A New Analytic Alignment Method for a SINS

Analytic alignment is a type of self-alignment for a Strapdown inertial navigation system (SINS) that is based solely on two non-collinear vectors, which are the gravity and rotational velocity vectors of the Earth at a stationary base on the ground. The attitude of the SINS with respect to the Earth can be obtained directly using the TRIAD algorithm given two vector measurements. For a traditional analytic coarse alignment, all six outputs from the inertial measurement unit (IMU) are used to compute the attitude. In this study, a novel analytic alignment method called selective alignment is presented. This method uses only three outputs of the IMU and a few properties from the remaining outputs such as the sign and the approximate value to calculate the attitude. Simulations and experimental results demonstrate the validity of this method, and the precision of yaw is improved using the selective alignment method compared to the traditional analytic coarse alignment method in the vehicle experiment. The selective alignment principle provides an accurate relationship between the outputs and the attitude of the SINS relative to the Earth for a stationary base, and it is an extension of the TRIAD algorithm. The selective alignment approach has potential uses in applications such as self-alignment, fault detection, and self-calibration

Application of H

The transfer alignment (TA) scheme is used for the initial alignment of Inertial Navigation System (INS) on dynamical base. The Kalman filter is often used in TA to improve the precision of TA. And the statistical characteristics of interference signal which is difficult to get must be known before the Kalman filter is used in the TA, because the interference signal is a random signal and there are some changes on the dynamic model of system. In this paper, the H∞ filter is adopted in the TA scheme of the angular rate matching when the various stages of disturbance in measurement are unknown. And it is compared with the Kalman filter in the same environment of simulation and evaluation. The result of simulation shows that the H∞ filter and the Kalman filter are both effective. The Kalman filter is more accurate than the H∞ filter when system noise and measurement noise are white noise, but the H∞ filter is more accurate and quicker than the Kalman filter when system noise and measurement noise are color noise. In the engineering practice, system noise and measurement noise are always color noise, so the H∞ filter is more suitable for engineering practice than the Kalman filter

Rapid Transfer Alignment of SINS with Measurement Packet Dropping based on a Novel Suboptimal Estimator

Transfer alignment (TA) is an important step for strapdown inertial navigation system (SINS) starting from a moving base, which utilises the information proposed from the higher accurate and well performed master inertial navigation system. But the information is often delayed or even lost in real application, which will seriously affect the accuracy of TA. This paper models the stochastic measurement packet dropping as an independent identically distributed (IID) Bernoulli random process, and introduces it into the measurement equation of rapid TA, and the influence of measurement packet dropping is analysed. Then, it presents a suboptimal estimator for the estimation of the misalignment in TA considering the random arrival of the measurement packet. Simulation has been done for the performance comparison about the suboptimal estimator, standard Kalman filter and minimum mean squared estimator. The results show that the suboptimal estimator has better performance, which can achieve the best TA accuracy

An FGO-based Unified Initial Alignment Method of Strapdown Inertial Navigation System

The initial alignment process can provide an accurate initial attitude of strapdown inertial navigation system. The conventional two-procedure method usually includes coarse and fine alignment processes. Coarse alignment converges fast because of its batch estimating characteristics and the initial attitude does not influence the results. But coarse alignment is low accuracy without considering the IMU's bias. The fine alignment is more accurate by applying a recursive Bayesian filter to estimate the IMU's bias, but the attitude converges slowly as the initial value influence the convergence speed of the recursive filter. Researchers have proposed the unified initial alignment to achieve initial alignment in one procedure, existing unified methods make improvements on the basics of recursive Bayesian filter and those methods are still slow to converge. In this paper, a unified method based on batch estimator FGO (factor graph optimization) is raised, which is converge fast like coarse alignment and accurate than the existing method. We redefine the state and rederivation the state dynamic model first. Then, the optimal attitude and the IMU's bias are estimated simultaneously through FGO. The fast convergence and high accuracy of this method are verified by simulation and physical experiments on a rotation SINS.Comment: 9 pages, Journal Paper

