Using HackRFs to Locate a UAV Transmitter via Signal Strength Analysis
During the 2019 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting conference, authors Xuemei Huang, Kun Yan, Hsiao-Chun Wu and Yiyan Wu presented a research paper titled "Unmanned Aerial Vehicle Hub Detection Using Software-Defined Radio". In their work they describe how they were able to use three HackRFs to determine the location of a UAV drone transmitter. The method they use is fairly simple as it makes use of path loss propagation models to determine an estimated distance from each HackRF, so prior knowledge of the transmitter properties is still required.
The applications of unmanned aerial vehicles (UAVs) have increased dramatically in the past decade. Meanwhile, close-range UAV detection has been intriguing by many researchers for its great importance in privacy, security, and safety control. Positioning of the UAV controller (hub) is quite challenging but still difficult. In order to combat this emerging problem for public interest, we propose to utilize a software-defined radio (SDR) platform, namely HackRF One, to enable the UAV hub detection and localization. The SDR receiver can acquire the UAV source signals. The theoretical path-loss propagation model is adopted to predict the signal strength attenuation. Thus, the UAV hub location can be estimated using the modified multilateration approach by only three or more SDR receivers.
I personally wonder why they did not use the difference in time of arrival of the UAV signal for a some unique part of the signal. Path loss cannot be assumed to be identical for the ground sensors to the UAV. Antenna gain and the pattern, will not be identical and not without lobes and notches.
Using path loss would require all 3 sensor antennas and the UAV antenna to be isotropic, meaning same gain in all azimuth and all elevation angles, without lobes and notches.
Since we live in a real world path loss is highly dependent on e.g. antenna pattern, (varies in azimuth and elevation) antenna elevation above ground, terrain and surface obstacle (trees, building) ground conductivity, weather to name just a few that impact the 2 ray model. Furthermore the aircraft antenna pattern is not static, but also with flight orientation.
If you are interested why I write this, read the RD reports from Johnson and Gierhardt for the IF-77 propagation model. To get an idea look for path loss at the propagation path loss for various antenna heights in the IF-77 atlas, and see how attenuation changes with elevation.
PS.: Unfortuntely you have to pay IEEE to read the paper and find out if all previous word on aeronautical propagation was wrong.
Yeah I was considering not posting this as the path loss method they used is too simple and naive, and probably won’t work in the real world. They only tested for the ground station though, so maybe with a counter UAV and direct line of sight this simple method might sometimes work.
There have been many papers over the years that try to use RSSI for geolocation. They have an ideal and unrealistic setup here with great contrived geometry and clear line of sight. In the real word you are better off waving a yagi around to DF the transmitter.