Inside Unmanned Systems

FEB-MAR 2017

Inside Unmanned Systems provides actionable business intelligence to decision-makers and influencers operating within the global UAS community. Features include analysis of key technologies, policy/regulatory developments and new product design.

Issue link: http://insideunmanned.epubxp.com/i/792105

Contents of this Issue

Navigation

Page 51 of 59

52 unmanned systems inside February/March 2017 To reliably identify a drone signal, automatic identification is essential. A human operator cannot be expected to understand and assess all signals in all frequency bands in a timely manner. A simple threshold-based RF alarm signal, available on many basic spectrum monitoring systems, can lead to false alarms whenever a signal threshold is detected. The automatic identification system must recog- nize the expected environment and be able to look for identifying parameters of an expected threat from a library of known drone signals. Time is the biggest advantage of an RF detec- tion system during the link establishment, and this advantage must be used to improve situ- ational awareness. Further, there may be situations where a "friendly" drone (white list) would be used for perimeter monitoring or crowd observation, and the individual identification of known "friendly" signals must be assessed against the environment. This presents the possibility of a multi-drone environment where a drone threat (black list) may coexist with a white list asset. This leads to the question of how many threats are expected in an environment. All too often, the performance assessment of a system might use a single threat to vali- date the functionality of a system. However, this could have grave consequences on the in- tended results, especially in the selection of a direction finding technology to be integrated into the system. There is a significant and complex challenge identifying signals in a multi-threat environ- ment (figure 4) especially when you consider the complexity of de-interleaving and sepa- rating the signatures of multiple commercial drones using FHSS technologies. Not only is this a challenge during the signal identification process, but it must also be noted that not all direction finding technology perform equally in a multi-signal environment. This is especially true with direction finding technologies that are not able to distinguish multiple signals at Consider the environment represented in f igure 3. This now becomes a little less ob- vious to a user when considering the typical environment of crowded ISM band technolo- gies. The presence of multiple WLAN access points and user terminals, Bluetooth technolo- gies, and Zigbee® devices, can all be present in the same spectrum and the FHSS drone can be lost in the view. Keep in mind that some drones also use WLAN signals for control or downlink video, so deeper inspection of all sig- nals present is warranted. Figure 5: Example Geo-location of drone threats and a "friendly" drone. ENGINEERING » DRONE TECHNOLOGY sponsored by ROHDE & SCHWARZ Figure 4: A multi-threat environment with multiple drones (uplink and downlink) identifi ed (lower left).

Articles in this issue

Archives of this issue

view archives of Inside Unmanned Systems - FEB-MAR 2017