Inertial Navigation System Measurements Research Articles

An implementation of the cubature Kalman filter for estimating trajectory parameters and air data of a hypersonic vehicle

Trajectory parameters (including the position, velocity, and attitude angles of a vehicle) and air data (consisting of the flow angles, the Mach number, and the freestream static pressure) are vital data for the analysis and evaluation process in the hypersonic flight tests. This paper describes a data fusion estimation algorithm for a flush air data sensing system/inertial navigation system/global positioning system integrated system, which is used to estimate the trajectory parameters and air data for an unpowered hypersonic vehicle. In the approach, the raw outputs of flush air data sensing system (i.e. the surface pressure measurements) are integrated with global positioning system results (the vehicle’s position and velocity) and inertial navigation system measurements (including the acceleration and the angular velocity measurements) by using a nonlinear Kalman filter algorithm. Firstly, the system state vector is defined with the trajectory parameters, the biases of the inertial sensors and the winds. Then, the system dynamic models are built based on the motion equations of an unpowered hypersonic vehicle, the inertial sensor error models and the wind model. Besides, the system measurement vector is designed with the global positioning system results and the flush air data sensing system raw outputs. Based on these works, the system state is directly estimated by using the cubature Kalman filter algorithm. After that, the air data is calculated based on the estimated values and a high-fidelity model of atmosphere. Simulation cases are implemented to assess the performance of the proposed algorithm. The results show that the proposed method could estimate the trajectory parameters and air data for hypersonic vehicle with a high precision.

Combining Stereo Vision and Inertial Navigation for Automated Aerial Refueling

This paper describes the design of an extended Kalman filter to obtain the precise relative position of two aircraft in a refueling maneuver while operating in GPS-denied environments. The extended Kalman filter uses the inertial navigation system already present in both aircraft as well as the stereo camera system organic to new tanker systems. The aircraft trajectories are generated to represent an authentic refueling profile with flight dynamics software and executed in a three-dimensional virtual environment to enable deterministic simulation of the stereo camera system and to demonstrate the effectiveness of the combined system in an authentic refueling scenario. Results show the system can achieve sufficient accuracy using only stereo machine vision and inertial navigation system measurements, though the system is capable of incorporating GPS measurements when available for an additional increase in accuracy.

Detecting Global Navigation Satellite System Spoofing Using Inertial Sensing of Aircraft Disturbance

Vulnerability of Global Navigation Satellite System users to signal spoofing is a critical threat to positioning integrity, especially in aviation applications where the consequences are potentially catastrophic. Spoofing may even become a more serious risk to aviation in the near future with the rollout of the Global Navigation Satellite System-based next-generation air traffic control system and the corresponding reduction in reliance on ground-based radar systems by air traffic control. In this work, it was shown that, for an aircraft equipped with an inertial navigation system, the vehicle dynamic response to wind gusts provides an advantage in detecting a spoofing attack. The reason is that the aircraft’s response to a gust will be instantaneously reflected in inertial navigation system measurements but not in the spoofed Global Navigation Satellite System signal. The main contribution of this work is the development of a rigorous methodology to compute upper bounds on the integrity risk resulting from a worst-case spoofing attack, without needing to simulate individual aircraft approaches with an unmanageably large number specific gust disturbance profiles. A Boeing 747 aircraft model is used to demonstrate the performance of the inertial navigation system monitor under worst-case Global Navigation Satellite System spoofing and to investigate gust intensity levels that are sufficient to meet integrity risk requirements for precision approach and landing.

An iterative method for the accurate determination of airborne gravity horizontal components using strapdown inertial navigation system/global navigation satellite system

In strapdown inertial navigation system and global navigation satellite system-based airborne gravimetry, there is a circular problem between the navigation solution and the gravity vector estimation. On one hand, the gravity vector estimation depends on the navigation solution. On the other hand, the navigation solution requires a predetermined gravity model. The normal gravity, which differs from the actual gravity by an amount known as the gravity disturbance, is commonly used for the navigation solution, and this will result in errors of gravimetry. We reviewed this problem and found that an iterative method can be effective if there were no gyroscope biases in the inertial navigation system measurements. Iteratively, the differences between the estimated gravity vector and actual gravity vector can converge to constant values in this specific condition. If we know the gravity values at the endpoints of each survey line, these constant values can be compensated for by matching these endpoints. After such compensations, the repeatability of the estimated horizontal gravity components can be improved significantly. An application to real airborne data was performed to test the validity of the new method. The results yielded an internal accuracy in the horizontal component of approximately 3 mGal with a spatial resolution of 4.8 km.

A Particle Filter for Smartphone-Based Indoor Pedestrian Navigation

This paper considers the problem of indoor navigation by means of low-cost mobile devices. The required accuracy, the low reliability of low-cost sensor measurements and the typical unavailability of the GPS signal make indoor navigation a challenging problem. In this paper, a particle filtering approach is presented in order to obtain good navigation performance in an indoor environment: the proposed method is based on the integration of information provided by the inertial navigation system measurements, the radio signal strength of a standard wireless network and of the geometrical information of the building. In order to make the system as simple as possible from the user’s point of view, sensors are assumed to be uncalibrated at the beginning of the navigation, and an auto-calibration procedure of the magnetic sensor is performed to improve the system performance: the proposed calibration procedure is performed during regular user’s motion (no specific work is required). The navigation accuracy achievable with the proposed method and the results of the auto-calibration procedure are evaluated by means of a set of tests carried out in a university building.

Using GPS with a model-based estimator to estimate critical vehicle states

This paper demonstrates a method to estimate the vehicle states sideslip, yaw rate, and heading using GPS and yaw rate gyroscope measurements in a model-based estimator. The model-based estimator using GPS measurements provides accurate and observable estimates of sideslip, yaw rate, and heading even if the vehicle model is in neutral steer or if the gyro fails. This method also reduces estimation errors introduced by gyroscope errors such as the gyro bias and gyro scale factor. The GPS and Inertial Navigation System measurements are combined using a Kalman filter to generate estimates of the vehicle states. The residuals of the Kalman filter provide insight to determine if the estimator model is correct and therefore providing accurate state estimates. Additionally, a method to predict the estimation error due to errors in the estimator model is presented. The algorithms are tested in simulation with a correct and incorrect model as well as with sensor errors. Finally, the estimation scheme is tested with experimental data using a 2000 Chevrolet Blazer to further validate the algorithms.