Sense and avoid radar using Data Fusion with other sensors

Cornic, Pascal; Garrec, Patrick; Kemkemian, Stephane; Ratton, Laurent

doi:10.1109/aero.2011.5747514

Cited by 14 publications

(6 citation statements)

References 4 publications

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“…optical sensors) and are harder to correlate with other data. 62 Generally, data coming from sensors will have a certain amount of noise leading to uncertainty about the object's location. A common method of dealing with this noise in tracking, localization, and navigation problems is to employ the Kalman filter or one of its nonlinear extensions such as the extended Kalman filter or particle filter.…”

Section: Target Tracking: Sensor and Data Fusionmentioning

confidence: 99%

Sense and avoid for small unmanned aircraft systems: Research on methods and best practices

Skowron

Chmielowiec

Glowacka

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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In this study, we discuss recent advances and state-of-the-art sense-and-avoid solutions for small unmanned aircraft systems. The purpose of this paper is to equip the reader with the most up-to-date information on research and commercial efforts aimed at introducing autonomous small unmanned aircraft systems into the shared airspace. We present a broad overview of the regulatory environment, current abilities, and limitations of various sensor types as well as algorithmic and computational aspects of establishing a reliable sense-and-avoid platform. This paper reviews a substantial portion of the literature, focusing on developing reliable sense-and-avoid systems. It brings together information from various sources, including commercial drone and sensor manufacturers, regulatory agencies, as well as basic research performed in this regard. While academic and industrial research activities aimed at developing an effective sense-and-avoid system for small unmanned aircraft systems are intensifying, the current technology does not yet allow for a comprehensive solution that would enable autonomous flight. However, recent advances in hardware miniaturization, increases in computational power, and new algorithmic solutions allow us to remain optimistic about future developments.

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Section: Target Tracking: Sensor and Data Fusionmentioning

confidence: 99%

Sense and avoid for small unmanned aircraft systems: Research on methods and best practices

Skowron

Chmielowiec

Glowacka

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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Section: Introductionmentioning

confidence: 99%

“…With a potentially multibillion dollar market opening up over the next decade, radars will be exploited in commercial use for remote sensing, altimetry, bridge integrity monitoring, land surveying, collision avoidance, and numerous other applications. UAS will not be approved by the FAA for generalized commercial applications unless they can prove collision avoidance capabilities [1], [2].SAA systems are classified into cooperative and noncooperative categories. For cooperative systems, Automatic Dependent Surveillance-Broadcast (ADS-B) systems are currently a predominant choice [3], [4].…”

mentioning

confidence: 99%

Compact FMCW radar for a UAS Sense and Avoid system

Mackie

Spencer

Warnick

2014

2014 IEEE Antennas and Propagation Society International Symposium (APSURSI)

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U.S. Federal Avionics Administration (FAA) regulations limit the use of unmanned air systems (UAS) in commercial and private industry because of the potential danger that they pose to other aircraft. The development of an onboard Sense and Avoid (SAA) system is necessary for UAS to share the same airspace as other aircraft. This paper presents the design and fabrication of a radar sensor designed for use as part of a reactive path planning system. The radar is a Frequency Modulated Continuous Wave (FMCW) type with two receive channels. This enables monopulse processing to provide both range and bearing information of detected targets. Without connectors or antennas, it weighs 46 g, is 7.6 x 5 x 3.8 cm in size, and costs less than $100 when built in quantities of 100 or more. The radar has been tested using outdoor targets and is currently being integrated with a UAS autopilot and collision avoidance algorithm for airborne testing. I. INTRODUCTIONUnmanned air systems (UAS) are currently transitioning from military to civil applications. With a potentially multibillion dollar market opening up over the next decade, radars will be exploited in commercial use for remote sensing, altimetry, bridge integrity monitoring, land surveying, collision avoidance, and numerous other applications. UAS will not be approved by the FAA for generalized commercial applications unless they can prove collision avoidance capabilities [1], [2].SAA systems are classified into cooperative and noncooperative categories. For cooperative systems, Automatic Dependent Surveillance-Broadcast (ADS-B) systems are currently a predominant choice [3], [4]. For intruder aircraft and other non-cooperative objects, radar or other optical detection systems must be used. UAS are limited in the amount of extra payload that they can carry and attributes such as size and weight are critical in the design of the radar. There is a good deal of work in the literature on compact radars, particularly for automotive collision avoidance [5].For UAS sense and avoid, design requirements are different than for other common radar applications. Size and weight are even more critical than for automotive applications, and performance of the reactive path planning algorithm is enhanced if the radar can provide bearing information. Based on these considerations, we present a design for a radar with mechanical and RF performance targeted to UAS sense and avoid applications.II. DESIGN The overall SAA system will include a radar sensor, a data processing module, and an autopilot as shown in Figure 1.

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“…On-board trajectory re-planning with dynamically updated constraints based on the intruder and the host dynamics is at present used to generate obstacle avoidance trajectories [10]. A coarseresolution radar based SAA solution is developed for small size UA [11] and its information is fused with data from Automatic Dependent Surveillance -Broadcast (ADS-B) system [12]. As part of this research, the possible synergies attainable with the adoption of different detection, tracking and trajectory generation algorithms are studied and as a fundamental objective, the errors propagation from different sources and the impacts of host and intruders dynamics on the ultimate SAA solution are also addressed.…”

Section: Introductionmentioning

confidence: 99%

Avionics sensor fusion for small size unmanned aircraft Sense-and-Avoid

Ramasamy

Sabatini

Gardi

2014

2014 IEEE Metrology for Aerospace (MetroAeroSpace)

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Cooperative and non-cooperative Sense-and-Avoid (SAA) systems are key enablers for Unmanned Aircraft (UA) to routinely access non-segregated airspace. In this paper the stateof-the-art cooperative and non-cooperative SAA technologies for small size UA are presented and the associated multisensor data fusion techniques are discussed. The non-cooperative sensors including both passive and active Forward Looking Sensors (FLS) and the cooperative systems including Traffic Collision Avoidance System (TCAS), Automatic Dependent Surveillance - Broadcast (ADS-B) system and/or Mode C transponders form part of the SAA holistic architecture. After introducing the SAA system processes, the key mathematical models are presented. The Interacting Multiple Model (IMM) algorithm is used to estimate the state vector of the intruders and this is propagated to predict the future trajectories using a probabilistic model. Adopting these mathematical models, conflict detection and resolution strategies for both cooperative and un-cooperative intruders are identified. Additionally, a detailed error analysis is performed to determine the overall uncertainty volume in the airspace surrounding the intruder tracks. This is accomplished by considering both the navigation and the tracking errors affecting the measurements and translating them to unified range and bearing uncertainty descriptors, which apply both to cooperative and non-cooperative scenarios. Detailed simulation case studies are carried out to evaluate the performance of the proposed SAA approach on a representative host platform (AEROSONDE UA) and various intruder platforms, including large transport aircraft and other UA. Results show that the required safe separation distance is always maintained when the SAA process is performed from ranges in excess of 500 metres.

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Sense and avoid radar using Data Fusion with other sensors

Cited by 14 publications

References 4 publications

Sense and avoid for small unmanned aircraft systems: Research on methods and best practices

Sense and avoid for small unmanned aircraft systems: Research on methods and best practices

Compact FMCW radar for a UAS Sense and Avoid system

Avionics sensor fusion for small size unmanned aircraft Sense-and-Avoid

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