Алгоритм управления беспилотными летательными аппаратами в процессе визуального сопровождения объектов с изменяемой траекторией движения

The purpose of the research was to create an algorithm for determining and correcting the output parameters of the navigation module and the flight-navigation complex of unmanned aerial vehicles which provides control of an aviation gyro-stabilized platform with a multispectral optoelectronic system during flight and tracking various objects of observation.Principles of control of an aviation technical vision system located on an unmanned aerial vehicle on a two-degree gyro-stabilized platform with the possibility of full turn around two perpendicular axes along the course and pitch are considered. Stability of tracking of observation objects at a distance of up to 10000 m is ensured by the use of a multispectral optoelectronic system including a rangefinder, thermal imaging and two visual channels.Analysis of the object of observation and the method of its support are carried out. An algorithm is proposed for integrating a Global Navigation Satellite System and a strapdown inertial navigation system based on the extended Kalman filter which includes two stages of calculations, extrapolation (prediction) and correction. Specialized software in the FreeRTOS v9.0 environment has been developed to obtain a navigation solution: latitude, longitude and altitude of the unmanned aerial vehicle in the WGS-84 coordinate system, as well as the pitch, heading and roll angles; north, east and vertical components of velocities in the navigation coordinate system; longitudinal, vertical and transverse components of free accelerations and angular velocities in the associated coordinate system based on data from the receiving and measuring module of the Global Navigation Satellite System and data from the 6-axis MEMS sensor STIM300.Рассмотрены принципы управления авиационной системой технического зрения, размещённой на беспилотном летательном аппарате на двухстепенной гиростабилизированной платформе с возможностью полного разворота вокруг двух перпендикулярных осей по курсу и тангажу. Устойчивость сопровождения объектов наблюдения на расстоянии до 10000 м обеспечивается применением мультиспектральной оптико-электронной системы, включающей дальномерный, тепловизионный и два визуальных канала.Выполнен анализ объекта наблюдения и методика его сопровождения. Предложен алгоритм интеграции спутниковой радионавигационной системы и бесплатформенной инерциальной навигационной системы на основе интегрального фильтра Калмана, предусматривающей две стадии вычислений: экстраполяцию (предсказание) и коррекцию. В модуль навигации встроено специализированное программное обеспечение для многозадачной операционной системы реального времени FreeRTOS, обеспечивающее получение навигационного решения: широты, долготы и высоты беспилотного летательного аппарата в системе координат WGS-84, а также углов крена тангажа и курса; северной, восточной и вертикальной составляющих скоростей в навигационной системе координат; продольной, вертикальной и поперечной составляющих свободных ускорений и угловых скоростей в связанной системе координат на основе данных от приёмо-измерительного модуля спутниковой радионавигационной системы и данных от 6-осевого МЭМС-датчика STIM 300

A Method for SINS Alignment with Large Initial Misalignment Angles Based on Kalman Filter with Parameters Resetting

In the initial alignment process of strapdown inertial navigation system (SINS), large initial misalignment angles always bring nonlinear problem, which causes alignment failure when the classical linear error model and standard Kalman filter are used. In this paper, the problem of large misalignment angles in SINS initial alignment is investigated, and the key reason for alignment failure is given as the state covariance from Kalman filter cannot represent the true one during the steady filtering process. According to the analysis, an alignment method for SINS based on multiresetting the state covariance matrix of Kalman filter is designed to deal with large initial misalignment angles, in which classical linear error model and standard Kalman filter are used, but the state covariance matrix should be multireset before the steady process until large misalignment angles are decreased to small ones. The performance of the proposed method is evaluated by simulation and car test, and the results indicate that the proposed method can fulfill initial alignment with large misalignment angles effectively and the alignment accuracy of the proposed method is as precise as that of alignment with small misalignment angles