Unmanned Aerial Systems in National Airspace

     Unmanned aircraft are a vital asset in today’s world. They have made aerial photography and videography cheaper and more accessible to both hobbyists and small business owners.
Today, the major hurtle is UAS integration into national airspace (NAS). One of the major aspects to integration of UAS into NAS is the ability for systems to sense and avoid other aircraft or obstacles. In the current FAA regulations, the term “see and avoid” exist, but in the future “sense and avoid” will be applied (Carey, 2013). Until that point UASs are unable to comply with the requirements, but this has not stopped companies and the military from working towards smart solutions to the providing a sense and avoid system that are dependable enough to garner FAA approval and support.

     The techniques for monitoring separation from both manned and unmanned systems come in multiple forms. The overarching concept, regardless of technique, is that the UAS is informed of nearby traffic and can execute a predictable solution that will provide regulatory separation. The two major techniques to gathering this traffic information can be categorized as ground based or airborne based sensing. Ground based sensing utilize radar systems similar to air traffic control agencies. The major difference is that these radar systems integrate directly into the ground control station (GCS) of the UAS (SRC Inc. 2016). The airborne technique relies on advanced sensors being equipped directly on the air vehicle portion of the UAS. A lot of research is going into the development of micro radar systems that would be able to fit a highly capable radar system into a very small package (Gorwara, 2014).
 
     Some of the major considerations that need to be factored in when deciding between ground based sensing or airborne sensing needs to be attributed to both the size and type of UAS airframe in questions. Small UASs need to be very cognizant of size power and weight of any additional sensors that need to be added to the air vehicle. These systems may benefit from a ground based system that is able to communicate with the ground control station. Additionally, micro radar systems are being produced to provide small quadcopter sized UASs with a robust ability to sense and avoid traffic with light weight and low power solutions (Gorwara, 2014).  Large UASs like military grade UASs have a large payload capacity and a large power source capable of both carrying and powering complex sensors that can provide adequate sense and avoid capabilities. Another aspect to consider is the type of airframe in question. A small quadcopter may move slowly and within a relatively small range. This means less powerful sensors could be used to provide the separation and spacing required. Large fast fixed wing system could fly at high altitudes and at high speeds with an enormous range, so providing powerful onboard solutions may make the most sense.

     Some larger systems like the MQ-4 global hawk actually have terminal collision and avoidance system (TCAS) which is used on most large commercial manned aircraft. There is also research into a new system call Airborne Collision Avoidance System for Unmanned Aircraft or ACAS Xu for short. This system will integrate with TCAS as well as provide autonomous functions that will support proper sense and avoid decision making if the UAS has lost link or is in autonomous flight (NASA, 2015).
 
     Another current initiative is the use of a system called the ground based sense and avoid system (GBSAA) by SRC Inc. This system is currently being installed by the US Army at posts that are hubs for large UAS training. Fort Hood and Fort Campbell are both test beds for this technology (Mishory, 2016). The system utilizes powerful and expensive ground based radar dishes to directly communicate any traffic advisories directly to the GCS of the UASs operating within its area of responsibility. This system can detect both manned and unmanned aircraft as well as other airborne obstacles. The benefits of this system are that just one GBSAA can provide coverage for multiple aircraft working in a defined area. Also, GBSAA does not add any additional power or weight requirements to the actual air vehicles that are utilizing its information (SRC Inc., 2016).

     Regardless to size and type, the need for FAA approved sense and avoid systems is vital to the integration of UAS into NAS. By understanding the limitations and capabilities associated with the size and type of a UAS will help engineers provide the best solution to each system on a case by case basis. The need to ensure the right capability is equipped on the right system is also vital in reducing excess costs and ensuring the general UAS user base is capable and willing to equip their UASs with these systems when it becomes available.  Additionally, integrating the UAS sense and avoid technology into manned sense and avoid systems like TCAS will be vital to future integration.      

