雷达原理

　　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) * v
　　# Wavenumber axes
　　kx = np.linspace(-np.pi/delta_x, np.pi/delta_x, len(data))
　　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) * v
　　# Wavenumber axes
　　kx = np.linspace(-np.pi/delta_x, np.pi/delta_x, len(data))
　　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)