Post by kately on Mar 1, 2024 4:04:04 GMT -5
North seekers generally use gyroscopes and accelerometers to statically seek north through a multi-position method. Errors in gyroscopes and accelerometers can be divided into deterministic errors and random errors. Deterministic errors can be accurately measured and compensated through multi-position transposition, while random errors have become an important factor affecting the accuracy of north seeking. To this end, various methods have been used to model and filter this random error.
In terms of gyro random error modeling, some experts use the auto-regressive moving average model (ARMA(n,m)) to establish a random error model for fiber optic gyroscopes (FOG). Some also use wavelet transform to model and filter FOG random errors, and Allan variance analysis method and its improved methods can be used to analyze various noises in the data before and after modeling and filtering.
This paper takes the north seeker using FOG and accelerometer as the core components as an example. On the basis of the above, the improved second-order autoregressive model-AR(2) model was used to carry out online modeling and filtering research on the measurement signal in the gyro north seeker, which improved the north seeking accuracy of the gyro north seeker.
The ARMA(n,m) model requires that the signal must be stationary, normally distributed, and have a zero-mean time series. Theoretical derivation and experiments prove that the static output data of FOG and accelerometer can basically be regarded as stationary and normal time series. However, due to the existence of ground speed and FOG bias as well as gravity acceleration and accelerometer bias, the static output data of FOG and accelerometer do not meet the condition of zero mean. In order to realize online modeling of the static output signals of FOG and accelerometers and real-time filtering of random errors, this article uses an improved ARMA(n,m) model.
The full article: www.ericcointernational.com/application/modeling-and-filtering-in-signals-collected-by-fog-north-seekers.html
In terms of gyro random error modeling, some experts use the auto-regressive moving average model (ARMA(n,m)) to establish a random error model for fiber optic gyroscopes (FOG). Some also use wavelet transform to model and filter FOG random errors, and Allan variance analysis method and its improved methods can be used to analyze various noises in the data before and after modeling and filtering.
This paper takes the north seeker using FOG and accelerometer as the core components as an example. On the basis of the above, the improved second-order autoregressive model-AR(2) model was used to carry out online modeling and filtering research on the measurement signal in the gyro north seeker, which improved the north seeking accuracy of the gyro north seeker.
The ARMA(n,m) model requires that the signal must be stationary, normally distributed, and have a zero-mean time series. Theoretical derivation and experiments prove that the static output data of FOG and accelerometer can basically be regarded as stationary and normal time series. However, due to the existence of ground speed and FOG bias as well as gravity acceleration and accelerometer bias, the static output data of FOG and accelerometer do not meet the condition of zero mean. In order to realize online modeling of the static output signals of FOG and accelerometers and real-time filtering of random errors, this article uses an improved ARMA(n,m) model.
The full article: www.ericcointernational.com/application/modeling-and-filtering-in-signals-collected-by-fog-north-seekers.html