GPS-aided Inertial Navigation Research Articles

A Lightweight Remote Sensing Payload for Wildfire Detection and Fire Radiative Power Measurements

Small uncrewed aerial systems (sUASs) have the potential to serve as ideal platforms for high spatial and temporal resolution wildfire measurements to complement aircraft and satellite observations, but typically have very limited payload capacity. Recognizing the need for improved data from wildfire management and smoke forecasting communities and the potential advantages of sUAS platforms, the Nighttime Fire Observations eXperiment (NightFOX) project was funded by the US National Oceanic and Atmospheric Administration (NOAA) to develop a suite of miniaturized, relatively low-cost scientific instruments for wildfire-related measurements that would satisfy the size, weight and power constraints of a sUAS payload. Here we report on a remote sensing system developed under the NightFOX project that consists of three optical instruments with five individual sensors for wildfire mapping and fire radiative power measurement and a GPS-aided inertial navigation system module for aircraft position and attitude determination. The first instrument consists of two scanning telescopes with infrared (IR) channels using narrow wavelength bands near 1.6 and 4 µm to make fire radiative power measurements with a blackbody equivalent temperature range of 320-1500 °C. The second instrument is a broadband shortwave (0.95-1.7 µm) IR imager for high spatial resolution fire mapping. Both instruments are custom built. The third instrument is a commercial off-the-shelf visible/thermal IR dual camera. The entire system weighs about 1500 g and consumes approximately 15 W of power. The system has been successfully operated for fire observations using a Black Swift Technologies S2 small, fixed-wing UAS for flights over a prescribed grassland burn in Colorado and onboard an NOAA Twin Otter crewed aircraft over several western US wildfires during the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field mission.

Research and calculate flight navigation parameters using data from the angular speed sensor and long acceleration sensor

Today, along with the development of science and technology, inertial navigation systems are applied with precision sensors and high-speed specialized digital computers. With the advantages of compactness and ease of integration and installation on aircraft, this type of navigation system is effectively applied to small and medium-sized unmanned aerial vehicles. When combined with other systems of the same function, such as satellite navigation systems, radio altimeter systems, barometric altimeter systems, etc., this system allows for the provision of angle and position information with high precision. In this paper, the Inertial Labs INS-P Professional Single Antenna GPS-Aided Inertial Navigation System was selected by the authors to improve the aircraft navigation system.

Indirect Estimation of Vertical Ground Reaction Force from a Body-Mounted INS/GPS Using Machine Learning.

Vertical ground reaction force (vGRF) can be measured by force plates or instrumented treadmills, but their application is limited to indoor environments. Insoles remove this restriction but suffer from low durability (several hundred hours). Therefore, interest in the indirect estimation of vGRF using inertial measurement units and machine learning techniques has increased. This paper presents a methodology for indirectly estimating vGRF and other features used in gait analysis from measurements of a wearable GPS-aided inertial navigation system (INS/GPS) device. A set of 27 features was extracted from the INS/GPS data. Feature analysis showed that six of these features suffice to provide precise estimates of 11 different gait parameters. Bagged ensembles of regression trees were then trained and used for predicting gait parameters for a dataset from the test subject from whom the training data were collected and for a dataset from a subject for whom no training data were available. The prediction accuracies for the latter were significantly worse than for the first subject but still sufficiently good. K-nearest neighbor (KNN) and long short-term memory (LSTM) neural networks were then used for predicting vGRF and ground contact times. The KNN yielded a lower normalized root mean square error than the neural network for vGRF predictions but cannot detect new patterns in force curves.

A GPS-aided Inertial Navigation System for Vehicular Navigation using a Smartphone

The accuracy of a low-cost inertial navigation system is highly dependent on the overall accuracy provided by the respective sensors and the quality of the mathematical modeling of the error sources. Sensor fusion allows to alleviate and bound the occurring errors, resulting in increased precision of the navigation solution. This paper investigates the standard integration techniques of aiding sources in an inertial navigation system for vehicular applications illustrated by the example of Pedelecs. For the realization of this application, a loosely-coupled integration scheme using an error-state Kalman filter in a closed-loop fashion is employed. As the tuning of the filter requires a delicate adjustment of the respective covariance matrices, a time domain technique was assessed to analyze and quantify the error sources in the reported measurements by the respective sensors. Evaluation is based on real data obtained from two field tests using the developed GPS-aided inertial navigation system.

Design and implementation of a model predictive observer for AHRS

A GPS-aided Inertial Navigation System (GAINS) is used to determine the orientation? position and velocity of ground and aerial vehicles. The data measured by Inertial Navigation System (INS) and GPS are commonly integrated through an Extended Kalman Filter (EKF). Since the EKF requires linearized models and complete knowledge of predefined stochastic noises? the estimation performance of this filter is attenuated by unmodeled nonlinearity and bias uncertainties of MEMS inertial sensors. The Attitude Heading Reference System (AHRS) is applied based on the quaternion and Euler angles methods. A moving horizon-based estimator such as Model Predictive Observer (MPO) enables us to approximate and estimate linear systems affected by unknown uncertainties. The main objective of this research is to present a new MPO method based on the duality principle between controller and observer of dynamic systems and its implementation in AHRS mode of a low-cost INS aided by a GPS. Asymptotic stability of the proposed MPO is proven by applying Lyapunov's direct method. The field test of a GAINS is performed by a ground vehicle to assess the long-time performance of the MPO method compared with the EKF. Both the EKF and MPO estimators are applied in AHRS mode of the MEMS GAINS for the purpose of real-time performance comparison. Furthermore? we use flight test data of the GAINS for evaluation of the estimation filters. The proposed MPO based on both the Euler angles and quaternion methods yields better estimation performances compared to the classic EKF.

