SBIR-STTR Award

Technology is proposed that exploits coherent waveforms generated by a multitude of RF systems to detect, classify, localize and track aircraft. This capability is derived from existing bodies of research as well as applied development activities undertaken by the team to measure atmospheric conditions using opportunistic sources like 4G and 5G LTE, broadcast television and emergency responder signals. This capability requires no RF band licensing, exploits the coherent nature of the underlying waveforms, is implemented using common Software Defined Radio (SDR) semiconductors, and can be used for both terrestrial based systems or airborne systems. Novelty is derived by exploiting the underlying coherent waveforms as well as the precision timing of these signals. The near ubiquity of RF systems from 10MHz to 60GHz provides a plentiful range of signals to harvest for our multi-static detection capability. The fact that many of these systems are critical infrastructure requires them to maintain operations in nearly all conditions - often having regulatory requirements for 99.999% operational availability. The number and types of signals offer exceptional source redundancy allowing a system to "switch" between sources depending on operational conditions and resolution required. This provides inherent system robustness. This class of passive Radar system can be used to augment air traffic control and traffic management in heretofore never experienced patterns and densities associated with urban air mobility and unmanned air vehicle concepts. As Agility Prime advances these systems control and monitoring systems like this will become critical for regulatory approval. Management of air traffic in urban and suburban areas will require a multitude of monitoring systems irrespective of the capabilities required by regulatory bodies for aircraft organic sensor systems to track their position and report it via data links. Clearly the potential for aircraft to have failures, be intentionally tampered with, or to spoof other aircraft in a highly congested airspace, particularly around hubs, suggests that terrestrial and other airborne tracking systems will be required. The ability to license RF channels to operate new active systems is very difficult in most regions. The potential for passive Radar where RF signals - from HF through K band - are plentiful is a logical application. The approach we pursue is a novel adaptation of applied research conducted by the team to measure temperature and humidity using opportunistic signals. In that effort we used commercially available SDR chip sets. In this effort we will use similar SDR chip sets to ultimately arrive at a very low cost adaptive receiver. The architecture has the capability to be an exceptionally low "Size Weight and Power and Cost" (SWaP-C) device that has a broad range of military, commercial and consumer applications.

In the last decade there has been an exponential growth in the technology and application of unmanned air vehicles (UAVs) or "drones". Once simple radio-controlled aircraft, modern UAVs are able to operate autonomously, in all conditions, and carry significant payloads. Civilian applications include drones for package delivery, inter- and intra- campus transport, eVTOL urban air mobility (UAM) for materials and people. In the defense sector, the US military has long used UAVs for ISR and for tactical operations. UAV and drone technology are now available to our advisories, having been used to target oil plants and military bases and operations. For commercial and non-hostile applications, tracking air vehicles, atmospheric conditions, and weather is critical to the widespread adoptions of manned and unmanned air vehicles (M/UAVs). However, neither transponders and beacons integrated in M/UAVs, nor implementation of a radar-based air traffic control (ATR) are practical due to spectrum regulation, development times, weight and cost. For non-cooperative and hostile applications, the lack of terrestrial based detection and tracking is a critical security gap; there is a requirement for "drone sensors" that can detect, classify, and locate a hostile drone to protect military operations. Currently there is no available passive M/UAV or drone sensor for either civilian or military applications. To address these critical gaps, 0BD and WRC are developing technology that exploits ubiquitous digital waveform broadcast (FM, TV), cellular network (4G, 5G), and other wireless signals as illumination sources of opportunity. The illumination sources are reflected passively off M/UAVs or phase shifted by weather and atmosphere. By collecting and analyzing these reflected and phase shifted signals, we have shown in Phase 1 and previous work, the ability to detect, classify, and locate M/UAVs and measure changes in atmospheric and meteorological conditions. We propose in this Phase 2 STTR to develop, prototype, and fabricate RF receivers and integrated signal processing to capture the opportunistic signals reflected by M/UAVs and use Deep Convolutional Neural Networks (DCNNs) that employ Transfer Learning (TL) to autonomous detect, classify, and track M/UAVs, outputting data that can be shared with other command and control systems.