Unmanned ship integrated navigation method based on self-adaptive federated Kalman filtering

A Kalman filter and Kalman filter technology, which is applied in navigation, surveying and navigation, navigation through speed/acceleration measurement, etc., can solve the problems of filtering accuracy influence and low filtering accuracy

Active Publication Date: 2019-12-17
DALIAN MARITIME UNIVERSITY
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Problems solved by technology

For the field of multi-sensor information fusion, the effect of federated Kalman filtering is the most significant, but its filtering accurac

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  • Unmanned ship integrated navigation method based on self-adaptive federated Kalman filtering
  • Unmanned ship integrated navigation method based on self-adaptive federated Kalman filtering
  • Unmanned ship integrated navigation method based on self-adaptive federated Kalman filtering

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Embodiment Construction

[0173] The present invention will be further described below in conjunction with the accompanying drawings.

[0174] The adaptive federated Kalman filter of the present invention is based on the improvement of the existing federated Kalman filter, because the sub-filter of the federated Kalman filter is a weight matrix adjustment to the state estimated value, and the adaptive federated Kalman filter is the covariance matrix adjustment of the estimated value of the state, so the correction parameters of the two are equivalent.

[0175] If the adaptive factor of the adaptive federated Kalman filter is used to represent the information distribution factor of the federated Kalman filter, the adaptability of the federated Kalman filter can be improved, and the equivalence between the two is proved below.

[0176] Let the estimated filtering value of the sub-filter, the estimated filtering value of the main filter and the global state estimate be X i,k 、X m,k 、X g,k , and the cor...

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Abstract

The invention discloses an unmanned ship integrated navigation method based on self-adaptive federated Kalman filtering. An unmanned ship integrated navigation system is used for navigation and includes an SINS, a GPS, a compass and an embedded processor. Sensor information fusion is performed in the embedded processor by using an adaptive federated Kalman filtering algorithm; and the position, speed and posture information of the unmanned ship is outputted. According to the invention, because of application of an error model and an observation model of unmanned ship SINS/GPS/Compass integrated navigation, the fault interference among subsystems is reduced and the reliability and stability of the unmanned ship navigation system are improved. Since information distribution factors of sub-filters of federated Kalman filtering are designed based on the federated Kalman filtering, the abnormal disturbance of the system can be effectively restrained and the influence of the distribution principle on the filtering precision can be reduced on the premise that the fault-tolerant capability of the system is guaranteed.

Description

technical field [0001] The invention belongs to the field of sensor information fusion, in particular to an unmanned ship integrated navigation method based on adaptive federal Kalman filtering. Background technique [0002] Due to the flexibility and versatility of maritime applications, unmanned ships have a wide range of applications in military production and other aspects. The federated Kalman filter is applied to unmanned ships, unmanned aerial vehicles, and unmanned vehicles. The integrated navigation can detect the failure of the navigation subsystem, and combine the navigation information of the normal subsystem to improve the fault tolerance and adaptability of the system. . Kalman filter, particle filter, federated filter and other methods are widely used in information fusion of integrated navigation sensors. For the field of multi-sensor information fusion, the effect of federated Kalman filtering is the most significant, but its filtering accuracy is affected...

Claims

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Application Information

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IPC IPC(8): G01S5/02G01S19/47G01C21/16G01C17/32
CPCG01S5/0294G01S19/47G01C21/165G01C17/32
Inventor 王宁杨毅李贺付水陈帅
Owner DALIAN MARITIME UNIVERSITY
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