IMPLEMENTATION OF ANFIS FOR GPS-AIDED INS UAV MOTION SENSING AT SHORT TERM GPS OUTAGE

The recent improvement in Micro-Electro-Mechanical System (MEMS) technology has enabled the evolvement of Inertial Navigation Unit (INU) to be built on top of a low cost, small size Integrated Circuit (IC) chip. Due to the nature of the MEMS INU, its outputs are normally corrupted by the resided stochastic noise. A common practice to regulate its measurements into usable motion data is by fusing the Global Positioning System (GPS) measurement data with the MEMS INU measurement data through Kalman filter for position, velocity and orientation estimations. Such integrated system is known as GPS-aided Inertial Navigation System (INS). Note that the robustness of the GPS-aided INS relies heavily on the availability of the GPS signals. In the event of no GPS signals, the overall system will solely depend on the INU to predict the position, velocity and orientation. The prediction results will eventually drift from its true value due to the INU's resided stochastic noise. In this study, a remedy system using Adaptive Neuro-Fuzzy Inference System (ANFIS) is developed to improve the performance of the GPS-aided INS during GPS outage condition. UAV motion sensing experiment was carried out and GPS outage conditions were imposed at several locations during the UAV navigation. The motion prediction dataduring GPS outages, with and without ANFIS implementation, were compared and the results clearly show that the GPS-aided INS with ANFIS implementation achieved better performance than the GPS-aided INS without ANFIS.

Differential GPS aided Inertial Navigation: a Contemplative Realtime Approach

Extended Kalman Filtering (EKF) based GPS-INS has poor performance when there are strong nonlinearities and the measurements are corrupted by noise or outliers. This paper proposes a Contemplative Realtime (CRT) framework for tightly coupled Differential GPS aided inertial navigation. This method guarantees low latency for real-time application and meanwhile enhances the accuracy and reliability of navigation. To realize these improvements, a Bayesian optimization based smoothing is combined with conventional filtering in an efficient way. For demonstration purpose, this CRT framework is implemented on an automotive vehicle with differential pseudorange and IMU measurements. The implementation result shows that compared to standard EKF method, the proposed approach could provide accurate vehicle navigation with robustness.

Accuracy Evaluation of Stereo Vision Aided Inertial Navigation for Indoor Environments

Abstract. Accurate knowledge of position and orientation is a prerequisite for many applications regarding unmanned navigation, mapping, or environmental modelling. GPS-aided inertial navigation is the preferred solution for outdoor applications. Nevertheless a similar solution for navigation tasks in difficult environments with erroneous or no GPS-data is needed. Therefore a stereo vision aided inertial navigation system is presented which is capable of providing real-time local navigation for indoor applications. A method is described to reconstruct the ego motion of a stereo camera system aided by inertial data. This, in turn, is used to constrain the inertial sensor drift. The optical information is derived from natural landmarks, extracted and tracked over consequent stereo image pairs. Using inertial data for feature tracking effectively reduces computational costs and at the same time increases the reliability due to constrained search areas. Mismatched features, e.g. at repetitive structures typical for indoor environments are avoided. An Integrated Positioning System (IPS) was deployed and tested on an indoor navigation task. IPS was evaluated for accuracy, robustness, and repeatability in a common office environment. In combination with a dense disparity map, derived from the navigation cameras, a high density point cloud is generated to show the capability of the navigation algorithm.

Navigation Facility for High Accuracy Offline Trajectory and Attitude Estimation in Airborne Applications

The paper focuses on a navigation facility, relying on commercial-off-the-shelf (COTS) technology, developed to generate high-accuracy attitude and trajectory measurements in postprocessing. Target performance is cm-level positioning with tenth of degree attitude accuracy. The facility is based on the concept of GPS-aided inertial navigation but comprises carrier-phase differential GPS (CDGPS) processing and attitude estimation based on multiantenna GPS configurations. Expected applications of the system include: (a) performance assessment of integrated navigation systems, developed for general aviation aircraft and medium size unmanned aircraft systems (UAS); (b) generation of reference measurements to evaluate the flight performance of airborne sensors (e.g., radar or laser); and (c) generation of reference trajectory and attitude for improving imaging quality of airborne remote sensing data. The paper describes system architecture, selected algorithms for data processing and integration, and theoretical performance evaluation. Experimental results are also presented confirming the effectiveness of the implemented approach.

