2023-07-31
Folker Hoffmann, Matthew Ritchie, Francesco Fioranelli, Alexander Charlish, Hugh Griffiths [Hof+16]
I rate this 7.9.
Super cool experiment, I’m frankly shocked that it works as well as it did. I wish I had radars to play with, and results from slightly less cooperative UAVs. Clearly lots of opportunities for “next steps” with this exact hardware setup. I want one.
I know LaTeX and maybe even IEEE insist on letting figures roam the countryside however they please, but good lord, this paper is just a description of figures and none of them are anywhere near the text describing them. 4 of them (called 2 figures, because 1 label applies to multiple figures for maximum confusion) are half way through a page with nothing else other than citations on it. Also raster figures that are just lines and text so I can’t even zoom. Minus 1 point just for this madness.
Unmanned Aerial Vehicles. Very scary. We can’t get experimental data from multistatic radar systems. First experimental validation of multistatic tracking of a UAV using micro doppler for clutter suppression.
NetRAD system is used for experimental trials.
3 identical separate nodes
Drone is a DJI Phantom Vision 2+. Ground truth comes from GPS.
Trees cause noise in the 100 m bins. Ignore it.
UAV can rapidly change velocity and also hover, making it difficult to separate from clutter based on Doppler shift alone. Micro-Doppler means it isn’t lost with lower radial velocity.
Extended Kalman Filter. 4 degrees of freedom drone, just a 2D Cartesian vector. UAV process noise is taken as . I’m surprised this works. Nothing else remarkable, but certainly nothing that feels as oddly specific as I was expecting, quite general.
They fly right along the beam, but have very impressive tracking.
Despite no angular info, high quality tracks.