Post by kately on Jan 25, 2024 4:42:23 GMT -5
Gyroscope sensors are not affected by ferromagnetic environments, so they are widely used in fields such as inertial navigation, oil logging technology, coalfield geological exploration, and autonomous driving. There are many types of gyro sensors, each with their own characteristics. Fiber optic gyro sensors have the characteristics of higher precision, larger size, and higher price, while MEMS gyroscopes have the characteristics of smaller size, lower cost, but lower accuracy. Today we are mainly studying fiber optic gyroscopes.
Deterministic error and random drift error are the two most important errors in FOG. Under normal circumstances, the deterministic errors of gyro sensors mainly include zero bias, sensitivity, non-orthogonal coordinate axes and misaligned installations. A deterministic error model can usually be established and eliminated through calibration methods. The random drift error will change with time during long-term operation. The use of directional drilling technology to drain gas from coal seams is the most effective method for underground coal mine gas control. When the coal mine underground drilling rig is working underground, the accuracy of the drilling rig, the accuracy of the attitude angle parameters, and the rapid acquisition of the attitude angle are closely related. Therefore, the gyro in the system must be Analyze and compensate for random drift errors in inertial sensors.
The usual compensation method is to establish a gyro random error model, and use Kalman filtering technology to compensate for the gyro’s random drift in real time. This article will use time series to observe the fiber optic gyroscope, establish a model, and then use Kalman filtering technology to compensate.
1.Time series analysis method
Time series is a series of observation values obtained in chronological order. The observation values in the sequence are characterized by interdependence and randomness. This analysis method is to analyze the patterns in the data from the time series field, model and study them, and predict trends. or exercise control. The basic model of time series analysis is the Auto-Regressive Moving Average Model, referred to as the ARMA model. It is a method that uses parameter model data to process and identify model parameters.
2.ARMA model
This paper analyzes the random error data of the fiber optic gyroscope, uses the AIC criterion to select the model, and finally determines that the time series ARMA model can more accurately reflect the static output characteristics of the fiber optic gyroscope as:3.Kalman filtering based on fiber optic gyroscope
Kalman filtering is a time domain filtering method and a recursive state estimation method. Kalman filter adopts the state space description method in modern control theory, performs a one-step recursion on the state variables according to the system state differential equation, and then measures the observed quantity at the current moment, and jointly estimates the state based on the specific relationship between the two. The estimated value at the current moment. According to FOG static output model, the state variable and observation equation of the Kalman filter can be obtained: Based on the ARMA model based on the random error of the fiber optic gyroscope in this article, it can be seen that:The system measurement equation is:Then the state equation and measurement equation are combined together, and Kalman filter is used for recursive calculation.
4.Summary
In view of the random errors, this paper uses the time series analysis method to establish the time series ARMA model of the gyroscope, and uses the Kalman filter method for estimation and compensation. It can be seen that the error model established can improve the accuracy of FOG. Ericco adheres to high requirements and high-quality development in inertial products. Among the fiber optic gyroscope series, ER-FOG-50, ER-FOG-60, and ER-FOG-70 have become Ericco’s advantageous products due to their low cost and high performance. If you are interested, please feel free to consult us.
Contact information:
Email: info@ericcointernational.com
Whatsapp: +8613992884879
Wechat: 13992884879
Article resource: www.ericcointernational.com/application/processing-and-analysis-of-random-drift-error-based-on-fiber-optic-gyroscope.html
Deterministic error and random drift error are the two most important errors in FOG. Under normal circumstances, the deterministic errors of gyro sensors mainly include zero bias, sensitivity, non-orthogonal coordinate axes and misaligned installations. A deterministic error model can usually be established and eliminated through calibration methods. The random drift error will change with time during long-term operation. The use of directional drilling technology to drain gas from coal seams is the most effective method for underground coal mine gas control. When the coal mine underground drilling rig is working underground, the accuracy of the drilling rig, the accuracy of the attitude angle parameters, and the rapid acquisition of the attitude angle are closely related. Therefore, the gyro in the system must be Analyze and compensate for random drift errors in inertial sensors.
The usual compensation method is to establish a gyro random error model, and use Kalman filtering technology to compensate for the gyro’s random drift in real time. This article will use time series to observe the fiber optic gyroscope, establish a model, and then use Kalman filtering technology to compensate.
1.Time series analysis method
Time series is a series of observation values obtained in chronological order. The observation values in the sequence are characterized by interdependence and randomness. This analysis method is to analyze the patterns in the data from the time series field, model and study them, and predict trends. or exercise control. The basic model of time series analysis is the Auto-Regressive Moving Average Model, referred to as the ARMA model. It is a method that uses parameter model data to process and identify model parameters.
2.ARMA model
This paper analyzes the random error data of the fiber optic gyroscope, uses the AIC criterion to select the model, and finally determines that the time series ARMA model can more accurately reflect the static output characteristics of the fiber optic gyroscope as:3.Kalman filtering based on fiber optic gyroscope
Kalman filtering is a time domain filtering method and a recursive state estimation method. Kalman filter adopts the state space description method in modern control theory, performs a one-step recursion on the state variables according to the system state differential equation, and then measures the observed quantity at the current moment, and jointly estimates the state based on the specific relationship between the two. The estimated value at the current moment. According to FOG static output model, the state variable and observation equation of the Kalman filter can be obtained: Based on the ARMA model based on the random error of the fiber optic gyroscope in this article, it can be seen that:The system measurement equation is:Then the state equation and measurement equation are combined together, and Kalman filter is used for recursive calculation.
4.Summary
In view of the random errors, this paper uses the time series analysis method to establish the time series ARMA model of the gyroscope, and uses the Kalman filter method for estimation and compensation. It can be seen that the error model established can improve the accuracy of FOG. Ericco adheres to high requirements and high-quality development in inertial products. Among the fiber optic gyroscope series, ER-FOG-50, ER-FOG-60, and ER-FOG-70 have become Ericco’s advantageous products due to their low cost and high performance. If you are interested, please feel free to consult us.
Contact information:
Email: info@ericcointernational.com
Whatsapp: +8613992884879
Wechat: 13992884879
Article resource: www.ericcointernational.com/application/processing-and-analysis-of-random-drift-error-based-on-fiber-optic-gyroscope.html