Cooperative Localisation of a GPS-Denied UAV in 3-Dimensional Space Using Direction of Arrival Measurements

This paper presents a novel approach for localising a GPS (Global Positioning System)-denied Unmanned Aerial Vehicle (UAV) with the aid of a GPS-equipped UAV in three-dimensional space. The GPS-equipped UAV makes discrete-time broadcasts of its global coordinates. The GPS-denied UAV receives the broadcast and in doing so takes a direction of arrival (DOA) measurement towards the origin of the broadcast in its local coordinate frame (obtained via an inertial navigation system (INS)). The aim is to determine the difference between the local and global frames, described by a rotation and a translation. In the noiseless case, global coordinates are recovered exactly by solving a system of linear equations. When DOA measurements are contaminated with noise, rank relaxed semidefinite programming (SDP) and the Orthogonal Procrustes algorithm are employed. Simulations are provided and factors affecting accuracy, such as noise levels and number of measurements, are explored.This work was supported by the Australian Research Council (ARC) under the ARC grants DP-130103610 and DP-160104500 and by Data61-CSIRO (formerly NICTA)

Cooperative Localisation of a GPS-Denied UAV using Direction-of-Arrival Measurements

A GPS-denied UAV (Agent B) is localised through INS alignment with the aid of a nearby GPS-equipped UAV (Agent A), which broadcasts its position at several time instants. Agent B measures the signals' direction of arrival with respect to Agent B's inertial navigation frame. Semidefinite programming and the Orthogonal Procrustes algorithm are employed, and accuracy is improved through maximum likelihood estimation. The method is validated using flight data and simulations. A three-agent extension is explored

A Review of Radio Frequency Based Localization for Aerial and Ground Robots with 5G Future Perspectives

Efficient localization plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned aerial vehicles (UAVs), which would contribute to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities for enhancing localization of UAVs and UGVs. In this paper, we review the radio frequency (RF) based approaches for localization. We review the RF features that can be utilized for localization and investigate the current methods suitable for Unmanned vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localization for both UAVs and UGVs is examined, and the envisioned 5G NR for localization enhancement, and the future research direction are explored

Communication-based UAV Swarm Missions

Unmanned aerial vehicles have developed rapidly in recent years due to technological advances. UAV technology can be applied to a wide range of applications in surveillance, rescue, agriculture and transport. The problems that can exist in these areas can be mitigated by combining clusters of drones with several technologies. For example, when a swarm of drones is under attack, it may not be able to obtain the position feedback provided by the Global Positioning System (GPS). This poses a new challenge for the UAV swarm to fulfill a specific mission. This thesis intends to use as few sensors as possible on the UAVs and to design the smallest possible information transfer between the UAVs to maintain the shape of the UAV formation in flight and to follow a predetermined trajectory. This thesis presents Extended Kalman Filter methods to navigate autonomously in a GPS-denied environment. The UAV formation control and distributed communication methods are also discussed and given in detail

A Review of Radio Frequency Based Localisation for Aerial and Ground Robots with 5G Future Perspectives

Efficient localisation plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned Aerial Vehicles (UAVs), which contributes to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities to enhance the localisation of UAVs and UGVs. In this paper, we review radio frequency (RF)-based approaches to localisation. We review the RF features that can be utilized for localisation and investigate the current methods suitable for Unmanned Vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localisation for both UAVs and UGVs is examined, and the envisioned 5G NR for localisation enhancement, and the future research direction are explored

Bearing-Only Measurement Self-Localization,Velocity Consensus and Formation Control

Self-localization and formation control tasks are considered when each agent in a multiagent formation observes its neighbors but does not communicate. Each agent is restricted to a predefined motion type on a 2-D plane and collects bearing-only measurements over a time interval to localize neighboring agents. The localization process is used by a three agent formation to achieve velocity consensus combined with formation shape control. Simulations are provided and noisy bearing measurements are investigated.This work was supported in part by the Australian Research Council (ARC) under Grant DP-130103610 and Grant DP-160104500, in part by the National Natural Science Foundation of China under Grant 61375072, and in part by Data61-CSIRO (formerly NICTA)

