Infrared search and track (IRST) for long-range, wide-area detect and avoid (DAA) on small unmanned aircraft systems (sUAS)

This paper describes results from an ongoing research and development effort to evaluate the potential for airborne long-range infrared target detection (aka, infrared search and track (IRST)) to meet critical requirements for small unmanned aircraft system (sUAS) airborne detect and avoid (DAA). Established sensors used for manned-aircraft airborne DAA are generally heavy, expensive, active, high-power devices that are difficult to scale to the size-weight-and-power/cost (SWaP/C) constraints of sUAS. Current low-SWaP sensors developed for sUAS DAA are not meeting the Well Clear (Safe Separation) detection range and coverage requirements to avoid non-cooperative aircraft. In this work, a low-SWaP staring IRST airborne DAA sensor payload system is being developed and tested to evaluate system performance against long range small airborne threats (e.g., sUAS, birds) and to guide system design studies. This paper presents results and analyses from a recent initial data collection using low-SWaP LWIR microbolometers suitable for Group 1-2 sUAS DAA applications against Group 1-2 sUAS targets. The modeling and results to date suggest that low-SWaP IR sensors such as those evaluated in this paper can provide the necessary detection range to support Group 1-2 sUAS DAA operations. Wide area coverage requirements, emerging from NASA and FAA UAS DAA studies, can be met through multiple cameras with decreasing angular resolution as they rotate aft to minimize overall system SWaP. Because of the limited SWaP capacity of sUAS, an IRST DAA system without extensive mechanical stabilization is desired. Analysis of UAS non-stabilized IR sensor vibration data found that the effects of motion blur on range performance were not significant and could be largely mitigated through deconvolution filters. The UAS sensor vibration introduced excessive scene clutter that interfered with effective UAS target tracking. The clutter was due to a time-scale conflict between the platform vibration energy and the baseline detector algorithms. Algorithms are being developed and tested to mitigate these effects and extend the baseline approach. Additional data reduction and analysis are planned to corroborate and extend these findings.