Proceedings ArticleDOI
Cooperative localization for fixed wing unmanned aerial vehicles
Anusna Chakraborty,Clark N. Taylor,Rajnikant Sharma,Kevin M. Brink +3 more
- pp 106-117
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TLDR
This paper compares the localization accuracy of the cooperative localization algorithm for different relative measurements such as range, bearing, range rate, and line-of-sight rate against the GPS data available from the flight test.Abstract:
In this paper, we investigate how relative measurements between fixed wing unmanned aerial vehicles (UAVs) and known landmarks can be used to cooperatively localize UAVs when GPS signals are not available. A centralized Extended Kalman Filter (EKF) is used to combine local sensor information from all the UAVs to estimate the required states of all the UAVs. We compare the localization accuracy of the cooperative localization algorithm for different relative measurements such as range, bearing, range rate, and line-of-sight rate. The dynamics, IMU, airspeed, and altimeter data used in the algorithm were collected from flight tests by Naval Postgraduate School [1]. However, the relative measurements are simulated using the flight data. The results are then compared against the GPS data available from the flight test.read more
Citations
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Journal ArticleDOI
UGV-to-UAV cooperative ranging for robust navigation in GNSS-challenged environments
TL;DR: It is shown that employing UGV-to-UAV cooperative navigation can reduce the positioning error of a UAV that is operating in a GNSS-challenged environment, from approximately 1-meter-level to approximately 10-cm-level 3D positioning error.
Journal ArticleDOI
Flight-testing of a cooperative UGV-to-UAV strategy for improved positioning in challenging GNSS environments
TL;DR: During a set of field tests, the positioning error of a UAV that is confronted with unfavorable GNSS satellite geometry is shown to be reduced by more than five-fold through the use of ranging updates from a UGV.
Journal ArticleDOI
Locating Multiple GPS Jammers Using Networked UAVs
TL;DR: This work proposes a simultaneous localization of multiple jammers and receivers (SLMR) algorithm by analyzing the variation in the front-end signal power recorded by the GPS receivers on-board a network of UAVs, and designs a Gaussian mixture probability hypothesis density filter over a graph framework.
Journal ArticleDOI
Cooperative Navigation of UAVs in GNSS-Denied Area With Colored RSSI Measurements
TL;DR: An improved extended Kalman filter is proposed to predict and correct the colored noise by adaptively integrating the current peer-to-peer radio ranging performance and its Allan variance.
References
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Mobile robot localization by tracking geometric beacons
TL;DR: An algorithm for, model-based localization that relies on the concept of a geometric beacon, a naturally occurring environment feature that can be reliably observed in successive sensor measurements and can be accurately described in terms of a concise geometric parameterization, is developed.
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A Probabilistic Approach to Collaborative Multi-Robot Localization
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Cooperative localization for autonomous underwater vehicles
James C. Preisig,Alexander Bahr +1 more
TL;DR: An algorithm for distributed acoustic navigation for Autonomous Underwater Vehicles that is computationally efficient, meets the strict bandwidth requirements of available AUV modems, and has potential to scale well to networks of large numbers of vehicles.
Proceedings ArticleDOI
Cooperative localization and control for multi-robot manipulation
TL;DR: A cooperative scheme for localizing the robots based on visual imagery that is more robust than decentralized localization and a set of control algorithms that allow the robots to maintain a prescribed formation are described.
Proceedings ArticleDOI
Recursive Bayesian search-and-tracking using coordinated uavs for lost targets
TL;DR: A unified sensor model and a unified objective function are proposed to enable search-and-tracking (SAT) within the recursive Bayesian filter framework to demonstrate the applicability of the technique to real search world scenarios.