CN111895986A - MEMS gyroscope original output signal noise reduction method based on LSTM neural network - Google Patents

MEMS gyroscope original output signal noise reduction method based on LSTM neural network Download PDF

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CN111895986A
CN111895986A CN202010622709.0A CN202010622709A CN111895986A CN 111895986 A CN111895986 A CN 111895986A CN 202010622709 A CN202010622709 A CN 202010622709A CN 111895986 A CN111895986 A CN 111895986A
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王燕妮
翟会杰
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Abstract

The invention discloses a method for reducing noise of an original output signal of an MEMS gyroscope based on an LSTM neural network, which comprises the following steps of; 1) acquiring original output data of the MEMS gyroscope; 2) preprocessing data; 3) constructing a noise reduction model, and constructing an LSTM neural network model based on a Keras framework; 4) noise reduction treatment, namely training a neural network model by using training set data, storing the model, and performing prediction treatment by using test set data as input of the trained model; 5) and evaluating the noise reduction result by evaluating the mean square error of the noise reduction evaluation index. The method adopts the memory function characteristic of the LSTM neural network model, is easier to obtain the potential rule of the data sequence output by the gyroscope, and has obvious noise reduction effect on the original output signal of the MEMS gyroscope.

Description

MEMS gyroscope original output signal noise reduction method based on LSTM neural network
Technical Field
The invention belongs to the field of digital signal processing, relates to noise reduction of an output signal of an MEMS (micro-electromechanical system) gyroscope, and particularly relates to a noise reduction method of an original output signal of the MEMS gyroscope based on an LSTM (least Square-spline) neural network.
Background
At present, the method for reducing the noise of the output data of the MEMS gyroscope comprises hardware design and software programming, wherein the software design mainly establishes a model, and corrects errors through a proper filter. Common modeling methods are:
1. the sliding autoregressive modeling method is used for building a sliding autoregressive model based on time sequence analysis, but random drift errors cannot be accurately described if the order of the sliding autoregressive model is too high.
2. In the wavelet analysis-based modeling method, wavelet transformation energy conversion is performed with different resolution analysis according to signal frequency, but accurate modeling cannot be performed if wavelet basis functions are unreasonably selected.
3. According to the modeling method of the MEMS gyroscope data manual, the modeling accuracy is high but the modeling method is not suitable for all MEMS gyroscopes.
4. The Kalman filter becomes a common MEMS gyroscope noise reduction algorithm due to high resolving speed and good real-time performance, but the dispersion of the filter can be caused by the mismatching of the model, and the adaptability to external input is poor.
The random drift error of the MEMS gyroscope can change along with the change of the external environment, and an accurate error model is difficult to establish without a definite rule. Therefore, the identification and noise reduction method for the random error of the output data of the MEMS gyroscope is one of the subjects of attention of those skilled in the art.
Disclosure of Invention
In view of the above-mentioned drawbacks or shortcomings of the prior art, an object of the present invention is to provide a method for reducing noise of an original output signal of a MEMS gyroscope based on an LSTM neural network.
In order to realize the task, the invention adopts the following technical solution:
a method for reducing noise of an original output signal of a MEMS gyroscope based on an LSTM neural network is characterized by comprising the following steps:
step 1), acquiring original output data of the MEMS gyroscope
Firstly, an MEMS gyroscope data acquisition system mainly comprising an MEMS gyroscope module, upper computer software, a computer and a power supply is built, the system uploads acquired data information to the upper computer in a serial port communication mode, the upper computer software acquires MEMS gyroscope data mainly through an RS-422 serial port communication protocol, and balanced transmission adopts unidirectional/non-reversible transmission and a transmission line with an enabling end or without the enabling end; the MEMS gyroscope module is connected with a USB interface of a computer through an RS-422 serial port communication protocol-USB interface, and upper computer software acquires original output data of the MEMS gyroscope through an operating system call interface of the control computer;
step 2), data preprocessing
Reading the gyroscope output sample data obtained in the step 1), performing sample data format conversion, performing normalization processing on the gyroscope data by adopting a maximum and minimum normalization method, mapping the sample data to a range between [0 and 1], and further performing normalization on the sample data set according to a ratio of 7: 3, dividing training set data and test set data according to the proportion;
step 3): establishing a noise reduction model
Constructing an LSTM neural network model based on a Keras framework, wherein the LSTM neural network model comprises 1 layer of LSTM and 3 layers of Dense, and setting the structure and model parameters of the LSTM neural network model;
step 4): noise reduction processing
Carrying out model training on training set data through an LSTM neural network model, carrying out parameter adjustment through a loss function until the model is converged, and storing the model; performing noise reduction treatment by taking the test set data as the input of the model;
step 5): noise reduction result evaluation
And evaluating through the mean square error of the noise reduction evaluation index and the noise reduction result comparison graph.
