GPS-aided Inertial Navigation System 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.

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.

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.

A posteriori estimation of stochastic model for multi-sensor integrated inertial kinematic positioning and navigation on basis of variance component estimation

Improving a priori stochastic models of the process and measurement noise vectors in Kalman Filer (KF) has always been a challenge. As one preferable technique to address this challenge, the variance component estimation (VCE) applied on the Kalman Filter’s process and measurement noise covariance matrix (Q & R) has been proved in plenty of applications. Unsurprisingly, VCE was expected to re-establish the stochastic model about the random errors in the IMU’s measurements in a multisensor integrated positioning and navigation system applying Kalman Filter. However, in the conventional error states-based GPS aided inertial navigation system (GPS/INS), the stochastic model tuning is difficult for the IMU’s measurements due to the amalgamation of the observables from inertial sensor and other aiding sensors. This paper proposes a generic method for the stochastic model tuning about the random errors in IMU measurements together with other sensors. The core of this novel approach is based on an innovative multisensor integration strategy which deploys upon the vehicle’s generic kinematic model and takes the IMU’s output as raw measurements in Kalman Filter (IMU/GNSS Kalman Filter). As a result, the statistical orthogonality between random error vectors of any two sensors enables the separate but parallel statistics collection of each individual random error source. Given these independent statistics corresponding to each error source, the VCE technique iteratively tunes all stochastic model of the process and measurement noise vectors. The success of the VCE algorithm is shown through a real dataset involving GPS and inertial sensors.

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.

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.

Magnetometer and differential carrier phase GPS-aided INS for advanced vehicle control

This paper describes the design, analysis, implementation, and experimental results of a triple redundancy navigation system incorporating magnetometer, inertial, and carrier phase differential Global Positioning System (GPS) measurements. The navigation system is able to accurately estimate vehicle attitude (including yaw) as long as the vehicle velocity is not zero. The motivating application was lateral vehicle control for intelligent highway systems. The system was designed to operate reliably whether or not GPS and magnetometer measurements were available. The navigation system provides vehicle position, velocity, acceleration, pitch and roll, yaw, and angular rates at 150 Hz with accuracies (standard deviation) of 2.8 cm, 0.8 cm/s, 2.2 cm/s/s, 0.03/spl deg/, 0.18/spl deg/, and 0.1/spl deg//s. This navigation state vector was processed to produce a control state vector at approximately 30 Hz. This triplicate redundancy navigation system reliably demonstrated lateral vehicle control in the following situations: both GPS and magnetometer aiding the inertial navigation system (INS), GPS-aided INS, magnetometer aided-INS, and switching between GPS and magnetometer aiding of the INS at random times.

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

Real-time differential carrier phase GPS-aided INS

This article describes the implementation and experimental results of a real-time carrier phase differential Global Positioning System (GPS) aided inertial navigation system (INS). The implementation is the result of a study to analyze the capabilities of such a system relative to the requirements of advanced vehicle control and safety systems for intelligent transportation systems. Such navigation systems have many application possibilities (e,g., aviation and precision flight, automated mining, precision farming, dredging, satellite attitude control). Advantages and disadvantages of the GPS, INS, and differential GPS-aided INS approaches are discussed. The implementation achieves 100-Hz vehicle state estimates with position accuracies at the centimeter level through the use of differential carrier phase GPS techniques.

Balloon gravimetry using GPS and INS

Measurement of the horizontal components of gravity at altitude using balloon-borne instrumentation consisting of a Global Positioning System (GPS) receiver and a strapdown inertial navigation system (INS) is discussed. GPS data are to be used primarily to determine the total inertial acceleration of the balloon, while the INS accelerometers sense all nongravitational accelerations. A covariance analysis based on the Kalman filter shows that conventional gravity estimation from GPS-aided INS data is possible only if external attitude updates are also available. An alternative technique is explored that attempts to estimate at least part of the gravitational spectrum without modeling the gravity disturbance as a state variable or relying on external attitude updates, while, at the same time, admitting uncorrected (long-wavelength) attitude errors. Simulations based on a model for typical balloon motion are used to discuss this possibility.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

GPS/INS integration on the Standoff Land Attack Missile (SLAM)

The Standoff Land Attack Missile (SLAM) is a worldwide, all-weather, precision-strike weapon system deployed from carrier-based aircraft. In the primary mode of operation, target location and other mission data are generated from intelligence sources available on the aircraft carrier and loaded into the missile prior to aircraft takeoff. After missile launch, the SLAM inertial navigation system (INS) guides the missile along the planned trajectory. Updating the missile INS from the Global Positioning System (GPS) during flight provides precise midcourse navigation and enhances target acquisition by accurate, on-target pointing of the SLAM Maverick seeker. The GPS/INS avionics and software integration used for SLAM are described in detail, along with some of the design tradeoffs that led to the approach. The avionics configuration integrates the Harpoon midcourse guidance unit, which includes a strapdown inertial sensor package and digital processor, with a Rockwell-Collins single-channel, sequential GPS receiver processor unit (RPU), a derivative of the GPS phase-III user equipment. In addition to the GPS receiver elements the RPU contains the navigation processor, which executes the SLAM navigation, Kalman filter algorithms, and other guidance algorithms including seeker pointing. Flight-test results of the SLAM GPS-aided INS are also included.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>