An innovative navigation and guidance system for small unmanned aircraft using low-cost sensors

Abstract

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.

A Novel 3D Multilateration Sensor Using Distributed Ultrasonic Beacons for Indoor Navigation

“…This approach is particularly suitable for navigation of autonomous vehicles in indoor and urban environments and is optimally fit to overcome the limitations of radionavigation and Global Navigation Satellite Systems (GNSS) in these scenarios [2,5]. As the considered ultrasonic sensors are relatively inexpensive and easy to set up, the proposed positioning system has the potential to be scaled up to cover a bigger volume, opening up various operational applications.…”

“…Navigation systems including the Global Navigation Satellite System (GNSS) are not possible to use underwater, and in several air and ground vehicle applications, it is prone to data degradations or the complete loss of signal due to multipath effects, interference, and antenna obscuration [1,2,3,4,5]. Hence, GNSS signals are not reliable in urban landscapes comprised of tall buildings and their performance further deteriorates in an indoor environment.…”

Particle filter based multi-sensor data fusion techniques for RPAS navigation and guidance

“…GNSS and Micro-Electro-Mechanical System (MEMS) based Inertial Measuring Unit (IMU) are a highly synergistic combination of navigation sensors capable of providing an accurate navigation state vector better than any single sensor [5]. Vision-Based Navigation (VBN) sensors are specifically used for precision approach and landing (i.e., the most demanding and potentially safety-critical flight phase) [6]. Aircraft Dynamics Model (ADM) is a knowledge-based module and is used as a virtual sensor to augment the navigation state vector in high dynamics attitude determination tasks [6].…”

“…Vision-Based Navigation (VBN) sensors are specifically used for precision approach and landing (i.e., the most demanding and potentially safety-critical flight phase) [6]. Aircraft Dynamics Model (ADM) is a knowledge-based module and is used as a virtual sensor to augment the navigation state vector in high dynamics attitude determination tasks [6]. When pre-processed with estimation techniques, the ADM estimates the true RPAS flight dynamics (aircraft trajectory and attitude motion) [7].…”

Low-Cost RPAS Navigation and Guidance System using Square Root Unscented Kalman Filter

“…A number of variants for the original KF have been developed and are illustrated in Figure 2. Our previous research activities [13][14][15][16][17][18][19][20][21][22][23] presented the various sensor choices for data fusion and the overall implementation of the VBN/IMU/GNSS/ADM (VIGA) NGS and UKF based VIGA (U-VIGA) architecture. In this paper, we propose an integrated NGS approach using SR-UKF employing three state-of-the-art physical sensors: MEMS-IMU, GNSS and VBN sensors, as well as augmentation from ADM [4-8 and 20-22].…”