Package specifically created for the MiloSar System. For system details see:
Jordan, D., et al. "Development of the miloSAR testbed for the one kilogramme radioCamera SAR for small drones." 2019 IEEE Radar Conference (RadarConf). IEEE, 2019.
This project is meant to get one started in getting images focused using Backprojection. It should lead to a more fleshed out, Backprojection library. Minimum getting started:
import MiloSarBP
import PyPlot
config = MiloSarBP.Config(
# Data Input Directory
"PATH_TO_FLIGHT/23_06_21_12_30_14",
# Range Offset due to system specifics
14.7
);
# Read the radar data
# data: Dataframe with Timestamp, Position, Orientation and Raw Radar Signal
# bandconfig: Configruation of the radar parameters (ramp steepness etc.)
data, bandconfig = MiloSarBP.read(config);
# Area in UTM Coordinates
area = MiloSarBP.generate_position_grid(
# Bottom Left
[2.609400501848896e+05, 6220761.085691286, 50.417764027515660],
# Top Right
[2.609948638294746e+05, 6220815.581316797, 49.323503814433934],
# Resolution
0.05
)
# Generate the Image. Only use part of the data, where the area is very visible
image = MiloSarBP.backprojection_vec(data[1140:5530], area, config, bandconfig);
scr, peak, clutter = MiloSarBP.Quality.signal_clutter_ratio(image);
println("SCR: $scr dB")
plt = MiloSarBP.Plot.backprojection(image, area)
PyPlot.clim(clutter-0.5, peak)
# PyPlot.gcf() # When in jupyter notebooksThe package Revise should be used when developing the package. E.g.
import Pkg
# Activate the environment of the package which is developed, so you use this version as working code and not the published package
Pkg.activate("path/to/MiloSarBP-jl/")
using Revise
import MiloSarBP
# When editing functions inside MiloSarBP, the package will be recompiled- Download Julia
- Getting Started