According to the invention, the process of outputting the sample data to the gyroscope in the step 2) and carrying out the format conversion of the sample data is as follows; starting from the statistical view, processing the random error of the gyroscope into a group of random variables of an ordered time sequence, and converting the gyroscope data noise reduction problem into time sequence data prediction.
Further, the model parameter formula in step 3) includes:
forget door ft＝σ(Wf[xt,ht-1]+bf) Wherein f ist∈[0,1]Selection weight of node pair representing time t to cell memory at time t-1, bfBias term for forgetting gate, ht-1Implicit state information, non-linear function, representing a t-1 node
Figure BDA0002563597440000031
Input door it＝σ(Wi[xt,ht-1]+bi) Wherein it∈[0,1]The selection weight of the node at time t to the current node information, biFor the bias term of the input gate, a non-linear function
Figure BDA0002563597440000032
Current node input information
Figure BDA0002563597440000033
Wherein
Figure BDA0002563597440000034
Is a bias term;
outputting the memory information Ct＝ft·Ct-1+it·CinIn which C ist-1Memory information representing a t-1 node;
output gate ot＝σ(Wo[xt,ht-1]+bo) Wherein o ist∈[0,1]Selection weight of node cell memory information representing time t, boIs the bias term of the output gate;
hidden layer state h at time tt＝ot·tanh(Ct)；
xtRepresenting the input vector, h, of the LSTM neural network node at time ttHidden layer state information representing LSTM neural network nodes at the time t;
Figure BDA0002563597440000035
tan h is a hyperbolic tangent activation function; wfWeight matrix for forgetting gate, WiIs a weight matrix of input gates, WoA weight matrix representing the information to be updated.
The method for reducing the noise of the MEMS gyroscope original output signal based on the LSTM neural network adopts the memory function characteristic of the LSTM neural network model, is easier to obtain the potential rule of the gyroscope output data sequence, and has obvious noise reduction effect on the MEMS gyroscope original output signal.
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FIG. 1 is a flow chart of the method for reducing noise of the MEMS gyroscope raw output signal based on the LSTM neural network.
FIG. 2 is a block diagram of a MEMS gyroscope data acquisition flow;
FIG. 3 is a comparison graph of noise reduction results, wherein (a) is a comparison graph of noise reduction results of training set data, and (b) is a comparison graph of noise reduction results of test set data.
The present invention will be described in further detail with reference to the following drawings and examples.
Detailed Description
Referring to fig. 1, the embodiment provides a method for reducing noise of an original output signal of a MEMS gyroscope based on an LSTM neural network, which includes the following specific detailed implementation steps:
step 1) obtaining original output data of MEMS gyroscope
Preparing a test environment: in the embodiment, a zero-position signal of the MEMS, namely an output signal when the sensing angular velocity is in a zero state, is selected, and the selected test method is a static test. Meanwhile, in order to improve the reliability of data and reduce the influence of environment and temperature change on output data, the MEMS gyroscope module is kept to be tested at a standard room temperature of 25 ℃ (the MEMS gyroscope module is placed in a temperature control turntable with the temperature set to be 25 ℃), and data collection is started after the gyroscope is kept in a static state for 30 min.
Building a data acquisition system: the system mainly comprises an MEMS gyroscope module, a power supply, upper computer software, a computer and the like; the acquired data information is uploaded to an upper computer in a serial port communication mode, the upper computer software acquires MEMS gyroscope data mainly through an RS-422 serial port communication protocol, and balanced transmission adopts a transmission line which is unidirectional/non-reversible and has an enabling end or no enabling end. The MEMS gyroscope module is connected with a USB interface of a computer through an RS-422 serial port communication protocol-USB interface, and the upper computer software acquires MEMS gyroscope data by controlling the operating system of the computer to call the interface.
Collecting data: as shown in fig. 2, a serial port is set, a baud rate is set to 921600, a storage path is selected, a power supply is powered on, gyroscope data transmission is started, a PC receives the data, then, the data is checked and resolved, the data is stored in a cache area of a computer, upper computer software calls a computer operating system to obtain the data received by the cache area, and the MEMS gyroscope data is output through a resolution protocol.
Step 2) data preprocessing
In this embodiment, the data preprocessing mainly includes preparing data to enter a neural network layer, including data format change and data normalization processing of a gyroscope.
Reading MEMS gyroscope sample data, transforming the format of the sample data, converting the gyroscope sample data into a time series data format, and expressing sampling points in the sequence sample as follows: x is the number ofi＝(x1,…,xi,…,xn) The method is suitable for entering a neural network layer to perform time series prediction.
Carrying out normalization processing and dividing training set data and test set data; the purpose of normalization processing is to eliminate the influence of dimensions, avoid training a model due to the influence of a large number of dimensions in the neural network training process, perform normalization processing by adopting a maximum and minimum normalization method, and map sample data between [0 and 1 ]. Dividing training set data and test set data, and preparing data to enter a neural network layer;
maximum and minimum normalization method formula:
Figure BDA0002563597440000051
and after normalization processing, the sample data set is divided into 7 parts: and 3, dividing training set data and test set data according to the proportion to prepare for entering a neural network layer.
