Quartz flexible accelerometer temperature compensation optimization method and circuit operation method

Abstract

The invention provides a quartz flexible accelerometer temperature compensation optimization method and a circuit operation method. The temperature compensation optimization method comprises the following steps: constructing a regression model of a quartz flexible accelerometer by using a regression equation; constructing a Gaussian regression model of the quartz flexible accelerometer based on the regression model of the quartz flexible accelerometer; and performing temperature compensation on the accelerometer based on the Gaussian regression model of the quartz flexible accelerometer. According to the invention, errors of the quartz flexible accelerometer caused by environment temperature factors can be effectively reduced. Compared with the prior art, the technical scheme of the invention can solve the technical problem that the acceleration value cannot meet the system precision requirement due to the temperature influence in the prior art.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of sensor and precision measurement, and particularly relates to a quartz flexible accelerometer temperature compensation optimization method and a circuit operation method. BACKGROUND

[0002] The quartz flexible accelerometer is used to measure the acceleration value of a carrier, and has the typical characteristics of high sensitivity and strong stability. The quartz flexible accelerometer is widely applied in the navigation control system of aviation, aerospace and various weapon equipment.

[0003] The acceleration value is once integrated into velocity and twice integrated into position, and the output error of the accelerometer will seriously affect the longitude of the navigation control system. The longitude error of the quartz flexible accelerometer is not only caused by the manufacturing process, internal structure and other characteristics of the quartz flexible accelerometer itself, but also caused by the influence of the ambient temperature. When the ambient temperature changes by a large amplitude (about 100 DEG C), the scale and bias value of the accelerometer will be greatly offset, and the system precision requirement cannot be met. SUMMARY

[0004] The application aims to solve at least one of the technical problems in the prior art.

[0005] According to an aspect of the application, a quartz flexible accelerometer temperature compensation optimization method is provided, and the method comprises the following steps:

[0006] A regression model of the quartz flexible accelerometer is constructed by using a regression equation: f(x) = X T θ, Y = f(x) + ξ, wherein X is a two-dimensional vector composed of the temperature and the temperature change rate, Y is the scale or bias value corresponding to X, f(x) is a Gaussian process regression prediction function relationship, θ is a one-dimensional vector of weighted values, and ξ is Gaussian noise and obeys a normal distribution;

[0007] A Gaussian regression model of the quartz flexible accelerometer is constructed based on the regression model of the quartz flexible accelerometer: wherein f * is the predicted scale or bias value of the function f at X * , X * is an input vector, is the mean of the prediction distribution, cov(f * ) is the mean square error of the prediction distribution, K(X,X * ) = X T ∑X * , K(X,X) = X T ∑X, σ n is the standard deviation of the Gaussian noise, and I is a unit matrix;

[0008] Based on the real-time voltage collected by the circuit, the acceleration value after temperature compensation is obtained based on the Gaussian regression model of the quartz flexible accelerometer, thus completing the temperature compensation optimization of the quartz flexible accelerometer.

[0009] Furthermore, according to a = (U acq -K0) / K1 obtains the temperature-compensated acceleration value, where a is the temperature-compensated acceleration value, and U acq The circuit acquires voltage in real time. K0 is the bias value after temperature compensation, and K1 is the scale after temperature compensation.

[0010] According to another aspect of the present invention, an accelerometer circuit operation method is provided, which employs the quartz flexible accelerometer temperature compensation optimization method described above to perform temperature compensation on the accelerometer.

[0011] Furthermore, the accelerometer circuit operation method specifically includes: real-time voltage acquisition and PID closed-loop control; real-time acquisition of acceleration values ​​and temperature, and calculation of temperature change rate; temperature compensation of acceleration values ​​based on the real-time voltage and temperature change rate acquired by the circuit using the Gaussian regression model of the quartz flexible accelerometer; and periodic uploading of temperature-compensated acceleration values ​​to the host computer.

