Two multi-sensor architectures for navigation and guidance of small Unmanned Aircraft (UA) are proposed and compared in this paper. These architectures are based respectively on a standard Extended Kalman Filter (EKF) approach and a more advanced Unscented Kalman Filter (UKF) approach for data fusion of Global Navigation Satellite Systems (GNSS), Micro-Electro-Mechanical System (MEMS) based Inertial Measurement Unit (IMU) and Vision Based Navigation (VBN) sensors. The main objective is to design a compact, light and relatively inexpensive system capable of providing the Required Navigation Performance (RNP) in all phases of flight of small UA, with a special focus on precision approach and landing. The novelty of this paper is the augmentation of Aircraft Dynamics Model (ADM) in both architectures to compensate for the MEMS-IMU sensor shortcomings in high-dynamics attitude determination tasks. Additionally, the ADM measurements are pre-filtered by an UKF with the purpose of increasing the ADM attitude solution stability time in the UKF based system. After introducing the key mathematical models describing the two architectures, the EKF based VBN-IMU-GNSS-ADM (VIGA) system and the UKF based system (VIGA +) performances are compared in a small UA integration scheme (i.e., AEROSONDE UA platform) exploring a representative cross-section of this UA operational flight envelope, including high dynamics manoeuvres and CAT-I to CAT-III precision approach tasks. The comparison shows that the position and attitude accuracy of the proposed VIGA and VIGA + systems are compatible with the Required Navigation Performance (RNP) specified in the various UA flight profiles, including precision approach down to CAT-II.
This paper presents a Particle Filter (PF) based Multi-Sensor Data Fusion (MSDF) technique in an integrated Navigation and Guidance System (NGS) design based on low-cost avionics sensors. The performance of PF based MSDF method is compared with other previously implemented data fusion architectures for small-sized Remotely Piloted Aircraft Systems (RPAS). The sensor suite of the implemented NGS includes; Global Navigation Satellite System (GNSS) sensor, which is adopted as the primary means of navigation, Micro-Electro-Mechanical System (MEMS) based Inertial Measuring Unit (IMU) and Vision-Based Navigation (VBN) sensor. Additionally, an Aircraft Dynamics Model (ADM) is used as a virtual sensor to compensate for the MEMS-IMU sensor shortcomings in highdynamics attitude determination tasks. The PF is specifically implemented to increase the accuracy of navigation solution obtained from the inherently inaccurate, low-cost Commercial-Off-The-Shelf (COTS) sensors. Simulations are carried out on the AEROSONDE RPAS performing high-dynamics manoeuvres representative of the RPAS operational flight envelope. The Extended Kalman Filter (EKF) based VBN-IMU-GNSS-ADM (E-VIGA) system, Unscented Kalman Filter (UKF) based U-VIGA system and the PF based P-VIGA system performances are evaluated and compared. Additionally, an error covariance analysis is performed on the centralised filter using Monte Carlo simulation. Results indicate that the PF is computationally expensive as the number of particles is increased. Compared to E-VIGA and U-VIGA systems, P-VIGA system shows an improvement of accuracy in the position, velocity and attitude measurements.
Low-cost sensors based multi-sensor data fusion techniques for RPAS navigation and guidance.
In this paper, Aircraft Dynamics Model (ADM) augmentation for Remotely Piloted Aircraft System (RPAS) navigation and guidance is presented. This approach provides additional information suitable to compensate for the shortcomings of vision based navigation sensors and Micro-Electromechanical System Inertial Measurement Unit (MEMS-IMU) sensors for attitude determination tasks. The ADM virtual sensor is essentially a knowledge-based module and is used to augment the navigation state vector by predicting RPAS flight dynamics (aircraft trajectory and attitude motion). The ADM employs a rigid body 6-Degree of Freedom (6-DoF) model and is implemented in integrated multi-sensor data fusion architectures. The integration is accomplished with an Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). After introducing the key mathematical models describing the 6-DoF ADM, the sensor and integrated system performance are compared in a small RPAS integration scheme (i.e., AEROSONDE RPAS platform) exploring a representative cross-section of the aircraft operational flight envelope and a preliminary sensitivity analysis is performed. In addition to a centralised filter, a dedicated ADM processor (i.e., a local pre-filter) is adopted to account for the RPAS manoeuvring envelope in different flight phases, in order to extend the ADM validity time across all segments of the RPAS trajectory. Sensitivity analysis of the errors caused by perturbations in the input parameters of the aircraft dynamics is performed to demonstrate the robustness of the proposed approach. Results verify that the ADM virtual sensor provides improved performance in terms of attitude data accuracy and a significant extension of the ADM validity time is achieved by pre-filtering.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.