Sense and avoid sensor selection

Unmanned aerial systems (UAS) integration into National Aerospace System (NAS) dictate establishing regulatory standards and equipment requirements to ensure UAS operational safety. Sense and avoid technology is one of the important issues pertaining to safety of UAS. Pilots of manned aircraft are responsible to see and avoid other traffic by relying on visual detection, air traffic control radar separation, and other available sensors installed on the aircraft. Unmanned aircraft pilots often have to rely on the UAS camera sensor picture with limited field of view, which is not sufficient for the sense-and avoid requirements for operation in the NAS. That’s why proper selection, testing, and certification of sense-and -avoid technology for UAS is important.

Electro optical/ infrared (EO/IR) sensor is a sense-avoid technology selected for this paper. EO/IR is a non-cooperative sensor, which means it does not rely on the technology carried by the intruder aircraft. EO/IR is a passive sensor. Due to this sensor’s size, weight and power (SWAP) considerations, it is suitable for use in the smaller size UAS (less than 55 pounds). It is capable to operate during day and night in all weather conditions. However, current EO/IR technology for SAA may have an increased rate of false alarms generated due to clutter in images and weather conditions such as fog or clouds.

EO and IR technologies are combined in a single compact sensor create a complete SAA system. The EO sensor takes images in the visible light spectrum with a charged coupled device camera. The infrared sensor works within infrared spectrum, creating images based on temperature differentiation.

EO/IR sensors have good performance in terms of detection of azimuth, elevation and traffic coverage. However, the drawbacks of this technology is that it is restricted range and has a limited field of view (FOV). It is important to mention, that a tradeoff exists between the FOV and detection range. For example, if the EO/IR sensor has a large FOV versus a small FOV to passively scan, the distance at which it can detect an object will decrease (Pearson, Moore, Ogdoc, & Choi, n.d). The particular EO/IR sensor which is suitable for smaller size UAS was developed by HoodTech Vision. Alticam AC-10 EO/IR sensor has small size and weighs only 5,700 grams and measures 25.4 cm in diameter. It is designed for both day and night and all weather operations. The sensor also features a laser pointer, a laser range finder, and a pan-over tilt. Gimbal that tilts 45 degrees up and 90 degrees down with 360 digress endless pan capability. Since power consumption is an important parameter to consider for small UAS, the AC- 10 was designed to use half the power of similar systems, freeing power for additional sensors and saving fuel for increased mission range. Power supply range is 24-32 VDC with 31 W continuous and 55 W peak consumption (HoodTech, n.d.).

It also has an increased FOV for better traffic detection: the IR sensor has a horizontal FOV of 1.7°- 22° and EO imager features 1.1°- 31.5° FOV. Since this particular sensor is enhanced with laser pointer and range finder, its application for as a sense-and-avoid sensor is greatly improved. The laser rangefinder operates in the 30 to 3000 meter range and it is eye-safe. The slew rate of the gimbal is 60° per second.

Figure 1. Alticam AC-10 EO/IR sensor. Adapted from “Alticam AC-10 specifications,” by HoodTech. (n.d.). Copyright by HoodTech.

 The concept of operations of a laser enhanced EO/IR system for sense-and avoid is as follows:

1. The EO/IR sensor detects potential intruders.

2. The laser subsystem confirms the azimuth and elevation angles of potential traffic and estimates range of the targets.

4. The gimbal with laser sensor slews to the target bearing angle detected by the EO/IR system.

5. After this information has been analyzed and, in case the intruder traffic presence is confirmed, the bearing angles from the EO/IR and the range from the laser ranger are fused to estimate the position and velocity of the intruder. In scenarios involving multiple intruders, the gimbals/scanners may be employed to slew the laser from one intruder to another (Ganguli, Avadhanam, Yadegar, Utt, & McCalmont, 2011).

Additional specifications details of AC-10 sensor are presented in Table 1.

Sensor	Wavelength	Horizontal FOV	Pixels	Video output	Zoom
IR imager	3-5 μm	1.7°- 22°	640 x 480	NTSC	Optical 13X; digital 2X
EO imager	0.4-0.9 μm	1.1°- 31.5°	1280 x 720	NTSC	Optical 30X, Digital 0.5-2X

Table 1. EO/IR sensor specifications. Adapted from Adapted from “Alticam AC-10 specifications,” by HoodTech. (n.d.). Copyright by HoodTech.

 The EO/OR sensor with laser range founder is a suitable solution for smaller UAS sense-and-avoid requirements. It is possible that in the future most UAS will require installation of cooperative technologies for traffic sense-and avoid. Such systems as transponder based Traffic Alert and Collison Avoidance system (TCAS) or GPS-based Automatic Dependent Surveillance Broadcast (ADS-B) or other similar technology will greatly enhance the sense-and-avoid capabilities for UAS. With the rapid technological advancements and miniaturization of sensor technology, weight, size, and power requirements of many sensors are decreasing while there technological capabilities are increasing. Installation of passive non-cooperative sensor such as the one presented in this paper in combination with cooperative active sensor such as TCAS will greatly enhance UAS sense and avoid capabilities.

References

Ganguli, A., Avadhanam, S., Yadegar, J., Utt, J., & McCalmont, J. (2011). Multiple intruder tracking using a laser enhanced EO/IR sense and avoid system. Retrieved from American Institute of Aeronautics and Astronautics: http://enu.kz/repository/2011/AIAA-2011-1478.pdf

HoodTech. (n.d.). Alticam AC-10 specifications. Retrieved from http://www.hoodtechvision.com/products/detail/alticam-ac-10

Pearson, J., Moore, Z., Ogdoc, J., & Choi, F. (n.d). Design of sense-and-avoid standards for RQ-7B Shadow under loss-link. Retrieved from http://seor.gmu.edu/projects/Capstone_2013/UAS_IEEE_Final_24Apr13.pdf