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Random drift modeling method for MEMS (Micro-electromechanical Systems) gyroscope with improved dynamic recursive network

A random drift and dynamic recursion technology, applied in the field of inertial navigation, can solve problems such as slow convergence speed and over-fitting, and achieve the effect of improving accuracy

Inactive Publication Date: 2019-07-23
BEIHANG UNIV
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Problems solved by technology

At present, algorithms such as backpropagation network (BP), radial basis neural network (RBF), and support vector machine (SVM) are widely used in the random drift modeling of gyroscopes, but they are easy to fall into local optimal solutions and the convergence speed Limitations such as slowness and overfitting

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  • Random drift modeling method for MEMS (Micro-electromechanical Systems) gyroscope with improved dynamic recursive network
  • Random drift modeling method for MEMS (Micro-electromechanical Systems) gyroscope with improved dynamic recursive network
  • Random drift modeling method for MEMS (Micro-electromechanical Systems) gyroscope with improved dynamic recursive network

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[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0036] See attached figure 1 , the embodiment of the present invention discloses a MEMS gyroscope random drift modeling method using an improved dynamic recursive network, the method comprising the following steps:

[0037] S1: Establish the output error model of the MEMS gyroscope, analyze the main error sources affecting the gyroscope output, and obtain the two main error sources of high-frequency white noise and low-frequency random drift;

[0038] S2: col...

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Abstract

The invention discloses a random drift modeling method for an MEMS (Micro-electromechanical Systems) gyroscope with an improved dynamic recursive network. According to the method, high-frequency whitenoises and low-frequency random drifts in the output of the MEMS gyroscope are separated by introducing a denoising algorithm, and the random drifts after denoising processing are trained by adoptingthe improved dynamic recursive network; a model relationship between the past and current moments of a non-stationary drift sequence is established, and the output layer node feedback is increased toimprove a network structure, so that the real-time prediction of the random drift change trend of the gyroscope is realized, and the precision of an MEMS inertial navigation system is effectively improved.

Description

technical field [0001] The invention relates to the technical field of inertial navigation, in particular to a MEMS gyroscope random drift modeling method using an improved dynamic recursive network. Background technique [0002] At present, as an important branch in the field of inertial navigation, the inertial navigation system based on micro-electromechanical systems (MEMS) has the advantages of small size, low cost, easy installation, light weight, high reliability and impact resistance, etc. Navigation and other fields have broad application prospects. However, affected by the manufacturing process and the use environment, MEMS inertial devices have lower precision than traditional inertial devices. Among them, the low signal-to-noise ratio of MEMS gyroscopes has become one of the main factors restricting the improvement of the accuracy of MEMS inertial navigation systems. MEMS gyro errors are mainly divided into two parts: deterministic error and random drift. The de...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/16G01C21/20
CPCG01C21/16G01C21/20
Inventor 高爽宋来亮张若愚纪少文李星
Owner BEIHANG UNIV
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