IMU data denoising method based on recurrent neural network

A technology of cyclic neural network and neural network, applied in the direction of neural learning method, biological neural network model, neural architecture, etc., can solve problems such as poor effect and poor precision, and achieve improved effect, good simulation noise characteristics, and good robustness The effect on generalization ability

Active Publication Date: 2020-01-24
SOUTHEAST UNIV
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Problems solved by technology

Traditional methods mostly use signal analysis methods such as median filtering and wavelet transform for modeling, but from the perspective of data, the sensor data we obtain...

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  • IMU data denoising method based on recurrent neural network
  • IMU data denoising method based on recurrent neural network
  • IMU data denoising method based on recurrent neural network

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Embodiment Construction

[0035] The present invention will be further described below in conjunction with specific embodiment and description accompanying drawing, and those skilled in the art can understand that, unless otherwise defined, all terms (comprising technical terms and scientific terms) used herein have the same meaning in the field to which the present invention belongs The same meaning is commonly understood by those of ordinary skill. It should also be understood that terms such as those defined in commonly used dictionaries should be understood to have a meaning consistent with the meaning in the context of the prior art, and unless defined as herein, are not to be interpreted in an idealized or overly formal sense Explanation. The preferred embodiments described here are only used to illustrate and explain the present invention, not to limit the present invention.

[0036] Such as Figure 1-2 Shown, a kind of IMU data denoising method based on neural network of the present embodiment,...

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Abstract

The invention discloses an IMU data denoising method based on a recurrent neural network, and the method comprises the steps: building an IMU data time sequence sample data set, building the recurrentneural network, and carrying out the training optimization to obtain a weight file; and finally obtaining a deployment model. According to the method, a time sequence prediction method is introducedinto measurement data of a micro-electromechanical system inertial measurement unit (MEMS IMU), the measurement data is regarded as a time sequence, a long-short-term memory network (LSTM) is designedto extract a nonlinear relation of the time sequence, and the effect of removing noise is achieved through a sequence prediction method. Compared with a traditional denoising method based on a statistical model, the algorithm has better robustness and generalization ability, and the randomness of original data is not changed. The method has important application value in the aspects of inertial technology, integrated navigation and the like.

Description

technical field [0001] The invention belongs to the field of micro-inertial instrument detection and deep learning, and relates to an IMU data denoising method based on a cyclic neural network. Background technique [0002] The MEMS inertial measurement unit (IMU) is a device that measures the three-axis angular velocity and acceleration of an object. It can independently calculate the position, heading and velocity of the object through time integration without being affected by the surrounding environment. Due to its low price, size advantage, and independent measurement, it has been widely used in integration with the Global Navigation Satellite System (GNSS) to provide integrated navigation solutions, and it can independently output positioning information when the satellite signal is weak and blocked. However, since the original measurement of the inertial measurement unit contains various nonlinear and random errors, and the integral will become larger and larger over ...

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Application Information

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IPC IPC(8): G06N3/04G06N3/08G06K9/00
CPCG06N3/084G06N3/044G06N3/045G06F2218/04
Inventor 金世俊杨凤高鹏举
Owner SOUTHEAST UNIV
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