D-SLATS: Distributed Simultaneous Localization and Time Synchronization

Through the last decade, we have witnessed a surge of Internet of Things (IoT) devices, and with that a greater need to choreograph their actions across both time and space. Although these two problems, namely time synchronization and localization, share many aspects in common, they are traditionally treated separately or combined on centralized approaches that results in an ineffcient use of resources, or in solutions that are not scalable in terms of the number of IoT devices. Therefore, we propose D-SLATS, a framework comprised of three different and independent algorithms to jointly solve time synchronization and localization problems in a distributed fashion. The First two algorithms are based mainly on the distributed Extended Kalman Filter (EKF) whereas the third one uses optimization techniques. No fusion center is required, and the devices only communicate with their neighbors. The proposed methods are evaluated on custom Ultra-Wideband communication Testbed and a quadrotor, representing a network of both static and mobile nodes. Our algorithms achieve up to three microseconds time synchronization accuracy and 30 cm localization error

Correlated-Data Fusion and Cooperative Aiding in GNSS-Stressed or Denied Environments

University of Minnesota Ph.D. dissertation. September 2014. Major: Aerospace Engineering and Mechanics. Advisor: Demoz Gebre-Egziabher. 1 computer file (PDF); xiii, 172 pages.A growing number of applications require continuous and reliable estimates of position, velocity, and orientation. Price requirements alone disqualify most traditional navigation or tactical-grade sensors and thus navigation systems based on automotive or consumer-grade sensors aided by Global Navigation Satellite Systems (GNSS), like the Global Positioning System (GPS), have gained popularity. The heavy reliance on GPS in these navigation systems is a point of concern and has created interest in alternative or back-up navigation systems to enable robust navigation through GPS-denied or stressed environments. This work takes advantage of current trends for increased sensing capabilities coupled with multilayer connectivity to propose a cooperative navigation-based aiding system as a means to limit dead reckoning error growth in the absence of absolute measurements like GPS. Each vehicle carries a dead reckoning navigation system which is aided by relative measurements, like range, to neighboring vehicles together with information sharing. Detailed architectures and concepts of operation are described for three specific applications: commercial aviation, Unmanned Aerial Vehicles (UAVs), and automotive applications. Both centralized and decentralized implementations of cooperative navigation-based aiding systems are described. The centralized system is based on a single Extended Kalman Filter (EKF). A decentralized implementation suited for applications with very limited communication bandwidth is discussed in detail. The presence of unknown correlation between the a priori state and measurement errors makes the standard Kalman filter unsuitable. Two existing estimators for handling this unknown correlation are Covariance Intersection (CI) and Bounded Covariance Inflation (BCInf) filters. A CI-based decentralized estimator suitable for decentralized cooperative navigation implementation is proposed. A unified derivation is presented for the Kalman filter, CI filter, and BCInf filter measurement update equations. Furthermore, characteristics important to the proper implementation of CI and BCInf in practice are discussed. A new covariance normalization step is proposed as necessary to properly apply CI or BCInf. Lastly, both centralized and decentralized implementations of cooperative aiding are analyzed and evaluated using experimental data in the three applications. In the commercial aviation study aircraft are simulated to use their Automatic Dependent Surveillance - Broadcast (ADS-B) and Traffic Collision Avoidance System (TCAS) systems to cooperatively aid their on board INS during a 60 min GPS outage in the national airspace. An availability study of cooperative navigation as proposed in this work around representative United States airports is performed. Availabilities between 70-100% were common at major airports like LGA and MSP in a 30 nmi radius around the airport during morning to evening hours. A GPS-denied navigation system for small UAVs based on cooperative information sharing is described. Experimentally collected flight data from 7 small UAV flights are played-back to evaluate the performance of the navigation system. The results show that the most effective of the architectures can lead to 5+ minutes of navigation without GPS maintaining position errors less than 200 m (1-σ). The automotive case study considers 15 minutes of automotive traffic (2,000 + vehicles) driving through a half-mile stretch of highway without access to GPS. Automotive radar coupled with Dedicated Short Range Communication (DSRC) protocol are used to implement cooperative aiding to a low-cost 2-D INS on board each vehicle. The centralized system achieves an order of magnitude reduction in uncertainty by aggressively aiding the INS on board each vehicle. The proposed CI-based decentralized estimator is demonstrated to be conservative and maintain consistency. A quantitative analysis of bandwidth requirements shows that the proposed decentralized estimator falls comfortably within modern connectivity capabilities. A naive implementation of the high-performance centralized estimator is also achievable, but it was demonstrated to be burdensome, nearing the bandwidth limits