Step 3) establishing a noise reduction model, and constructing an LSTM neural network model based on a keras frame
An LSTM neural network model is constructed, an LSTM (Long ShortMemoryNetwork) long and short memory neural network is a special recurrent neural network, is proposed for improving the problem of gradient disappearance (influence factors of past time on the current time exist among different hidden layers, but the influence is weakened along with the increase of time span), is good at exploring the nonlinear relation among time series data, and is proved to be more effective for the time series prediction problem; the MEMS gyroscope is used as an actually existing physical system, state continuity exists in output at adjacent time points, after gyroscope data is processed into time sequence data, certain correlation exists between the output at the current moment and the output at the adjacent moment, and the time sequence can be considered to reflect the internal law of the system, so that an LSTM neural network model is applied to noise reduction of an original output signal of the MEMS gyroscope, and further output of the system is predicted by a fitting sequence modeling method, so that a better noise reduction effect on the output data of the MEMS gyroscope is achieved.
The LSTM neural network model processes long-term memory by introducing a 'gate structure' to enable the model to have a long-term selective memory mode, the special structural design enables the model to be more suitable for predicting or processing time-based series data, and the influence of previous information on current information is controlled by the following three 'gate' structures, namely:
(1) a forget gate that determines which cell states should be forgotten;
(2) an input gate that determines which new states should be added;
(3) an output gate for determining an output based on the current state and the current input;
the formula for the "door structure" model includes:
forget door ft＝σ(Wf[xt,ht-1]+bf) Wherein f ist∈[0,1]Selection weight of node pair representing time t to cell memory at time t-1, bfBias term for forgetting gate, ht-1Implicit state information, non-linear function, representing a t-1 node
Figure BDA0002563597440000061
Input door it＝σ(Wi[xt,ht-1]+bi) Wherein i ist∈[0,1]The selection weight of the node at time t to the current node information, biFor the bias term of the input gate, a non-linear function
Figure BDA0002563597440000062
Current node input information
Figure BDA0002563597440000063
Wherein,
Figure BDA0002563597440000064
is a bias term;
outputting the memory information Ct＝ft·Ct-1+it·CinWherein, Ct-1Memory information representing a t-1 node;
output gate ot＝σ(Wo[xt,ht-1]+bo) Wherein o ist∈[0,1]Selection weight of node cell memory information representing time t, boIs the bias term of the output gate;
hidden layer state h at time tt＝ot·tanh(Ct)；
Wherein x istRepresenting the input vector, h, of the LSTM neural network node at time ttHidden layer state information representing LSTM neural network nodes at the time t;
Figure BDA0002563597440000065
tan h is a hyperbolic tangent activation function; wfWeight matrix for forgetting gate, WiIs a weight matrix of input gates, WoA weight matrix representing the information to be updated.
Configuring a training model, wherein the training model comprises a 1-layer LSTM neural network layer and a 3-layer Dense layer, setting the structure and the hyper-parameters of the LSTM neural network model, and mainly comprises input data dimensionality, output data dimensionality, the number of nodes of each layer, an excitation function, a learning rate, a loss function, an optimization function, batch processing quantity, iteration times and the like.
The input data dimension of the LSTM layer is 1, the output data dimension is 1, the activation function of the input layer is relu, the number of nodes of each layer is 128, and the learning rate is 0.001. The excitation function is a tanh function and a sigmoid function, the loss function loss is mse (mean square error), the optimization function is an Adam function, the batch processing quantity is 1, and the iteration number is 100;
the tanh function is formulated as:
Figure BDA0002563597440000071
the sigmoid function is formulated as:
Figure BDA0002563597440000072
step 4) noise reduction treatment
The main idea of noise reduction is to use a long-short term memory network LSTM neural network model to predict a time sequence, the LSTM model has strong learning capacity, the motion characteristics of gyroscope output data are learned from a gyroscope training data sequence, a loss function loss is minimized through an optimizer, and parameter adjustment is carried out to enable the model to be converged.
The LSTM neural network model training process is that 70% of gyroscope sample data is used as training set data to be input into an input layer of the LSTM neural network model, obtained weight and bias of the input layer are input into an output layer of the LSTM neural network model, the result passes through a 3-layer Dense layer through a full-connection network, the output result is a noise reduction result of the training model, and the model is stored.
And further, inputting the residual 30% of gyroscope sample data serving as data of a test set into the trained long-short term memory network LSTM model, and predicting further output of the gyroscope system to realize the noise reduction of the gyroscope data.
Step 5) noise reduction result evaluation
Evaluating through a noise reduction evaluation index; the objective evaluation uses Mean Squared Error (MSE) as an evaluation index, and the formula is as follows:
Figure BDA0002563597440000081
in the formula (f)iIs the predicted value, yiThe value is an observed value.
In this embodiment, the mean square error MSE after 100 iterations is reduced from 0.2046 to 0.0173, which is 90% reduction in mean square error.
FIG. 3 shows a comparison graph of noise reduction results, where (a) is a comparison graph of noise reduction results of training set data, and (b) is a comparison graph of noise reduction results of test set data.

