Kameron Simon, Kratos; Steve Williams, Kratos; Bill Sward, Kratos; Austin Beer, Kratos
Keywords: passive RF
Abstract:
The growing proliferation of space and large constellations of commercial and government satellites is driving the need for rapid location, identification, discrimination, and attribution of non-cooperative space-borne objects. This paper will discuss a technique that should be considered to uniquely identify objects, the fusion of Specific Emitter Identification (SEI) based on RF fingerprints with passive RF passive ranging.
Traditional techniques utilize electro-optical and radar sensors but each of these have their own limitations. Passive electro-optical techniques may perform well during the night but become ineffective during daylight, cloudy conditions, and are hampered by lightning strikes. Also, electro-optical sensors also have a difficult time distinguishing between objects of similar size (i.e. cubesat visual magnitude are similar to each other). Furthermore, as seen with Space X, satellites now have a preponderance of sunshades or are being painted black as to not impact astronomers. This makes it next to impossible for electro-optical techniques to identify or track these objects. Radar is generally effective in all-weather conditions, day or night but have difficulty distinguishing between objects of similar size and shape (i.e. cubesat radar cross sections are very similar to each other). Further, radars transmit high power RF, are expensive, and are therefore sparsely deployed. Passive RF passive ranging can perform in all-weather conditions, day or night and has the ability to uniquely identify objects even if they are the same in size, shape, and brightness.
Passive RF is able to do this by providing a novel and powerful solution that combines passive RF passive ranging with SEI. Passive RF passive ranging is executed by utilizing three or more, physically separated ground sites to observe the same downlink signal from a satellite. Signal processing techniques are then used to determine the location of the space-borne emitter. Collecting and processing downlink samples from several surveyed, time-synchronized ground sites is a mature method of passive ranging orbit determination based upon Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) processing. Benefits include all-weather performance, day or night capability, passive sensing, and improved location accuracy even for non-cooperative emitters.
SEI exploits the unintentional characteristics and perturbations of individual RF transmitters to uniquely identify individual emitters solely from processing their transmitted signal. SEI generally requires only one ground site, although results may improve with multiple sites. SEI implemented with RF Machine Learning (RFML) is an area of active research, experimentation, and field trials with very encouraging results.
This paper will show that by merging passive RF passive ranging with SEI, passive RF will accurately identify, locate, discriminate, and track non-cooperative space-borne objects entirely via passive monitoring of their RF downlinks – even when vehicles fly in clusters or Closely Spaced Operations (CSO). This capability would also enable the SDA community to uniquely identify Space X satellites or other mega constellations when they are launched in clusters. It will allow for a better knowledge base of where objects are in space, preserving the safety of flight for all objects. Kratos has begun practicing some of this today by utilizing an existing network of commercially available satellite ground antennas, the Kratos Global Sensor Network (KGSN), to passively range objects and implement fusion with SEI.
Date of Conference: September 14-17, 2021
Track: Non-Resolved Object Characterization