Polarized Radar for Detection and Automatic Non-Visual Assessment of Unmanned Aerial Systems.

Technical Report · Tue Oct 01 00:00:00 EDT 2019

DOI:https://doi.org/10.2172/1570099· OSTI ID:1570099

McVay, John Anthony Bacon

This Laboratory Directed Research and Development ( LDRD ) effort performed fundamental Research and Development ( R&D ) to develop a robust radar processing algorithm capable of assessing the difference between an Unmanned Aerial System (UAS) and a biological target such as a bird, based on mathematics applied to the polarized radar returns of the target object , alone. The current threat s of using such a UAS as a delivery platform for a host of destructive components is a major concern for the protection of various assets. Most r ecently , on 14 th Sept . 2019, dozens of suicide or kamikaze drones (UAV - X) coordinated an attack o n two Saudi oil facilities that demonstrated the potential to disrupt global oil supplies. While r adar - based UAS detection systems can detect UAS at ranges greater than 1 - k m, the issue s of excessive Nuisance/False Alarm Rates (NAR/FAR) from natural sources (birds in particular) has not been sufficiently addressed. In this effort we describe and utilize the Adaptive Polarization Difference Imaging - based (APDI) algorithm s for the d etection and a utomatic n on - v isual a ssessment of Unmanned Aerial System applications. Originally developed for optical imaging and sensing of polarization information in nature, the algorithm s developed here are modified to serve for the target detection purposes in counter - UAS (cUAS) environment s . We exploit the polarization statistics of the observing scene for detection and identification of changes withi n the scene and assess from these changes for UAS/bird classifications . Several cases are considered from independent data sources, including numerically generated data, ane choic chamber data as well as experimental radar data, to show the applicability of the technique s developed here . The method s developed in this effort are designed to be used in cUAS setups but have shown promise for a multitude of other radar - based class ification uses as well. ACKNOWLEDGEMENTS The authors would like to acknowledge the efforts of Robby L. Robertson (R3 Technologies LLC) and Manuel Rangel ( Chief Technology Officer of R3 Technologies LLC ) whom were crucial to project success in supplying sophisticated and sensitive radar equipment capable of both co - polarization as well as cross - polarization measurements, supplying and setting up targets and in particular providing measured data on living falcons, which were kindly provided by Matthew Mitc hell, a native New Mexican whom has been a master falconer for the past 40 years along with breeding hawks and falcons for over 20 years. The authors would like to further acknowledge David K. Novick ("Daveman") (Sandia Organization 0 6533 ) for providing a supply of UAS for radar measurements, and the efforts of Donald P. McLemore ( Sandia Organization 0 1354 ) and Jaclynn J. Stubbs (Sandia Organization 0 6514 ) whose supplementary processing techniques are included in the A ppendi c es here, as well as Charles Q. L ittle (Sandia Organization 0 6535 ) for his additions to this project . The acknowledgements would not be complete without recognizing the efforts of Thomas E. Roth and Ward E. Patitz (Sandia Organization 05345) for their assistance and efforts in the collect ion of Radar Cross Section r esults from the Facility for Antenna and Radar - Cross - Section Measurements (FARM) . Their efforts in the data collection and especially for the calibration of the measured data w ere critical to project success.

• Research Organization: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 1570099
• Report Number(s): SAND2019-12053; 680123
• Country of Publication: United States
• Language: English