Claims (3)

1. A method for reducing noise of an original output signal of a MEMS gyroscope based on an LSTM neural network is characterized by comprising the following steps:
step 1), acquiring original output data of the MEMS gyroscope
Firstly, an MEMS gyroscope data acquisition system mainly comprising an MEMS gyroscope module, upper computer software, a computer and a power supply is built, the system uploads acquired data information to the upper computer in a serial port communication mode, the upper computer software acquires MEMS gyroscope data mainly through an RS-422 serial port communication protocol, and balanced transmission adopts unidirectional/non-reversible transmission and a transmission line with an enabling end or without the enabling end; the MEMS gyroscope module is connected with a USB interface of a computer through an RS-422 serial port communication protocol-USB interface, and upper computer software acquires original output data of the MEMS gyroscope through an operating system call interface of the control computer;
step 2), data preprocessing
Reading the gyroscope output sample data obtained in the step 1), carrying out sample data format conversion, carrying out normalization processing on the gyroscope data by adopting a maximum and minimum normalization method, mapping the sample data to a range between [0 and 1], and further dividing a sample data set into training set data and test set data according to a ratio of 7: 3;
step 3): establishing a noise reduction model
Constructing an LSTM neural network model based on a Keras framework, wherein the LSTM neural network model comprises 1 layer of LSTM and 3 layers of Dense, and setting a structure and a model parameter formula of the LSTM neural network model;
step 4): noise reduction processing
Carrying out model training on training set data through an LSTM neural network model, carrying out parameter adjustment through a loss function until the model is converged, and storing the model; performing noise reduction treatment by taking the test set data as the input of the model;
step 5): noise reduction result evaluation
And evaluating through the mean square error of the noise reduction evaluation index and the noise reduction result comparison graph.
2. The method of claim 1, wherein in step 2), the gyroscope outputs sample data, and the sample data format is transformed; starting from the statistical view, processing the random error of the gyroscope into a group of random variables of an ordered time sequence, and converting the gyroscope data noise reduction problem into time sequence data prediction.
3. The method of claim 1, wherein the model parameter formula in step 3) comprises:
forget door ft＝σ(Wf[xt，ht-1]+bf) Wherein f ist∈[0，1]Selection weight of node pair representing time t to cell memory at time t-1, bfBias term for forgetting gate, ht-1Implicit state information, non-linear function, representing a t-1 node
Figure FDA0002563597430000021
Input door it＝σ(Wi[xt，ht-1]+bi) Wherein it∈[0，1]The selection weight of the node at time t to the current node information, biFor the bias term of the input gate, a non-linear function
Figure FDA0002563597430000022
Current node input information
Figure FDA0002563597430000023
Wherein
Figure FDA0002563597430000024
Is a bias term;
outputting the memory information Ct＝ft·Ct-1+it·CinIn which C ist-1Memory information representing a t-1 node;
output gate ot＝σ(Wo[xt，ht-1]+bo) Wherein o ist∈[0，1]Selection weight of node cell memory information representing time t, boIs the bias term of the output gate;
hidden layer state h at time tt＝ot·tanh(Ct)；
xtRepresenting the input vector, h, of the LSTM neural network node at time ttHidden layer state information representing LSTM neural network nodes at the time t;
Figure FDA0002563597430000025
tan h is a hyperbolic tangent activation function; wfWeight matrix for forgetting gate, WiIs a weight matrix of input gates, WoA weight matrix representing the information to be updated.
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113447021A (en) * 2021-07-15 2021-09-28 北京理工大学 MEMS inertial navigation system positioning enhancement method based on LSTM neural network model
CN113838471A (en) * 2021-08-10 2021-12-24 北京塞宾科技有限公司 Noise reduction method and system based on neural network, electronic device and storage medium
CN118035652A (en) * 2024-04-10 2024-05-14 东北石油大学三亚海洋油气研究院 Processing method and device for measurement while drilling data and electronic equipment
CN118468229A (en) * 2024-07-10 2024-08-09 北京理工大学 Gyro array fusion system and method based on improved LMD and LSTM

