A Range-Based Monte Carlo Patient Localization during Emergency Response to Crisis

Chandra-Sekaran, Ashok-Kumar; Stefánsson, Gunnar; Kunze, Christophe; Müller-Glaser, Klaus D.; Weisser, Pascal

doi:10.1109/aict.2009.10

Cited by 3 publications

(1 citation statement)

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“…There are a few indoor localization solutions proposed in literature, but none has been widely adopted. Chandra-Sekaran et al (2009a;2009b) proposed a system to locate doctors and patients carrying radio nodes in outdoor/indoor emergencies. Monte Carlo and unscented Kalman filter techniques were used for location estimation.…”

Section: Literature Reviewmentioning

confidence: 99%

An Environment-Aware Sequence-Based Localization Algorithm for Supporting Building Emergency Response Operations

Becerik-Gerber

Krishnamachari

et al. 2013

Computing in Civil Engineering

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Building emergencies are big threats to the safety of building occupants and first responders. When emergencies occur, unfamiliar environments are difficult and dangerous for first responders to search and rescue, sometimes leading to secondary casualties. One way to reduce such hazards is to provide first responders with timely access to accurate location information. Despite its importance, access to the location information at emergency scenes is far from being automated and efficient. This paper identifies a set of requirements for indoor localization during emergency response operations through a nationwide survey, and proposes an environmentaware sequence-based localization algorithm that is free of signal path loss models or collection of prior data, and mitigates signal multipath effects. The algorithm enables efficient on-scene ad-hoc sensor network deployment and optimizes sensing space division by strategically selecting sensor node locations. Building information is integrated, in order to enable building-specific space divisions and to support contextbased visualization of localization results. Proposed algorithm is evaluated through a building-size simulation. Room-level accuracy of up to 87.3% was reported, and up to 15.0% of deployment effort was reduced compared with using randomly selected sensor locations. The algorithm also showed good computational speed, with negligible time required for refreshing location estimation results in simulation. INTRODUCTIONBuilding emergencies, including flooding, building collapses, terrorist attacks and especially structure fires, are big threats to the safety of building occupants and first responders. For example, public fire departments across the U.S. attended 484,500 fires in buildings in 2011, which caused 2,460 deaths and 15,635 injuries (Karter 2012). When emergencies occur, unfamiliar environments are difficult and dangerous for first responders to search and rescue, sometimes leading to secondary casualties. With the increasing number of complex buildings, and less live-fire training, first responders are twice as likely to die inside structures as they were 20 years ago, and the leading cause of these line-of-duty deaths is getting lost, being trapped or disoriented (Brouwer 2007). One way to reduce such hazards is to provide firefighters with timely access to accurate location information. It is also of critical importance for an incident commander to know the locations of deployed first responders in real time, so that decision-making process is faster and more informed.

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Section: Literature Reviewmentioning

confidence: 99%