Square Root Higher Order Volumetric Kalman Filter Method with Unknown Measurement Noise Variance

A technology of unknown measurement noise and Kalman filtering, applied in impedance networks, adaptive networks, electrical components, etc., can solve problems such as filtering of nonlinear systems with unknown measurement noise variance, to improve numerical stability, improve operating efficiency, and solve Effects of Filtering Problems in Nonlinear Systems

Active Publication Date: 2017-04-12
NINGBO UNIVERSITY OF TECHNOLOGY
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Problems solved by technology

Then, on the basis of SHCKF, by using the Sage-Husa estimator to estimate the statistical characteristics of the measurement noise in real time, the nonlinear system filtering problem with unknown variance of the measurement noise is effectively solved

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  • Square Root Higher Order Volumetric Kalman Filter Method with Unknown Measurement Noise Variance
  • Square Root Higher Order Volumetric Kalman Filter Method with Unknown Measurement Noise Variance
  • Square Root Higher Order Volumetric Kalman Filter Method with Unknown Measurement Noise Variance

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Embodiment Construction

[0023] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0024] refer to figure 1 , let the state-space model of the nonlinear dynamic system be:

[0025] x(k+1)=f(x(k))+w(k)

[0026] z(k)=h(x(k))+v(k)

[0027] In the above formula, x(k)∈R n is the target state, z(k)∈R m Indicates the measured value; f: R n →R n is a nonlinear state evolution process, h:R n →R m is the corresponding nonlinear measurement mapping; the process noise w(k)∈R n is Gaussian white noise with zero mean, and its variance is Q(k); measurement noise v(k)∈R m is white Gaussian noise with zero mean, but the time-varying variance R(k) is unknown.

[0028] The process noise and measurement noise are assumed to be uncorrelated in the system model. The initial state x(0) of the system means x 0 , the variance is P 0 , and is independent of w(k) and v(k).

[0029] Below, based on the system model, the specific implementation step...

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Abstract

The invention belongs to the field of filtering of nonlinear systems, and particularly relates to a high order volume Kalman filtering method for a square root with the unknown measurement noise variance. The method is used for high order volume Kalman filtering for the square root with the unknown measurement noise variance, on the basis of HCKF, QR decomposition, Choleskey factor updating, the efficient least square method and other matrix decomposition technologies are utilized at first, and the operating efficiency and value stability of the filtering method are improved. On the basis of SHCKF, a Sage-Husa estimator is adopted for estimating the statistical property of measurement noise in real time, and therefore the nonlinear system filtering problem at the measurement noise variance position is effectively solved.

Description

technical field [0001] The invention belongs to the field of filtering of nonlinear systems, and in particular relates to a square root high-order volumetric Kalman filtering method for processing unknown measurement noise variance. Background technique [0002] In many fields, the problem of dynamic estimation of system state has been the focus of people's attention. The state estimation problem of linear Gaussian system generally adopts Kalman filtering method. But when dealing with practical problems, such as target tracking, navigation and positioning, and video surveillance, the state equation or measurement equation of the system usually shows strong nonlinear characteristics. Therefore, it is of great theoretical significance and practical application value to study the state estimation of nonlinear systems, that is, the nonlinear filtering problem. Extended Kalman filtering (EKF) is the most direct and simplest nonlinear filtering method. The EKF method starts wit...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H03H21/00
Inventor 管冰蕾黄晶包蕾骆再飞汤显峰
Owner NINGBO UNIVERSITY OF TECHNOLOGY
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