Acoustic-Seismic Detection of Ballistic-Missile Launches for Cooperative Early Warning of Nuclear Attack

Altmann, Jürgen

doi:10.1080/08929880500357716

Cited by 4 publications

(1 citation statement)

References 38 publications

(17 reference statements)

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“…Therefore, even if it were feasible to install radar systems in prominent urban areas, the RADAR measurement analysis methods would be unable to effectively distinguish drones from false positives. Alternative airspace surveillance mechanisms such as acoustic seismic detection could be easily foiled with the use of near-silent electric motor propulsion sources 6 . Current optical plane detection systems for use on UAVs are comparable to human observation when used to detect human piloted aircraft, though ground operation of these systems would be greatly limited by the emplacement and operational overhead requirements to mount them to fixed or mobile objects 7 .…”

Section: Introductionmentioning

confidence: 99%

A collaborative smartphone sensing platform for detecting and tracking hostile drones

Boddhu¹,

McCartney²,

Ceccopieri³

et al. 2013

SPIE Proceedings

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In recent years, not only United States Armed Services but other Law-enforcement agencies have shown increasing interest in employing drones for various surveillance and reconnaissance purposes. Further, recent advancements in autonomous drone control and navigation technology have tremendously increased the geographic extent of dronebased missions beyond the conventional line-of-sight coverage. Without any sophisticated requirement on data links to control them remotely (human-in-loop), drones are proving to be a reliable and effective means of securing personnel and soldiers operating in hostile environments. However, this autonomous breed of drones can potentially prove to be a significant threat when acquired by antisocial groups who wish to target property and life in urban settlements. To further escalate the issue, the standard detection techniques like RADARs, RF data link signature scanners, etc..., prove futile as the drones are smaller in size to evade successful detection by a RADAR based system in urban environment and being autonomous, have the capability of operating without a traceable active data link (RF). Hence, towards investigating possible practical solutions for the issue, the research team at AFRL's Tec^Edge Labs under SATE and YATE programs has developed a highly scalable, geographically distributable and easily deployable smartphone-based collaborative platform that can aid in detecting and tracking unidentified hostile drones.In its current state, this collaborative platform built on the paradigm of "Human-as-Sensors", consists primarily of an intelligent Smartphone application that leverages appropriate sensors on the device to capture a drone's attributes (flight direction, orientation, shape, color, etc..,) with real-time collaboration capabilities through a highly composable sensor cloud and an intelligent processing module (based on a Probabilistic model) that can estimate and predict the possible flight path of a hostile drone based on multiple (geographically distributed) observation data points. This developed collaborative sensing platform has been field tested and proven to be effective in providing real-time alerting mechanism for the personnel in the field to avert or subdue the potential damages caused by the detected hostile drones.

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Section: Introductionmentioning

confidence: 99%