Tutorials

				
					sample_idx = 20
radar_rel_paths = dataframe['unit1_radar'].values
radar_data = scipyio.loadmat(os.path.join(scenario_folder, radar_rel_paths[sample_idx]))['data']

RADAR_PARAMS = {'chirps':            128, # number of chirps per frame
                'tx':                  1, # transmitter antenna elements
                'rx':                  4, # receiver antenna elements
                'samples':           256, # number of samples per chirp
                'adc_sampling':      5e6, # Sampling rate [Hz]
                'chirp_slope': 15.015e12, # Ramp (freq. sweep) slope [Hz/s]
                'start_freq':       77e9, # [Hz]
                'idle_time':           5, # Pause between ramps [us]
                'ramp_end_time':      60} # Ramp duration [us]

samples_per_chirp = RADAR_PARAMS['samples']
n_chirps_per_frame = RADAR_PARAMS['chirps']
C = 3e8
chirp_period = (RADAR_PARAMS['ramp_end_time'] + RADAR_PARAMS['idle_time']) * 1e-6
    
RANGE_RES = ((C * RADAR_PARAMS['adc_sampling']) / 
                    (2*RADAR_PARAMS['samples'] * RADAR_PARAMS['chirp_slope']))

VEL_RES_KMPH = 3.6 * C / (2 * RADAR_PARAMS['start_freq'] * 
                          chirp_period * RADAR_PARAMS['chirps'])

min_range_to_plot = 5
max_range_to_plot = 15 # m
# set range variables
acquired_range = samples_per_chirp * RANGE_RES
first_range_sample = np.ceil(samples_per_chirp * min_range_to_plot / 
                            acquired_range).astype(int)
last_range_sample = np.ceil(samples_per_chirp * max_range_to_plot / 
                            acquired_range).astype(int)
round_min_range = first_range_sample / samples_per_chirp * acquired_range
round_max_range = last_range_sample / samples_per_chirp * acquired_range

# Range-Velocity Plot
vel = VEL_RES_KMPH * n_chirps_per_frame/2
ang_lim = 75 # comes from array dimensions and frequencies

def minmax(arr):
    return (arr - arr.min())/ (arr.max()-arr.min())

def range_velocity_map(data):
    data = np.fft.fft(data, axis=1) # Range FFT
    # data -= np.mean(data, 2, keepdims=True) 
    data = np.fft.fft(data, axis=2) # Velocity FFT
    data = np.fft.fftshift(data, axes=2)
    data = np.abs(data).sum(axis = 0) # Sum over antennas
    data = np.log(1+data)
    return data

def range_angle_map(data, fft_size = 64):
    data = np.fft.fft(data, axis = 1) # Range FFT
    data -= np.mean(data, 2, keepdims=True)
    data = np.fft.fft(data, fft_size, axis = 0) # Angle FFT
    data = np.fft.fftshift(data, axes=0)
    data = np.abs(data).sum(axis = 2) # Sum over velocity
    return data.T

fig, axs = plt.subplots(figsize=(8,6), ncols=2, tight_layout=True)

# # Range-Angle Plot
radar_range_ang_data = range_angle_map(radar_data)[first_range_sample:last_range_sample]
axs[0].imshow(minmax(radar_range_ang_data), aspect='auto', 
              extent=[-ang_lim, +ang_lim, round_min_range, round_max_range], 
              cmap='seismic', origin='lower')
axs[0].set_xlabel('Angle [$\degree$]')
axs[0].set_ylabel('Range [m]')


radar_range_vel_data = range_velocity_map(radar_data)[first_range_sample:last_range_sample]
axs[1].imshow(minmax(radar_range_vel_data), aspect='auto', 
              extent=[-vel, +vel, round_min_range, round_max_range], 
              cmap='seismic', origin='lower')
axs[1].set_xlabel('Velocity [km/h]')
axs[1].set_ylabel('Range [m]')