filtering, can solve problems such as increasing the time complexity of the algorithm

Inactive Publication Date: 2016-02-24
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

According to the implementation steps of SCKF, this subtraction factor introduction method is equivalent to repeating the part from "calculating the volume point" to "calculating the cross-correlation covariance matrix" in the measurement update twice, which greatly increases the time complexity of the algorithm

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Embodiment Construction

[0046] The present invention will be further described below with reference to the accompanying drawings and an embodiment of a ship propulsion system, and the present invention includes but not limited to the following embodiments.

[0047] The realization steps of the present invention are as follows:

[0048] Consider the following discrete-time nonlinear dynamical system:

[0049] x k+1 = f k (x k )+w k

[0050] the y k+1 =h k+1 (x k+1 )+v k+1

[0051] where x k ∈ R n is the system state vector, y k+1 ∈ R m is the measurement vector; and are the state function and measurement function of the nonlinear system, respectively; w k ∈ R n is the system noise, v k+1 ∈ R m For the measurement noise, both are Gaussian white noise, uncorrelated with each other, and the covariances are Q and R respectively.

[0052] The specific process of the ISTSCKF algorithm based on the above nonlinear system is as follows:

[0053] 1) Set initial parameters

[0054] Set t...

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Abstract

The invention provides an improved strong tracking square-root cubature Kalman filtering method. A mechanism for increasing the robustness of a strong tracking algorithm through an attenuation factor and an SCKF algorithm flow are analyzed; therefore, ISTCKF reselects the introduction position of the attenuation factor; and thus, the extra calculation amount due to introduction of the attenuation factor is reduced.

Description

technical field [0001] The invention relates to a nonlinear filtering method. Background technique [0002] Volumetric Kalman filter (cubature Kalman filter, CKF) is a new nonlinear approximate filtering algorithm proposed by Canadian scholars Ienkaran Arasaratnam and Simon Haykin in 2009. Since CKF needs to square the covariance matrix when solving the volume point, as the number of filtering iterations increases, the accumulation of rounding errors may cause the covariance matrix to lose its non-negative definiteness or even lose its symmetry. On the basis of CKF, IenkaranArasaratnam and SimonHaykin added the square root algorithm and proposed the square root volumetric Kalman filter algorithm (square-rootcubatureKalmanfilter, SCKF). This algorithm effectively solves the numerical stability problem of CKF, reduces the amount of computation, and provides better filtering performance. [0003] In the actual system, because (1) the mathematical model is inaccurate; (2) the ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H03H17/02
Inventor 张安鲍水达任卫
Owner NORTHWESTERN POLYTECHNICAL UNIV
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