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Marginalized cubature Kalman filter (CKF)-based gravity aided navigation method

A technology for assisting navigation and gravity, applied in navigation, mapping and navigation, navigation calculation tools, etc., can solve the problems of high complexity and poor real-time performance of gravity-assisted navigation

Inactive Publication Date: 2014-02-26
HARBIN ENG UNIV
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Problems solved by technology

[0004] The purpose of the embodiments of the present invention is to provide a method for gravity-assisted navigation based on marginalized CKF, which aims to solve the problems of high complexity and poor real-time performance of the gravity-assisted navigation method

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  • Marginalized cubature Kalman filter (CKF)-based gravity aided navigation method

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[0050] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0051] The application principle of the present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0052] Such as figure 1 As shown, the method for assisted navigation based on marginalized CKF gravity in the embodiment of the present invention includes the following steps:

[0053] S101: Obtain the position information (longitude and latitude) output by the inertial navigation system and the gravity anomaly value measured by the gravimeter in real time, and ensure that the position information indicated by the inertial navigation system and the gravity anoma...

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Abstract

The invention discloses a marginalized cubature Kalman filter (CKF)-based gravity aided navigation method which comprises the following steps: acquiring position information output by an inertial navigation system and a gravity anomaly value measured by a gravity meter in real time; establishing an output error model of an inertial device and an error equation of the inertial navigation system, and determining the state quantity and measurement quantity; dividing the iteration process into state update and measurement update; taking the observed quantity at each moment in the filtering process as a gravity anomaly value measured by the gravity meter in real time, and estimating an observation predicted value, an autocorrelation covariance matrix, a mutual correlation covariance matrix and Kalman gain; calculating a state estimation value and a state error covariance estimation value according to the obtained state and observation predicted value; correcting the inertial navigation system through output correction according to the obtained navigation parameter error of the inertial navigation system. According to the method, the state estimation is effectively and rapidly realized, filtering is performed in a sampling point form by virtue of a gravity anomaly reference map, an error brought by model inaccuracy is avoided, and the gravity aided navigation can be accurately and rapidly realized.

Description

technical field [0001] The invention belongs to the technical field of gravity-assisted navigation, and in particular relates to a method for gravity-assisted navigation based on marginalized CKF. Background technique [0002] With the development of modern anti-submarine technology, the survivability of submarines and other underwater vehicles has been seriously threatened. Relying solely on inertial navigation systems for underwater navigation can no longer meet the accuracy requirements of modern submarine navigation. The combined use of the inertial navigation system reduces the concealment of the submarine. With the maturity of gravity measurement technology, the use of gravity anomaly to assist navigation is gradually applied. The concealment of the submersible undoubtedly has very important tactical value. [0003] For the integrated navigation system composed of gravity anomaly-assisted inertial navigation, recursive optimal estimation filtering is one of the key te...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/16G01C21/20
CPCG01C21/165G01C21/20G01C25/005
Inventor 高伟赵博周广涛姜鑫夏秀玮于春阳郝勤顺孙艳涛刘凤
Owner HARBIN ENG UNIV
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