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107239859A (en) * 2017-06-05 2017-10-10 国网山东省电力公司电力科学研究院 The heating load forecasting method of Recognition with Recurrent Neural Network is remembered based on series connection shot and long term
CN108364028A (en) * 2018-03-06 2018-08-03 中国科学院信息工程研究所 A kind of internet site automatic classification method based on deep learning
CN109389059A (en) * 2018-09-26 2019-02-26 华南理工大学 A kind of P300 detection method based on CNN-LSTM network
US20190135616A1 (en) * 2017-11-03 2019-05-09 University Of Dayton Deep learning software enhanced microelectromechanical systems (mems) based inertial measurement unit (imu)
CN110728357A (en) * 2019-09-19 2020-01-24 东南大学 IMU data denoising method based on recurrent neural network
CN111238462A (en) * 2020-01-19 2020-06-05 湖北三江航天红峰控制有限公司 LSTM fiber-optic gyroscope temperature compensation modeling method based on deep embedded clustering

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107239859A (en) * 2017-06-05 2017-10-10 国网山东省电力公司电力科学研究院 The heating load forecasting method of Recognition with Recurrent Neural Network is remembered based on series connection shot and long term
US20190135616A1 (en) * 2017-11-03 2019-05-09 University Of Dayton Deep learning software enhanced microelectromechanical systems (mems) based inertial measurement unit (imu)
CN108364028A (en) * 2018-03-06 2018-08-03 中国科学院信息工程研究所 A kind of internet site automatic classification method based on deep learning
CN109389059A (en) * 2018-09-26 2019-02-26 华南理工大学 A kind of P300 detection method based on CNN-LSTM network
CN110728357A (en) * 2019-09-19 2020-01-24 东南大学 IMU data denoising method based on recurrent neural network
CN111238462A (en) * 2020-01-19 2020-06-05 湖北三江航天红峰控制有限公司 LSTM fiber-optic gyroscope temperature compensation modeling method based on deep embedded clustering

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
JIANG CHANGHUI,ET.AL: "A MEMS IMU De-Noising Method Using Long Short Term Memory Recurrent Neural Networks (LSTM-RNN)", 《SENSORS》 *
XUGUANG CAO: "The Realization Methods of Fuzzy Sensor Linguistic Symbol Description" *
马艺翔: "深度学习在多因子选股交易中的应用研究", 《中国优秀硕士学位论文全文数据库经济与管理科学辑》 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113447021A (en) * 2021-07-15 2021-09-28 北京理工大学 MEMS inertial navigation system positioning enhancement method based on LSTM neural network model
CN113838471A (en) * 2021-08-10 2021-12-24 北京塞宾科技有限公司 Noise reduction method and system based on neural network, electronic device and storage medium
CN118035652A (en) * 2024-04-10 2024-05-14 东北石油大学三亚海洋油气研究院 Processing method and device for measurement while drilling data and electronic equipment
CN118468229A (en) * 2024-07-10 2024-08-09 北京理工大学 Gyro array fusion system and method based on improved LMD and LSTM
CN118468229B (en) * 2024-07-10 2024-09-10 北京理工大学 Gyro array fusion system and method based on improved LMD and LSTM

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Application publication date: 20201106