 References
Carey, B. (2013, June 22). FAA Plans Unmanned 'Sense and Avoid' Rule in 2016. Retrieved
October 03, 2016, from http://www.ainonline.com/aviation-news/air-transport/2013-07-22/faa-plans-unmanned-sense-and-avoid-rule-2016

Gorwara, A. (2014). Doppler micro sense and avoid radar. Retrieved October 3, 2016, from http://pmi-rf.com/documents/DopplerMicroSenseandAvoidRadarPaper.pdf

Mishory, J. (2016, June 16). Initial UAS flights using GBSAA system at Ft. Hood have been delayed. Retrieved October 03, 2016, from https://insidedefense.com/daily-news/initial-uas-flights-using-gbsaa-system-ft-hood-have-been-delayed

NASA. (2015, January 25). NASA, FAA, Industry Conduct Initial Sense-and-Avoid Test. Retrieved October 03, 2016, from http://www.nasa.gov/centers/armstrong/Features/acas_xu_paves_the_way.html

SRC Inc. (2016). Ground-Based Sense and Avoid Radar System. Retrieved October 03, 2016, from http://www.srcinc.com/what-we-do/radar-and-sensors/gbsaa-radar-system.html

Data Format, Protocols, and Storage Methods of Mars Curiosity Rover

Data transfer between unmanned systems and ground control stations is a challenge for any unmanned system. Data transfer between an unmanned system and a ground control station which are planets apart becomes even more of challenge given the limitations of our current technology. Understanding the methods behind the Mars Curiosity Rover communications system will help us gain understanding about data management, treatment, and movement in any unmanned system. Additionally, being able to understand the onboard sensor and how they interact with the data transfer architecture in terms of power requirements, data storage requirements, and data treatment methods, will help us analyze the current state of technology. Upon a detailed study of the current situation, proposed future changes could streamline the entire data transfer process from the sensor to the final product.  

Data Format, Protocol, and Storage

The Curiosity Rover has a network of satellites that allows the rover on the surface of Mars to communicate with the ground control station (GCS) on earth at high data rates. Unlike smaller unmanned systems, direct communication between the ground station and the vehicle are impracticable due to the power requirements and bandwidth limitations that exits when transmitting data between Earth and Mars. The rover and the GCS can be as far as 400,000,000 km apart, and to transmit data over that distance while using a relatively feasible mount of power, the bandwidth peaks at 800 b/ s using the onboard high gain antenna, which utilizes X band frequencies (Gordon, 2012). This type of communication can transfer telemetry and navigational commands to the rover, but sending raw imagery or data from one of its many sensors would be extremely impractical. For large data the Curiosity Rover utilizes the Mars Reconnaissance Orbiter (MRO) to relay its data back to earth. The MRO orbits only 275km over the surface of Mars, which exponentially decreases the power requirement to transmit large data files off the surface of Mars (Taylor, 2006). The orbiter is designed not only to relay and amplify signals back to Earth, but it is an instrument of science loaded with its own sensors that are used in conjunction with the rover. The final piece of the communications architecture is the GCS. There are three large ground stations, one in Goldstone, California, one in Madrid, Spain, and one in Canberra, Australia. These GCSs are on a much larger scale than any terrestrial based unmanned system GCS. They come standard with one 70m parabolic antenna and at least two 34m parabolic antennas. By creating huge power hungry antennas on the earth, the size and power required by the orbiter and rover to transmit and receive data can be decreased (Gordon, 2012).    

Onboard Sensors

In terms of sensors onboard Curiosity, there are many high tech sensors that have a range of purposes (NASA, 2015). In an effort to study data transfer techniques; analyzing the sensors with the highest data transfer requirement will assist in understanding how to streamline the process in the future. Some of the highest data producing sensors on the rover are the mast cameras. There are two 2-megapixl cameras that not only capture imagery, but can be used as a stereo pair in order to conduct 3D mapping of the rovers immediate surrounds. Anyone with a background in photography would think 2MP cameras are not the best choice for a 2.5 billion dollar space probe, but this is the first step of the data treatment method utilized by the engineers on the rover project. By utilizing a lower megapixel camera, the data transfer requirement is decreased before utilizing compression, or other forms of data treatment. The mast cameras utilizes a 1600 x 1200 pixel resolution interline camera sensor (Cangeloso, 2012). This camera is capable of storing up to 5500 full size images on the actual cameras memory itself, which acts as a buffer. This capture resolution roughly translates to about a 1.4mb image file in its raw form (Gordon, 2012). This is where the use of the MRO and UFH radio communication come in. The UHF radio on the rover can pump out much higher bandwidth, which is up to 256 kb/s. This is done with a 12 watts transceiver. If the rover was required to send via its high gain antenna straight back to Earth via X-band communications, data transfer could only top out around 800 b/s and required 15 watts of power. This would mean the same images would take much longer to transmit and require the 15watt transceiver to be active and drawing energy for a much longer period of time (Taylor, 2006). The power and time constraints placed on the entire system when the MRO is not used would severely limit the mission success of the rover.      

