GPS/INS

About: GPS/INS is a research topic. Over the lifetime, 3554 publications have been published within this topic receiving 62784 citations.

A novel hybrid fusion algorithm to bridge the period of GPS outages using low-cost INS

Abstract: Land Vehicle Navigation (LVN) mostly relies on integrated system consisting of Inertial Navigation System (INS) and Global Positioning System (GPS). The combined system provides continuous and accurate navigation solution when compared to standalone INS or GPS. Different fusion methodology such as those based on Kalman filtering and particle filtering has been proposed that estimates and models the INS error during the GPS signal availability. In the case of outages, the developed model provides an INS error estimates, thereby improving its accuracy. However, these fusion approaches possess several inadequacies related to sensor error model, immunity to noise and computational load. Alternatively, Neural Network (NN) based approaches has been proposed. In the case of low-cost INS, the NN suffers from poor generalization capability due to the presence of high amount of noises. The paper thus introduces a novel and hybrid fusion methodology utilizing Dempster-Shafer (DS) theory augmented by Support Vector Machines (SVM), known as DS-SVM. The INS and GPS data fusion is carried using DS fusion whereas SVM models the INS error. During GPS availability, DS provides accurate solution; whereas during outages, the trained SVM model corrects the INS error thereby improving the positioning accuracy. The proposed methodology is evaluated against the existing Artificial Neural Network (ANN) and the Random Forest Regression (RFR) methodology. A total of 20-87% improvement in the positional accuracy was found against ANN and RFR.

A new approach for airborne vector gravimetry using GPS/INS

Abstract: A new method for airborne vector gravimetry using GPS/INS has been developed and the results are presented. The new algorithm uses kinematic accelerations as updates instead of positions or velocities, and all calculations are performed in the inertial frame. Therefore, it is conceptually simpler, easier, more straightforward and computationally less expensive compared to the traditional approach in which the complex navigation equations should be integrated. Moreover, it is a unified approach for determining all three vector components, and no stochastic gravity modeling is required. This approach is based on analyzing the residuals from the Kalman filter of sensor errors, and further processing with wavenumber coefficient filterings is applied in case closely parallel tracks of data are available. An application to actual test-flight data is performed to test the validity of the new algorithm. The results yield an accuracy in the down component of about 3–4 mGal. Also, comparable results are obtained for the horizontal components with accuracies of about 6 mGal. The gravity modeling issue is discussed and alternative methods are presented, none of which improves on the original approach.

Tightly Coupled INS/GPS with Bias Estimation for UAV Applications

Abstract: This paper presents an analysis of a proposed tightly coupled Inertial/GPS navigation system for UAV applications. An indirect, error state extended Kalman filter is employed to estimate vehicle position, velocity, attitude and IMU bias errors. The indirect configuration linearises the state transition matrix and greatly reduces the required feedback frequency. Standard GPS C/A code pseudo-ranges are used directly to update the Kalman filter. The advantage of this configuration arises in situations of poor GPS availability. Traditional loosely coupled filters can deliver no new information to the filter when the observed satellite number falls below four. In the tightly coupled configuration, all available information is delivered to the filter even in situations where only one satellite remains observable. The extension of the vehicle states to include IMU biases further improves navigation accuracy by constraining drift during the INS alone cycle and in periods of low GPS observability. Both algorithms are suited to a low cost implementation. Results from flight trials of the Brumby Mk. III UAV are presented.

Integrated Navigation and Guidance Systems

Abstract: Principles of Navigation and the Concept of an Integrated Navigation System Newton's Laws Applied to Navigation (Geodesics, Basic Reference Frames, Simplified Aerospace Vehicle Equation) Inertial Navigation Systems (INS) and Global Positioning System (GPS) Uncertainty in Navigation, INS Error Propagation, Probabilities, Autocorrelation and the Method of Least Squares Kalman Filters and Their Key Role in the Integration of Aircraft Avionics Systems GPS Theory and its Application to Navigation (Including System Accuracy) GPS Application to Precision Approach and Landing, Attitude Control and Air Traffic Control Flight Testing of Navigation Systems Computer Exercises.