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

Abstract

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.

Magnetometer Calibration for Small Unmanned Aerial Vehicles Using Cooperative Flight Data

“…In this respect, this work lies in the framework of research activities, carried out at the Department of Industrial Engineering of the University of Naples "Federico II", aiming at investigating the possibility to exploit cooperative strategies to improve navigation performance of mini/micro UAVs (MAVs) [11,12]. If these vehicles fly under nominal Global Navigation Satellite System (GNSS) coverage, autonomous and safe navigation is enabled by integrating measurements from low-cost GNSS receivers and commercial-grade micro-electro-mechanical systems (MEMS)-based inertial (i.e., gyroscopes Sensors 2020, 20, 538 2 of 17 and accelerometers) and magnetic sensors, within Extended Kalman Filters [13,14] or, more recently, Particle Filters [15], and using either loosely-or tightly-coupled architectures [16]. In this respect, since onboard gyros do not have enough sensitivity to measure the Earth rate vector, magnetometers play a key role, despite being typically characterized by low bandwidth and high measurement noise.…”

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

“…The requirements for RPAS to be integrated into all classes of airspace are illustrated in Figure 1. Enhanced surveillance solutions based on Automatic Dependent Surveillance-Broadcast (ADS-B) system and Traffic Collision Avoidance System (TCAS) are required for addressing cooperative DAA functions [11][12][13][14][15][16][17][18]. The Performance Based Navigation (PBN) concept specifies that aircraft RNP and area navigation (RNAV) requirements have to be met for all flight phases [11].…”

“…Vision Based Navigation (VBN) sensors are typically adopted for precision approach and landing (i.e., the most demanding and potentially safety-critical flight phase). Aircraft Dynamics Models (ADM) are not currently employed in any commercial systems as a virtual sensor, however previous research [19,20,22,23] shows that it can act as a knowledge-based module augmenting the navigation state vector and thus increasing the accuracy of the navigation solution [19,20,22,23]. The performance of the ADM is satisfactory only for a short time period and hence requires reinitialisation.…”

“…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].…”

Application of Multi-sensor Information Fusion Based on Improved Particle Swarm Optimization in Unmanned System Path Planning

“…The establishment of a modern intelligent transportation system will be mentioned on the agenda. Intelligent Vehicles, as an important part of Intelligent Transportation Systems, is also the main body of the system, which not only improves driving safety and road traffic efficiency, but also reduces energy consumption [1][2][3]. Due to many advantages, the technology research has been increasingly concerned about the relevant institutions at home and abroad.…”