  

Alternative Data Treatment Strategy

Since 2004, which is when Curiosity and the MRO were designed many things have changed in terms of image compression and data storage. Starting with the sensor, I would recommend the use of a higher resolution camera pair, but include the use of lossy image compression. This would have two aspects, it would increase the native resolution and quality of the imagery coming from the mast camera set, but the lossy compression would reduce the data back into a manageable data set that would be easily relayed back to earth (Chin, 2013). The second methodology in terms of data transfer would be increasing the onboard storage capacity from about 8 GB to 100 GB per sensor. Along with the increased storage, I would implement a server based tagging method to the imagery that would add metadata and allow scientist to query the server and then pull full resolution imagery as needed. Not having to send back all the imagery at full resolution would save bandwidth to allow simultaneous transfer of the “current view” low resolution imagery as well a full resolution stills that are being queried by scientists for further investigation. 

Both data storage and processing power are increasing exponentially every day. The physics of data transfer and the power required to accomplish it are not as rapidly advancing as fast, and this is why alternatives and advances in data transfer should focus on the digit aspects rather than the electromagnetic aspects. Long distance laser data transfer may be a long term solution to the current slower moving portions of the electromagnetic spectrum that we utilize, but with todays rapid increase in processing power, the near term solution may be in compression and processing advances in data transfer.          

References

Gordon, S. (2012, January 1). Talking to Martians: Communications with Mars Curiosity Rover. Retrieved February 8, 2015, from https://sandilands.info/sgordon/communications-with-mars-curiosity 

Taylor, J. (2006). Mars Reconnaissance Orbiter Telecommunications. DESCANSO Design and Performance Summary Series, (Article 12). Retrieved February 8, 2015, from http://descanso.jpl.nasa.gov/DPSummary/MRO_092106.pdf 

Makovsky, A. (2009). Mars Science Laboratory Telecommunications System Design. DESCANSO Design and Performance Summary Series, (Article 14). Retrieved February 8, 2015, from http://descanso.jpl.nasa.gov/DPSummary/Descanso14_MSL_Telecom.pdf 

NASA. (n.d.). Mars Science Laboratory; Curiosity Rover. Retrieved February 8, 2015, from http://mars.nasa.gov/msl/

Cangeloso, S. (2012, August 9). Why does the $2.5 billion Curiosity use a 2-megapixel camera? | ExtremeTech. Retrieved February 8, 2015, from http://www.extremetech.com/extreme/134239-why-does-the-2-5-billion-curiosity-use-a-2-megapixel-camera 

Chin, M. (2013, December 18). New data compression method reduces big-data bottleneck; outperforms, enhances JPEG. Retrieved February 8, 2015, from http://newsroom.ucla.edu/releases/ucla-research-team-invents-new-249693

Bluefin-21’s Sensors and Advances in Maritime Sensors

By Brett Chereskin

Abstract

This paper provides an explanation of the sensors and capabilities of the Bluefin-21 autonomous underwater vehicle (AUV). There are four topics address within this paper. The first is an analysis of marine sensors that support underwater search and rescue. The second topic proposes modification to the AUV that would increase search and rescue capability. The third topic proposes possible sea to air unmanned system coordination, and the final topic weighs the pros and cons between manned and unmanned systems in maritime sensor technology.  

Introduction

The Bluefin-21 is an autonomous underwater vehicle (AUV) that is build by Bluefin Robotix. This advanced maritime AUV has recently been used in the search for Malaysia Airline flight MH370 and contains many sensors that are perfectly suited to the underwater search and rescue mission (Chand, 2014). Understanding the available sensors used on the Bluefin-21 will help determine if any modification can be made that will increase it’s capabilities as well as if integrating with unmanned aerial systems (UASs) could provide an increase to its mission success. Additionally, by analyzing the advantages of sensors suited to unmanned systems a clear understanding of this topic will be gained.       

