Coupled GPS/MEMS IMU Attitude Determination of Small UAVs with COTS

Strohmeier, Michael; Montenegro, Sérgio

doi:10.3390/electronics6010015

Cited by 18 publications

(10 citation statements)

References 10 publications

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“…Here, y represents the measured data, Ω = [p, q, r] T is the angular velocity, β is a temperature-related bias, and η is the Gaussian white noise, η ∼ N(0, Q). For the GPS measurement shown in Table 4, the position error and its dynamic characteristics are both required, and a Gauss-Markov error model is an appropriate description of the GPS measurement process [19].…”

Section: Sensors Measurement Modelmentioning

confidence: 99%

“…In the dynamic process, the measurement is consistent with the trend of the true signal, and when entering the steady state, the measurement noise is large, and there is a significant deviation. Table 4, the position error and its dynamic characteristics are both required, and a Gauss-Markov error model is an appropriate description of the GPS measurement process [19].…”

Section: Sensors Measurement Modelmentioning

confidence: 99%

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Automatic Control and Model Verification for a Small Aileron-Less Hand-Launched Solar-Powered Unmanned Aerial Vehicle

et al. 2020

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This paper describes a low-cost flight control system of a small aileron-less hand-launched solar-powered unmanned aerial vehicle (UAV). In order to improve the accuracy of the whole system model and quantify the influence of each subsystem, detailed modeling of UAV energy and a control system including a solar model, engine, energy storage, sensors, state estimation, control law, and actuator module are established in accordance with the experiment and component principles. A whole system numerical simulation combined with the 6 degree-of-freedom (DOF) simulation model is constructed based on the typical mission route, and the parameter precision sequence and energy balance are obtained. Then, a hardware-in-the-loop (HIL) experiment scheme based on the Stewart platform (SP) is proposed, and three modes of acceleration, angular velocity, and attitude are designed to verify the control system through the inner and boundary states of the flight envelope. The whole system scheme is verified by flight tests at different altitudes, and the aerodynamic force coefficient and sensor error are corrected by flight data. With the increase of altitude, the cruise power increases from 47 W to 78 W, the trajectory tracking precision increases from 23 m to 44 m, the sensor measurement noise increases, and the bias decreases.

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Section: Sensors Measurement Modelmentioning

confidence: 99%

Section: Sensors Measurement Modelmentioning

confidence: 99%

Automatic Control and Model Verification for a Small Aileron-Less Hand-Launched Solar-Powered Unmanned Aerial Vehicle

et al. 2020

View full text Add to dashboard Cite

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“…Another way to improve the heading performance of AHRS under condition of magnetic disturbance is to incorporate the low-cost GPS module into the AHRS and utilize the GPS/SINS-integrated navigation-based heading determination method [14][15][16][17][18][19][20][21]. e core of the GPS/ SINS-integrated navigation method is to suppress the drift of gyroscope integration by making use of the aided information provided by the GPS, thus yielding more accurate heading information for a long time [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

MCTLS-Assisted Completed SINS/GPS Integrated and Applied to Low-Cost Attitude and Heading Reference System

Tao

Song

Weng

2021

Mathematical Problems in Engineering

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In this paper, a robust heading determination method is proposed for low-cost attitude and heading reference system (AHRS) aided by the global positioning system (GPS). As compared with the traditional GPS/SINS-integrated navigation-based heading determination method, in the proposed method, the heading information obtained from the GPS velocity outputs is first incorporated into the observation vector, which constructs a novel completed GPS/SINS integration framework and greatly improves the observability of azimuthal misalignment in the Kalman filter. Moreover, a multivariate constrained total least square (MCTLS) method is proposed and integrated into the completed integration framework to deal with the measurement error in both input and output data of GPS velocity measurement model, which improves the accuracy of the observed heading information and yields a robust heading estimation at each time instant. Simulation and experiment results demonstrate that the proposed robust heading determination method can outperform the related state-of-the-art methods for the GPS-aided attitude and heading reference system.

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“…A detailed case study of autonomous car was discussed based on the distributed architecture [7]. In addition, numerous researches of integrating GNSS/IMU and RTK technique have been conducted for the applications of Unmanned Aerial Vehicles (UAVs), but most of them apply a singlefrequency GNSS receiver [8,9].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based GNSS Network-Based Real-Time Kinematic Improvement for Autonomous Ground Vehicle Navigation

Kim

Bae

2019

Journal of Sensors

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Much navigation over the last several decades has been aided by the global navigation satellite system (GNSS). In addition, with the advent of the multi-GNSS era, more and more satellites are available for navigation purposes. However, the navigation is generally carried out by point positioning based on the pseudoranges. The real-time kinematic (RTK) and the advanced technology, namely, the network RTK (NRTK), were introduced for better positioning and navigation. Further improved navigation was also investigated by combining other sensors such as the inertial measurement unit (IMU). On the other hand, a deep learning technique has been recently evolving in many fields, including automatic navigation of the vehicles. This is because deep learning combines various sensors without complicated analytical modeling of each individual sensor. In this study, we structured the multilayer recurrent neural networks (RNN) to improve the accuracy and the stability of the GNSS absolute solutions for the autonomous vehicle navigation. Specifically, the long short-term memory (LSTM) is an especially useful algorithm for time series data such as navigation with moderate speed of platforms. From an experiment conducted in a testing area, the LSTM algorithm developed the positioning accuracy by about 40% compared to GNSS-only navigation without any external bias information. Once the bias is taken care of, the accuracy will significantly be improved up to 8 times better than the GNSS absolute positioning results. The bias terms of the solution need to be estimated within the model by optimizing the layers as well as the nodes each layer, which should be done in further research.

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Coupled GPS/MEMS IMU Attitude Determination of Small UAVs with COTS

Cited by 18 publications

References 10 publications

Automatic Control and Model Verification for a Small Aileron-Less Hand-Launched Solar-Powered Unmanned Aerial Vehicle

Automatic Control and Model Verification for a Small Aileron-Less Hand-Launched Solar-Powered Unmanned Aerial Vehicle

MCTLS-Assisted Completed SINS/GPS Integrated and Applied to Low-Cost Attitude and Heading Reference System

Deep Learning-Based GNSS Network-Based Real-Time Kinematic Improvement for Autonomous Ground Vehicle Navigation

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