INS/GPS/MNS Integrated Navigation System with Federated Kalman Filtering

A multi-sensor integrated solution that combine complementary features of the Global Positioning System (GPS), inertial navigation system (INS), and magnetometer is presented due to GPS-aided inertial navigation system (INS) provides poor observability of heading angle. In addition, Based on the principle of federated Kalman filtering and Adaptive Extended Kalman Filter, the algorithm is presented also for accuracy of positioning and attitude, rapidity, and error tolerance of the navigation system. The algorithm is implemented in the integrated navigation system. Experimental results show that the observability issue is solved and improvement in accuracy.

Integration of a Triaxial Magnetometer into a Helicopter UAV GPS-Aided INS

The University of Alberta's Applied Nonlinear Controls Lab (ANCL) helicopter unmanned aerial vehicle's (UAV's) existing GPS-aided inertial navigation system (INS) does not provide observability of heading angle during hover. This motivates the integration of a triaxial magnetometer aiding sensor into the extended Kalman filter (EKF)-based navigation algorithm. A novel magnetometer calibration procedure is implemented and compared with conventional approaches. Experimental results in ground and flight testing confirm that the observability issue is resolved and demonstrate improvements in attitude estimation.

Low Cost Multisensor Kinematic Positioning and Navigation System with Linux/RTAI

Despite its popularity, the development of an embedded real-time multisensor kinematic positioning and navigation system discourages many researchers and developers due to its complicated hardware environment setup and time consuming device driver development. To address these issues, this paper proposed a multisensor kinematic positioning and navigation system built on Linux with Real Time Application Interface (RTAI), which can be constructed in a fast and economical manner upon popular hardware platforms. The authors designed, developed, evaluated and validated the application of Linux/RTAI in the proposed system for the integration of the low cost MEMS IMU and OEM GPS sensors. The developed system with Linux/RTAI as the core of a direct geo-referencing system provides not only an excellent hard real-time performance but also the conveniences for sensor hardware integration and real-time software development. A software framework is proposed in this paper for a universal kinematic positioning and navigation system with loosely-coupled integration architecture. In addition, general strategies of sensor time synchronization in a multisensor system are also discussed. The success of the loosely-coupled GPS-aided inertial navigation Kalman filter is represented via post-processed solutions from road tests.

Time Synchronization Errors in Loosely Coupled GPS-Aided Inertial Navigation Systems

The effects of data time synchronization errors in a loosely coupled Global-Positioning-System (GPS)-aided inertial navigation system (INS) are studied and quantified in terms of the increased mean square error (MSE) of the navigation solution. An expression for evaluating the MSE of the navigation solution, given the vehicle trajectory and the model of the INS error dynamics, is derived. Thereafter, a software-based time synchronization method, where the time synchronization error is included as a state to be estimated by the data integration filter, is proposed. A practical approach to the implementation of the proposed time synchronization method is also briefly described. Moreover, an expression for the MSE of the navigation solution in the system that employs the proposed synchronization method is derived. Finally, through simulations and tests with real-world data, the correctness of the derived MSE expressions is validated, and the application of the proposed synchronization method is shown. The test results show that, with the proposed synchronization approach, a data time synchronization, which is accurate to the order of a few milliseconds, can be achieved.

Unmanned space vehicle technology demonstrator

The unmanned space vehicle (USV) program has been undertaken by the Italian Center for Aerospace Research with the aim of developing flying test beds of next generation reentry launch vehicles. In this framework, the development of small demonstrators is also foreseen to validate technological and operational aspects of full-scale vehicles and missions. In this paper, a small-scale demonstrator of the sub-orbital re-entry test mission of the USV program is described. Both mission profile and objectives are very challenging in terms of demonstrator guidance, navigation and control. After a short description of the mission and demonstrator architectures, particular emphasis is given to the guidance and navigation analysis. To this end, mission objectives and reduced-scale system constaints are integrated and translated into innovative guidance solutions relying on optimization techniques. Then, performance of a commercial-off-the-shelf GPS-aided, miniature inertial navigation system over the proposed trajectories is evaluated by Monte Carlo analysis. Standalone inertial and GPS-aided inertial navigation performance is also compared considering GPS loss conditions due to antenna plasma effects.

Development of GPS Aided Inertial Navigation System for High Speed Flight Demonstrator

Development of GPS Aided Inertial Navigation System for High Speed Flight Demonstrator

高精度ＧＰＳ補強型慣性航法システムの開発

高精度ＧＰＳ補強型慣性航法システムの開発

GPS Aided Inertial Navigation

The Global Positioning System is an extremely accurate satellite-based navigation system which, after its completion in 1989, will provide users worldwide, 24 hour. all weather coverage. A joint research project among Boeing, Rockwell-Collins, and Northrop has been completed in which a GPS receiver was integrated with a low-cost strap-down inertial navigation system and a flight computer. A Kalman filter in the latter allows in-fight alignment and calibration of the INS. In addition, feedback from the INS to the GPS receiver improves the system's ability to reacquire satellite signals after outages. The resulting system combines the accuracy of GPS with the jamming immunity and autonomy of inertial navigation. System tests were conducted in which a Boeing owned T-33 jet aircraft was flown through known test pattern to align and calibrate the INS. Earlier tests, including tests against an airborne jammer, were conducted in a modified passenger bus.