[0012] This invention provides a temperature compensation optimization method and circuit operation method for a quartz flexible accelerometer. The method first constructs a regression model of the quartz flexible accelerometer, and based on this, constructs a Gaussian regression model of the accelerometer to perform temperature compensation. This invention performs temperature compensation for the accelerometer from an algorithmic perspective, which can effectively reduce the error of the quartz flexible accelerometer caused by environmental temperature factors. Compared with the prior art, the technical solution of this invention can solve the technical problem that the acceleration value cannot meet the system accuracy requirements due to temperature influence in the prior art. Attached Figure Description

[0013] The accompanying drawings, which form part of this specification, are provided to further illustrate embodiments of the invention and, together with the textual description, explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0014] Figure 1 A flowchart illustrating an accelerometer circuit operation method according to a specific embodiment of the present invention is shown. Detailed Implementation

[0015] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0017] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention. It should also be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0018] A specific embodiment of the present invention provides a method for optimizing temperature compensation of a quartz flexible accelerometer, the method comprising:

[0019] A regression model for a quartz flexible accelerometer is constructed using the regression equation: f(x) = X T θ,Y=f(x)+ξ, where X is a two-dimensional vector composed of temperature and temperature change rate, Y is the scale or bias corresponding to X, f(x) is the regression prediction function relationship of Gaussian process, θ is a weighted one-dimensional vector, and ξ is Gaussian noise that follows a normal distribution.

[0020] A Gaussian regression model for a quartz flexible accelerometer is constructed based on the regression model of the quartz flexible accelerometer: Among them, f * For the function f in X * The prediction scale or bias at X * For the input vector, To predict the mean of the distribution, cov(f) * ) represents the mean square error of the predicted distribution. K(X,X * ) = X T ∑X * K(X,X)=X T ∑X, σ n Let I be the standard deviation of the Gaussian noise, and I be the identity matrix.

[0021] Based on the real-time voltage collected by the circuit, the acceleration value after temperature compensation is obtained based on the Gaussian regression model of the quartz flexible accelerometer, thus completing the temperature compensation optimization of the quartz flexible accelerometer.

[0022] This configuration method provides an optimization approach for temperature compensation in quartz flexible accelerometers. The method first constructs a regression model for the quartz flexible accelerometer, and then builds a Gaussian regression model based on this model to perform temperature compensation. This invention provides temperature compensation for the accelerometer algorithmically, effectively reducing errors caused by environmental temperature factors.

[0023] The following section provides a detailed introduction to the optimization method for temperature compensation of quartz flexible accelerometers.

[0024] First, in this invention, a regression model for a quartz flexible accelerometer is established using a regression equation.

[0025] f(x) = X T θ,Y=f(x)+ξ, where X is a two-dimensional vector composed of temperature and temperature change rate, Y is the scale or bias corresponding to X, f(x) is the regression prediction function relationship of Gaussian process, θ is a weighted one-dimensional vector, and ξ is Gaussian noise that follows a normal distribution.

[0026] When the prior distribution of the weighted one-dimensional vector θ is a Gaussian distribution (N(0,Σ)), the prior distribution h(Y|X,θ) of θ is: Wherein, p(Y i |x i ,θ) is the given input x i Output Y at θ i The conditional probability density function that it follows, x i Given the input feature vector (i.e., temperature), Y i For the observed output value (bias or scale), σ n Let N be the standard deviation of Gaussian noise, N(,) represent a Gaussian distribution, I be the identity matrix, and n be the number of samples, i = 1, 2, ..., n.

[0027] When the posterior distribution of θ is Gaussian, the prior distribution of θ, t(θ|X,Y), is: Where π(θ) is the prior probability density function of the parameter θ. This is the posterior mean.

[0028] Furthermore, a Gaussian regression model for the quartz flexible accelerometer is constructed based on the regression model of the quartz flexible accelerometer, specifically including the following steps.

[0029] The input vector X is derived from the posterior distribution of θ. * Predicted distribution p(f) * |X * (X,Y): Wherein, p(f * |X * ,θ) is a given new input X * Output f at θ * The conditional probability density function that it follows, X * Given the input vector, f * For the function f in X * The prediction scale or bias at that point.

[0030] Using matrix transformation formulas to predict distribution p(f) * |X * Simplifying (X,Y) yields:

[0031] make X T ∑X * =K(X,X) * ), X T ∑X=K(X,X), The Gaussian regression model for the quartz flexural accelerometer is: in, To predict the mean of the distribution, i.e., the optimal solution of the Gaussian process regression model; cov(f * ) represents the mean square error of the predicted distribution, indicating the uncertainty of the predicted value.

