Saturday, February 20, 2016

UAS Assigment


UAS Mission

Every year billions of dollars in property damages and loss of many lives are the consequences of enormous wildfires into the United States territory. The Government and private sectors agencies have the responsibility to reduce and respond quickly and effectively to this threat. The mission is to demonstrate the potential capabilities that Unmanned aerial systems UAS have in the US Forest Service, with the objective to provide fire intelligence to management teams. The first platform system outline here is the NASA Ikhana, it is a modified Predator-B (MQ-9) UAV with the altitude of 45000 feet. This autonomous, onboard processing transfer information to the ground personal dealing with the wildfires (V. G. Ambrosia, 2010). The information from the satellite is not completed to monitor individual events that are why NASA needs to get the support of airborne vehicles for thermal sensor data observations. This has impacted greatly the recollection of information, performance, fuel burned. and additional by automating onboard sending information to the ground personal (V. G. Ambrosia, 2010).
The second UAS platform considered to be used in the wildfire surveillance system is the Helicopter base UAS. (Enric Pastor, 2011). This UAS system is good to operate in difficult terrains with the future to provide very important data information to the air and ground squads. The report stated that “The UAV is the Sky Eye system; it is a helicopter base UAS platform that facilitates the development of wildfire remote sensing applications providing tactical support to wildfire monitoring” (Enric Pastor, 2011).  The third UAS platform considered in gathering information to deal with wildfires is  “the ALTAIR high-altitude, long Endurance (HALE) UAS,  which has the potential to increase the image resolution and update rates over satellites base systems” (V. G. Ambrosia, 2010).  Possible applications with some low altitude short endurance (LASE) could  be incorporated into this mission, but, there are some important issues as latency and limited communication ranges that have to be resolved before they could be used in the wildfires business (David W. Caster, 2011). In general, all these systems have showed Tobe very efficient in their mission for the wildfire disaster control. Actually, there are limited regulations for these systems. For the UAS integration into NAS, issues as air safety, airspace operation, mission, and system safety and system performance are the most important considerations for this UAS integration into NAS (Corcoran, 2014). The UAS limited regulations are restricting their full operation. There is a need education at all levels in the regulatory agencies to provide rapidly attention to the UAS growing industry.
References

 

Corcoran, M. (2014). Newsgathering applications of Unmanned aerial Vehicles (UAVs) in covering conflict, civil unrest and disaster. Drone Journalism:.
David W. Caster, D. B. (2011). Cooperative Forest Fire Surveillance using a team of small unmanned air Vehicles.
Enric Pastor, C. B. (2011). Architecture for a Helicopter-Base Unmanned aerial systems wildfire surveillance system.
V. G. Ambrosia, S. T. (2010). The Ikhana Unmanned airborne system (UAS) western states fire imaging missions: from concept to reality (2006-2010).

     

     

1 comment:

  1. Great post,
    Fire prevention is a great way to exploit the capabilities of UAS. An IEEE article 'Wildfire Monitoring Using a Mixed Air-Ground Mobile Network' the authors envision a system of UAS and balloons to form a network in order to detect fires in known hot spots to allocated resources before the fire becomes out of control. They also envision the system to be able to monitor large fires and relay important information to commanders on the ground to make tactical decisions on how to best fight the fire and keep fire fighters safe.

    Reference

    Barrado, C., Messeguer, R., Lopez, J., Pastor, E., Santamaria, E., & Royo, P. (2010). Wildfire monitoring using a mixed air-ground mobile network. IEEE Pervasive Computing, 9(4), 24-32. doi:10.1109/MPRV.2010.54

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