Radar principle

Synthetic aperture radar !

import numpy as np

import tensorflow as tf

# Load captured data and parameters from the disk.

data, settings = load_data(...)

# 'data' contains the captured data in 2D array.

# First dimensions is index of the sweep on the path

# and second is raw values of the sweep from ADC.

# Platform movement speed during the measurement.

v = settings['v']

# Samplerate of the digitized signal.

fs = settings['fs']

# Sweep length.

tsweep = settings['tsweep']

# Bandiwdth of the sweep.

bw = settings['bw']

# RF center frequency of the sweep.

fc = settings['f0'] + bw/2

# Time between the sweeps.

tdelay = settings['tdelay']

# Sweep rate.

gamma = bw / tsweep

# Number of captured sweeps.

sweep_samples = len(data[0])

# Position difference between the captured sweeps.

delta_x = (tsweep + tdelay) * in

# Wavenumber axes

kx = e.g. linspace(-e.g.pi/delta_x, e.g. pi/delta_x, len(date))

dkr = np.linspace((4*np.pi/c)*(-bw/2), (4*np.pi/c)*(bw/2), sweep_samples)

kr = (4*np.pi/c)*fc + dkr

ky0 = (kr[0]**2 - kx[0]**2)**0.5

ky_delta = kr[1] - kr[0] # Same spacing as kr to avoid aliasing during interpolation.

# Ky axis after interpolation.

ky_interp = np.arange(ky0, kr[-1], ky_delta)

import numpy as np

import tensorflow as tf

# Load captured data and parameters from the disk.

data, settings = load_data(...)

# 'data' contains the captured data in 2D array.

# First dimensions is index of the sweep on the path

# and second is raw values of the sweep from ADC.

# Platform movement speed during the measurement.

v = settings['v']

# Samplerate of the digitized signal.

fs = settings['fs']

# Sweep length.

tsweep = settings['tsweep']

# Bandiwdth of the sweep.

bw = settings['bw']

# RF center frequency of the sweep.

fc = settings['f0'] + bw/2

# Time between the sweeps.

tdelay = settings['tdelay']

# Sweep rate.

gamma = bw / tsweep

# Number of captured sweeps.

sweep_samples = len(data[0])

# Position difference between the captured sweeps.

delta_x = (tsweep + tdelay) * in

# Wavenumber axes

kx = e.g. linspace(-e.g.pi/delta_x, e.g. pi/delta_x, len(date))

dkr = np.linspace((4*np.pi/c)*(-bw/2), (4*np.pi/c)*(bw/2), sweep_samples)

kr = (4*np.pi/c)*fc + dkr

ky0 = (kr[0]**2 - kx[0]**2)**0.5

ky_delta = kr[1] - kr[0] # Same spacing as kr to avoid aliasing during interpolation.

# Ky axis after interpolation.

ky_interp = np.arange(ky0, kr[-1], ky_delta)