Neural network assisted integrated navigation method for underwater vehicle

An underwater vehicle and neural network technology, applied in navigation, navigation through speed/acceleration measurement, surveying and navigation, etc., can solve problems such as degradation of navigation accuracy and long repair time of navigation system

Active Publication Date: 2015-02-04
SOUTHEAST UNIV
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Problems solved by technology

However, if the repair time of the failed navigation system is relatively long, and there is

Method used

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  • Neural network assisted integrated navigation method for underwater vehicle
  • Neural network assisted integrated navigation method for underwater vehicle
  • Neural network assisted integrated navigation method for underwater vehicle

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Embodiment

[0079] In the simulation experiment, the initial speed of the underwater vehicle is 40kn; the initial pitch, roll and heading angles are: 0°, 0° and 45° respectively; the initial longitude and latitude are 165° and 32°; the gyroscope: x, The random drift in the y and z directions is 0.04° / h, and the constant drift is 0.04° / h; accelerometer: the random bias is 50ug, and the constant bias is 50ug; the measurement error covariance of DVL is 0.4m / s, the measurement error covariance of MCP is 0.3°, and the measurement error covariance of TAN is: 50m. Since the working principle of the three subsystems introduced into the neural network is the same and there are many combinations of failures, taking DVL failure as an example, the disconnection time of 60s, 120s, 180s and 300s is set as the DVL failure time, as shown in the table As shown in 1, the simulation time length is 5100s, and the simulation result diagram is given, as shown in Figure 7 , Figure 8 with Figure 9 shown. ...

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Abstract

The invention discloses a neural network assisted integrated navigation method for an underwater vehicle. The neural network assisted integrated navigation method is implemented by use of strapdown inertial navigation system (SINS), a Doppler velocity log (DVL), a magnetic compass pilot (MCP) and a terrain aided navigation system (TAN), wherein the integrated navigation is completed by use of a decentralized filter structure of Kalman filtering and a fault-tolerant method, assisted by a radial basis function neutral network (RBFNN). In a fault-free time period, RBFNN is in an online learning model, the observed quantity difference between the SINS and each auxiliary system is taken as the expected output of the RBFNN, and the output fb of an accelerometer after error compensation and the output shown in the specification of a gyroscope are taken as the inputs of the RBFNN; when a sub-system composed of the SINS serving as a reference system and each auxiliary system is out of order, an RBFNN prediction mode is immediately activated, and the predicted output is taken as the measurement input of a corresponding sub-filter. Compared with the SINS mode out of order, the RBFNN mode has the advantages that the navigation accuracy is improved; especially when the fault recovery time is relatively long, the improvement of the navigation accuracy of the RBFNN mode is particularly obvious.

Description

technical field [0001] The invention relates to the technical field of underwater navigation, in particular to design a method capable of assisting integrated navigation and positioning when a certain subsystem of an underwater vehicle fails. Background technique [0002] As an important means of exploring the ocean, underwater vehicles have been widely used in both military and civilian applications. With the continuous development of underwater vehicles and the continuous expansion of their working range, their underwater navigation and positioning Higher requirements require that the navigation system it is equipped with can not only provide high-precision navigation and positioning, but also have high reliability. For this reason, it is necessary to meet the above requirements through information fusion of multiple navigation sensors. At present, the underwater integrated navigation system mostly adopts the federated filter structure with good real-time performance, flex...

Claims

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Application Information

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IPC IPC(8): G01C21/00G01C21/16
CPCG01C21/165G01C21/18
Inventor 徐晓苏周峰张涛汤郡郡田泽鑫金博楠
Owner SOUTHEAST UNIV
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