In-flight alignment of a strapdown inertial navigation system of an unmanned aerial vehicle

Veremeenko, K. K.; Savel'Ev, V. M.

doi:10.1134/s1064230712060147

Cited by 17 publications

(5 citation statements)

References 1 publication

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“…The reason is that the output velocity of GPS is in the navigation frame, but the output velocity of OD is in the body frame. In the fine alignment procedure, a linear Kalman Filter (KF) is generally used in the fine alignment based on equations of small misalignment angles (Zhao et al, 2015; Chang, 2015; Veremeenko and Savel'ev, 2013; Li et al, 2015; Wang, 2009). An in-motion fine alignment error model was developed by Zhao et al (2015) in which the position increment differences between SINS and OD along the navigation frame were considered as measurements, and a KF was used to estimate the error angle.…”

Section: Introductionmentioning

confidence: 99%

In-motion Alignment Algorithm for Vehicle Carried SINS Based on Odometer Aiding

et al. 2017

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In-motion alignment plays an important role in improving the manoeuvring capability of a vehicle, and allows the initialisation of a Strapdown Inertial Navigation System (SINS) while moving. Odometer (OD) aided in-motion alignment is widely adopted owing to its fully self-contained characteristic. This paper proposes a complete in-motion alignment algorithm for a vehicle-carried SINS based on odometer aiding, in which an in-motion coarse alignment method using the integration form of the velocity update equation in the body frame to give a rough initial angle is introduced and a new measurement equation in the body frame with a Kalman filter (KF) for the in-motion fine alignment is established. The advantages of the proposed method are verified by simulation and measured data.

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Section: Introductionmentioning

confidence: 99%

In-motion Alignment Algorithm for Vehicle Carried SINS Based on Odometer Aiding

et al. 2017

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“…In addition, Ding (2008) focuses on problems in modelling and implementation of an INS/GPS integration such as synchronisation, raw INS measurement noises, adaptive Kalman filter, and fusion of an INS sensor with an independent GPS receiver. Veremeenko and Savel'Ev (2013) studied an INS for an Unmanned Aerial Vehicle (UAV). This research introduced a method for setting initial parameters of an on board INS for a UAV.…”

Section: Introductionmentioning

confidence: 99%

An Augmented Strapdown Inertial Navigation System using Jerk and Jounce of Motion for a Flying Robot

Bayat

Atashgah

2017

J. Navigation

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This paper offers an algorithm for enhancement of positioning accuracy of a quad-rotor flying robot, based on jerk and jounce of motion. The suggested method utilises the first and second numerical derivatives of the vehicle's acceleration and augments the mathematical model in the estimation process. For this purpose, the Kalman Filter (KF) is implemented for integration of a Strapdown Inertial Navigation System (SINS) and Global Navigation Satellite System (GNSS). The required data are collected from a low-cost/quality Micro Electromechanical Sensors (MEMS) during an assisted flight. For increasing the precision and accuracy of the collected data, all instruments including accelerometers, gyroscopes and magnetometers are calibrated before the experiments. Moreover, to reduce and limit the measurement noises of the MEMS sensor, a low-pass filter is applied; this is while sensors in the autopilot are affected by high levels of noise and drift, which makes them inappropriate for accurate positioning. The experimental results exhibit an improvement in positioning and altitude sensing through augmentation of the loosely coupled SINS/GNSS navigation method.

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“…Gyrocompass alignment based on compass effect is an important fine alignment method with the properties of interference suppression and lower computational complexity [18,19], but it needs to set longer damping time constant and always costs much more time to finish the alignment process [20,21]. Kalman filter is an optimal 2 Mathematical Problems in Engineering linear estimator when measurement noise has a Gaussian distribution with known measurement and process noise variance values [8,[22][23][24][25]. However, it is impractical that measurement noise has a Gaussian distribution with known measurement and process noise variance values, and it is very sensitive to the rocking interference and has the disadvantage of large amount of calculation [20].…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification Method for SINS Initial Alignment under Inertial Frame

Han¹,

Guo²,

Zhou³

2016

Mathematical Problems in Engineering

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The performance of a strapdown inertial navigation system (SINS) largely depends on the accuracy and rapidness of the initial alignment. The conventional alignment method with parameter identification has been already applied widely, but it needs to calculate the gyroscope drifts through two-position method; then the time of initial alignment is greatly prolonged. For this issue, a novel self-alignment algorithm by parameter identification method under inertial frame for SINS is proposed in this paper. Firstly, this coarse alignment method using the gravity in the inertial frame as a reference is discussed to overcome the limit of dynamic disturbance on a rocking base and fulfill the requirement for the fine alignment. Secondly, the fine alignment method by parameter identification under inertial frame is formulated. The theoretical analysis results show that the fine alignment model is fully self-aligned with no external reference information and the gyrodrifts can be estimated in real time. The simulation results demonstrate that the proposed method can achieve rapid and highly accurate initial alignment for SINS.

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In-flight alignment of a strapdown inertial navigation system of an unmanned aerial vehicle

Cited by 17 publications

References 1 publication

In-motion Alignment Algorithm for Vehicle Carried SINS Based on Odometer Aiding

In-motion Alignment Algorithm for Vehicle Carried SINS Based on Odometer Aiding

An Augmented Strapdown Inertial Navigation System using Jerk and Jounce of Motion for a Flying Robot

Parameter Identification Method for SINS Initial Alignment under Inertial Frame

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