7 results about "Allan variance" patented technology

The invention discloses a noise component calculation method and device of a hemispherical resonator gyroscope. The method comprises the following steps: acquiring a test result of the zero-bias stability of the hemispherical resonator gyroscope; carrying out ALLAN variance analysis on the test result of the zero bias stability, and determining a quantization noise coefficient, an angle random walk coefficient, a zero bias instability coefficient, a rate random walk coefficient and a rate slope noise coefficient; and calculating and determining a quantization noise component, an angle random walk noise component, a zero offset instability noise component, a rate random walk noise component and a rate slope noise component according to the quantization noise coefficient, the angle random walk coefficient, the zero offset instability coefficient, the rate random walk coefficient and the rate slope noise coefficient. According to the method, contributions of different noise components in the zero offset error source of the hemispherical resonator gyroscope can be obtained, and the long-term stability and the measurement precision of the hemispherical resonator gyroscope are improved.

This invention belongs to the field of sensor signal processing technology. It provides a drift suppression method and system for a six-axis sensor, comprising: acquiring actual operating data of the six-axis sensor; using Allan variance analysis of the actual operating data to perform cumulative analysis on the drift of any axis within the acquisition period to assess the drift risk of the six-axis sensor; if the drift risk of the six-axis sensor is high, extracting the instantaneous zero bias of the accelerometer and the gyroscope, and analyzing whether the zero bias drift trends of the accelerometer and the gyroscope are consistent within the acquisition period; if consistent, extracting temperature data within the acquisition period and performing timestamp matching to determine whether the accelerometer drift and gyroscope drift are caused by temperature. This invention improves the measurement accuracy and stability of the sensor under temperature variation environments, extends the effective service life of the sensor, reduces the need for replacement due to excessive drift, and lowers equipment maintenance costs.

The invention discloses an improved IMU (Inertial Measurement Unit) calibration method based on filtering optimization, which comprises the following steps of: S1, respectively placing MEMS IMUs at three predefined positions, and acquiring acceleration data in a static state at each position; s2, identifying the random error of the MEMS IMU through Allan variance, and performing wavelet threshold filtering and denoising on the output signal of the MEMS IMU at each position by referring to the identification result to obtain a denoised signal; s3, discrete Kalman filtering processing is carried out on a result obtained after wavelet denoising; and S4, based on a Kalman filtering result, representing an output value model of the MEMS IMU accelerometer in a static state as a linear model, and based on three-position method calibration and least square modeling, solving a calibration result. According to the method, the noise characteristics are determined through Allan variance analysis, the original data output by the IMU are filtered by using a wavelet denoising technology and Kalman filtering, and the calibration precision and stability of the IMU in a static environment can be remarkably improved by combining with the three-position method for calibration.

The invention discloses an error modeling method based on recursive variance information of a fiber-optic gyroscope, and relates to the field of error identification of the fiber-optic gyroscope, the method comprises the following steps: acquiring N output data of the fiber-optic gyroscope within bundling time tau, and generating an original output sequence according to the output data; the output data comprises an angular rate; according to the original output sequence, based on an Allan variance estimation method, determining an Allan variance corresponding to the fiber-optic gyroscope; according to the output data and the Allan variance, constructing a random error representation model; and optimizing the output data by using the random error representation model. According to the method, the real-time performance and the accuracy of FOG random error analysis can be improved, and then different types of noise sources can be quickly and accurately identified.

The application discloses an in-orbit reference frequency stability estimation method of an inter-satellite link system, which comprises the following steps: calculating a combined phase measurement value Θ p (t) according to a two-way phase measurement value Θ(t) without time scale correction; calculating a relative frequency error δf i ‑δf j ; calculating a relative frequency error sampling value, repeating the sampling according to a nominal sampling frequency, and calculating a relative Allan variance. The application is improved based on the calculation formula of the Allan variance, can accurately estimate the relative frequency stability between two stars of the inter-satellite link, and further estimate the frequency stability of a single star.

The invention discloses a multi-position north seeking method of a north seeker, which comprises the following steps of: calibrating an Allan variance curve of a gyroscope, and determining a characteristic time constant corresponding to each noise type; controlling the north seeker to sequentially rotate to a plurality of preset positions, identifying a dominant noise type in real time at each position through power spectral density analysis, and dynamically adjusting a sampling or signal processing strategy; meanwhile, acquiring internal multi-point temperature information to calculate a temperature gradient, and compensating zero offset of the gyroscope in real time by using a compensation model containing the temperature gradient; and finally, calculating the northbound angle based on the sampling data of all positions and the compensation result. According to the method, through adaptive noise suppression and multivariable temperature compensation, north-seeking precision and temperature adaptability in a complex environment are remarkably improved, and measurement efficiency and system stability are both considered by means of an optimization algorithm.

The invention discloses a silicon micro-resonant accelerometer noise suppression method and system based on an improved auto-encoder. According to the method, output data with low typical random error parameter value and high signal-to-noise ratio of the accelerometer is screened out by using an Allan variance analysis method to serve as a training set, the data utilization rate is improved and the calculation complexity is reduced by adopting a mode of dynamically intercepting continuous intermediate data, and lightweight training is realized. The auto-encoder model adopts a two-stage training strategy: in the first stage, a high-quality input signal training model is used to reconstruct an original signal; in the second stage, a loss function introduces an output power spectral density constraint, so that a noise reduction result approaches to a reference sample frequency spectrum in a frequency domain, and typical random errors such as speed random walk, zero offset instability and acceleration random walk are optimized at the same time. The noise reduction result is verified through an Allan variance analysis method, and the method can remarkably reduce the output noise level of the accelerometer.