[0032] The Gaussian regression model for the accelerometer derived above describes the relationship between temperature, rate of temperature change, and scaling or bias. Based on the real-time voltage acquisition of the circuit, the temperature-compensated acceleration value is obtained based on the Gaussian regression model of the quartz flexible accelerometer, thus completing the temperature compensation optimization of the quartz flexible accelerometer.

[0033] Specifically, according to a = (U acq -K0) / K1 obtains the temperature-compensated acceleration value, where a is the temperature-compensated acceleration value, and U acqThe circuit acquires voltage in real time. K0 is the bias value after temperature compensation, and K1 is the scale after temperature compensation.

[0034] The core of the temperature compensation algorithm for the Gaussian regression process in this invention is that it does not depend on a specific function form, can be applied to various complex data distributions and relationships, and can effectively model the nonlinear relationship of quartz flexural accelerometer affected by temperature changes, facilitating subsequent prediction and analysis.

[0035] like Figure 1 As shown, according to another invention of the present invention, an accelerometer circuit operation method is provided, which uses the quartz flexible accelerometer temperature compensation optimization method described above to perform temperature compensation on the accelerometer.

[0036] Specifically, the operation method of the accelerometer circuit includes:

[0037] The circuit acquires voltage in real time and performs PID closed-loop control.

[0038] Real-time acquisition of acceleration and temperature values, and calculation of temperature change rate;

[0039] Based on the real-time acquisition of voltage and temperature change rates by the circuit, the acceleration values ​​are temperature-compensated using the Gaussian regression model of the quartz flexible accelerometer.

[0040] The temperature-compensated acceleration values ​​are periodically packaged and uploaded to the host computer.

[0041] The accelerometer circuit operation method of the present invention performs temperature compensation on the acceleration value based on temperature and the rate of temperature change during the accelerometer acquisition process, and then calculates the compensated acceleration value. This method can effectively reduce the influence of temperature changes on the accelerometer acceleration value.

[0042] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing temperature compensation in a quartz flexible accelerometer, characterized in that, The method includes: A regression model for a quartz flexible accelerometer is constructed using the regression equation: f(x) = X T θ,Y=f(x)+ξ, where X is a two-dimensional vector composed of temperature and temperature change rate, Y is the scale or bias corresponding to X, f(x) is the regression prediction function relationship of Gaussian process, θ is a weighted one-dimensional vector, and ξ is Gaussian noise that follows a normal distribution. A Gaussian regression model for a quartz flexible accelerometer is constructed based on the regression model of the quartz flexible accelerometer: Among them, f * For the function f in X * The prediction scale or bias at X * For the input vector, To predict the mean of the distribution, cov(f) * ) represents the mean square error of the predicted distribution. K(X,X * ) = X T ∑X * K(X,X)=X T ∑X, σ n Let I be the standard deviation of the Gaussian noise, and I be the identity matrix. Based on the real-time voltage collected by the circuit, the acceleration value after temperature compensation is obtained based on the Gaussian regression model of the quartz flexible accelerometer, thus completing the temperature compensation optimization of the quartz flexible accelerometer.

2. The temperature compensation optimization method for quartz flexible accelerometers according to claim 1, characterized in that, According to a=(U acq -K0) / K1 obtains the temperature-compensated acceleration value, where a is the temperature-compensated acceleration value, and U acq The circuit acquires voltage in real time. K0 is the bias value after temperature compensation, and K1 is the scale after temperature compensation.

3. An accelerometer circuit operation method, characterized in that, The accelerometer circuit operation method employs the temperature compensation optimization method for quartz flexible accelerometers as described in claim 1 or 2 to perform temperature compensation on the accelerometer.

4. The accelerometer circuit operation method according to claim 3, characterized in that, The accelerometer circuit operation method specifically includes: The circuit acquires voltage in real time and performs PID closed-loop control. Real-time acquisition of acceleration and temperature values, and calculation of temperature change rate; Based on the real-time acquisition of voltage and temperature change rates by the circuit, the acceleration values ​​are temperature-compensated using the Gaussian regression model of the quartz flexible accelerometer. The temperature-compensated acceleration values ​​are periodically packaged and uploaded to the host computer.

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