The Sensors of Bluefin-21

The Bluefin-21 can be fitted with multiple sensors depending on the mission requirement. One of the most commonly used sensors for maritime search and rescue on the Bluefin-21 is the EdgeTech 2200-M 120/410 kHz side scan sonar (Chand, 2014). The EdgeTech 2200-M gathers side scan and/or sub-bottom data using EdgeTech’s proprietary technology in water depths up to 6000 meters (EdgeTech, 2015).  This proprioceptive sensor is specifically deigned for use in the maritime environment due to the fact that only in water will acoustic/ FM sonar sensors provide the best wave propagation and resolution.  To note, one of the greatest advantages of this particular sonar imagining sensor is that is uses full spectrum signal processing that sends out a broad band transmitting pulse. One benefit of a full spectrum system is the relative power savings over a conventional continues wave sonar system. In order to obtain the same resolution, conventional sonar would required 100 times more power compared to the full spectrum sonar system (EdgeTech, 2015).

Possible Modifications

Upon analysis of the EdgeTech 2200-m on the Bluefin-21, other than attempting to decrease weight and power consumption, which are key to any unmanned system, I propose that the ability to network multiple sensors via underwater acoustic networks should be integrated. Due to the temporal constraints of any search and rescue mission, being able to cover larger amounts of area in less time is essential. The Bluefin-21 uses an INS to accurately track its positions, but allowing the position data to georeference the EdgTech’s imagery would be the first step in the multi-sensor integration (Bluefin-21, 2015). After georeferenceing, multiple sensors would need to communicate the data in order to run algorithms that would optimize participating AUV tracks so the multi-sensor system could map the largest possible area the least amount of time.

Aerial Integration

     In order to network multiple sensors via underwater acoustic networks, consideration must be made to the limitations of acoustic communication networks.  Underwater acoustic communications are generally recognized as one of the most difficult communication media in use today (Stojanovic, 2009). Due to this limitation, an alternative could be the introduction of an aerial command and control node, specifically an unmanned aerial system capable of extended transit and loiter times as well as beyond line of site communication of high bandwidth datasets. The multiple Bluefin-21’s could surface at regularly scheduled intervals and upload key data to the command and control UAS. The UAS would not only send the AUVs datasets to the mission command center at real near time, but it could also provide mission parameter changes to all other AUVs participating in a particular mission set. 

Unmanned vs Manned

            In terms of maritime sensors, there are a few key reasons why the use of AUVs over manned systems is beneficial. One major reason has to do with sensor depth. In terms of maritime sensor operation, it is known that attenuation of sonar pulses and noise are a limiting factor in obtaining high-resolution products (Stojanovic, 2009). If sensors were limited to shallow depths due being mounted on manned systems, the resolution during deep-water search and rescue would be limited. If the system is mounted on an AUV like the Bluefin-21, which is capable of diving to 4500 meters, it would be able to retrieve higher resolution products, and possible dive deep enough to obtain true camera imagery of the bottom of the ocean (Bluefin, 2014).

Conclusion

            The integration of unmanned systems into maritime search and rescue has taken previously existing sensor technology to the next level. The ability to take these sensors deeper and coordinate and optimize multi-sensor operations will save lives in the near future. By further integrating the unmanned aerial layer into the maritime layer, it will speed up data transfer and facilitate ad hoc mission changes.

   

References

Chand , N. (2014). Unmanned/Autonomous Underwater Vehicles. SP’s Naval Forces, Jun 2014 Issue. Retrieved from http://www.spsnavalforces.com/story.asp?mid=37&id=6

EdgeTech Corp. (2015). 2200-M Modular Sonar System. Retrieved from 

            http://www.str-subsea.com/file_download/22/EdgeTech+2200+Modular+Sonar+System.pdf

BlueFin Robotix Corp. (2015). Bluefin-21 Summary. Retrieved from http://www.bluefinrobotics.com/products/bluefin-21/

Stojanovic, M., & Preisig, J. (2009). Underwater Acoustic Communication Channels Propagation Models and Statistical Characterization. IEEE Communications Magazine Retrieved from http://web.mit.edu/millitsa/www/resources/pdfs/chmj-print.pdf