Inflight path planning replacing pure collision avoidance, using ADS-B

Holdsworth, Robert; Lambert, Joseph V.; Harle, N.

doi:10.1109/62.904241

Cited by 20 publications

(8 citation statements)

References 1 publication

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“…Non-cooperative systems are microwave, millimeter-wave, lidar (laser radar), EOIIR, or optical sensor. Cooperative systems that mean a target aircraft has receiver are the TCAS [7] used currently broadly for manned aircraft or Automatic Dependant Surveillance-Broadcast (ADS-B) [8] under development for a future collision avoidance device for manned aircraft as well as UAV. The TCAS II, a cooperative sensor, will be investigated in this paper.…”

Section: Types Of Collision Avoidance Sensorsmentioning

confidence: 99%

Implementation of collision avoidance system using TCAS II to UAVs

Lee

2006

IEEE Aerosp. Electron. Syst. Mag.

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There will come the day that UA Vs can fly into the airspace of mann ed aircraft in the near future because of the increasing number of operational UAVs, together with technologies development. Since pilots of UA Vs are on the ground, the equipment for sensing and avoiding obstacles in front is indispensable. In this paper, the TCAS II, a collision avoidance device for manned aircraft, is implemented to UA V s and investigated for the interfaces of a flight control computer. It turns out that the onboard directional antenna of TCAS II does not provide precise direction information, and the TCAS II is not ass umed to be installed alone, but used as supplement with other device which tells precise direction.

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Section: Types Of Collision Avoidance Sensorsmentioning

confidence: 99%

Implementation of collision avoidance system using TCAS II to UAVs

Lee

2006

IEEE Aerosp. Electron. Syst. Mag.

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“…"Traffic Alert and Collision Avoidance System" (TCAS) 4 and "Automatic Dependent Surveillance-Broadcast" (ADS-B) 5 are the standard solutions for manned aircraft, and the later is considered a suitable one for unmanned systems. 6,7 Non-cooperative sensors sense the environment in order to get information about the obstacles and airplanes. Current technologies include radar, electro-optical, and infrared sensor systems.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional Guidance Filter for Autonomous Collision Avoidance

Galisteu

Almeida

2014

AIAA Guidance, Navigation, and Control Conference

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The operation of manned and unmanned aerial vehicles, over populated areas and inside non-segregated airspaces, imposes the need of coordinated flight among vehicles and with the air traffic control. In case of an eventual loss of communication with the groundbased traffic control, or lack of visual contact with another aircraft, the operator must autonomously provide safe separation and avoid the collision. While this procedure is normally employed by manned aircraft pilots, unmanned aerial vehicles currently are not able to see or detect and avoid another conflict traffic. Thus, this work presents a new method to provide three-dimensional guidance in order to perform, autonomously, collision avoidance and also to restore adequate safe separation, without any type of on-line optimization algorithms. The proposed method is analyzed through several possible encounter scenarios, showing adequate performance with little computational burden. Nomenclature χ Course, rad ∆χ Course error γ Flight path angle, rad κ Path curvature λ Longitude, rad µ Latitude, rad Ω Turn rate, rad/s ω n Natural frequency ζ Damping ratio D Horizontal distance, m D N Distance in the north direction, m D E Distance in the east direction, m D D Distance in the down direction, m D min Minimum horizontal distance D saf e Safe distance e L Lateral distance error H Vertical distance, m H min Minimum vertical distance h Altitude, m K LAT Lateral guidance gain matrix K LON Longitudinal guidance gain matrix R E Earth radius (6378137 m) R t Path turn radius T min Minimum safe TTE value V True airspeed, m/s V G Ground (horizontal) speed, m/s V Z

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“…Several collision avoidance mechanisms including Automatic Dependant Surveillance Broadcast (ADS-B) and Traffic Collision Avoidance System (TCAS) have been proposed to report the real-time GPS location of the UAVs [12]- [14]. Since these mechanisms rely upon open and un-encrypted transmission signals, they are invariably prone to spoofing and other message infringement forms of attacks [15].…”

Section: Introductionmentioning

confidence: 99%

Cognitive Chaotic UWB-MIMO Detect-Avoid Radar for Autonomous UAV Navigation

Nijsure

Kaddoum

Mallat

et al. 2016

IEEE Trans. Intell. Transport. Syst.

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Abstract-A cognitive detect and avoid radar system based on chaotic UWB-MIMO waveform design to enable autonomous UAV navigation is presented. A Dirichlet-Process-Mixture-Model (DPMM) based Bayesian clustering approach to discriminate extended targets and a Change-Point (CP) detection algorithm are applied for the autonomous tracking and identification of potential collision threats. A DPMM based clustering mechanism does not rely upon any a priori target scene assumptions and facilitates online multivariate data clustering/classification for an arbitrary number of targets. Furthermore, this radar system utilizes a cognitive mechanism to select efficient c haotic waveforms to facilitate enhanced target detection and discrimination. We formulate the CP mechanism for the online tracking of target trajectories which present a collision threat to the UAV navigation and thus we supplement the conventional Kalman filter based tracking. Simulation results demonstrate a significant performance improvement for the DPMM-CP assisted detection as compared with direct generalized likelihood ratio based detection. Specifically, w e o bserve a 4 d B p erformance g ain i n target detection over conventional fixed U WB w aveforms a nd superior collision avoidance capability offered by the joint DPMM-CP mechanism.

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Inflight path planning replacing pure collision avoidance, using ADS-B

Cited by 20 publications

References 1 publication

Implementation of collision avoidance system using TCAS II to UAVs

Implementation of collision avoidance system using TCAS II to UAVs

Three-dimensional Guidance Filter for Autonomous Collision Avoidance

Cognitive Chaotic UWB-MIMO Detect-Avoid Radar for Autonomous UAV Navigation

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