Advanced Algorithms for Radar Classification of Airborne Targets

The proliferation of Unmanned Aircraft Systems (UAS) provides many benefits and opportunities, but also introduces a number of new challenges and threats. These UAS are widely commercially available and can be operated by individuals with little training. These UAS present a threat in both commercial and military environments. For example, near airports or forest fires, low flying aircraft could collide with a small UAS. Enemy operators in a military base or outpost could use a low cost UAS for surveillance or other nefarious purposes. In addition to the problems that UAS can create in terms of a tactical threat, they can also have issues of colliding with birds. Not only can UAS have problems with birds, but birds can be nuisance on airfields as well and can interfere with the taking off and landing of various aircrafts. A key to mitigating the risks presented by these low-cost UAS and birds is the ability to detect them and initiate proper actions. Radar, with its all weather, day and night operation is an excellent option for providing this capability. IMSAR proposes leveraging it's proven capability with machine learning to automatically distinguish between UAS and birds.