import radarsimpy
print("`RadarSimPy` used in this example is version: " + str(radarsimpy.__version__))
`RadarSimPy` used in this example is version: 12.4.0
Doppler of a Turbine¶
Introduction¶
The Doppler effect is a fascinating phenomenon that occurs when there is relative motion between a source of waves and an observer. One intriguing application of the Doppler effect is its occurrence in turbines. Turbines, commonly used in various industries such as power generation and aviation, are known for their rotational motion. As a turbine's blades rotate, they create disturbances in the surrounding fluid or air, resulting in a unique Doppler effect.
In this demonstration, we leverage the powerful ray tracing capability of RadarSimPy to simulate the intricate Doppler signatures induced by a rotating wind turbine. Additionally, we showcase the step-by-step process of plotting these Doppler signatures on a spectrogram, providing a visual representation of the frequency shifts caused by the turbine's rotation. By exploring this simulation and spectrogram plotting, we gain valuable insights into the complex nature of the Doppler effect produced by wind turbines.
Create Radar Model¶
Firstly, import the required modules from radarsimpy. numpy will also be needed in this example.
import numpy as np
from radarsimpy import Radar, Transmitter, Receiver
Transmitter¶
Setup the basic transmitter parameters through Transmitter module.
Define a Radar Transmitter¶
As shown in the diagram below, f and t are used to define the waveform modulation. For a linear frequency-modulated continuous waveform (FMCW), it can be specified as f = [fstart, fend] and t = [tstart, tend]. If t is a single number t = t0, which is equivalent to t = [0, t0]. The bandwidth of the FMCW is abs(fstart - fend). prp is the pulse repetition period, and prp >= (tend - tstart).
| prp
| +-----------+
|
| +---f[1]---> / / /
| / / /
| / / /
| / / /
| / / / ...
| / / /
| / / /
| / / /
| +---f[0]--->/ / /
|
| +-------+
| t[0] t[1]
tx = Transmitter(
f=24.125e9, t=20, tx_power=20, pulses=1, channels=[dict(location=(0, 0, 0))]
)
rx = Receiver(
fs=800,
noise_figure=4,
rf_gain=20,
baseband_gain=50,
load_resistor=1000,
channels=[dict(location=(0, 0, 0))],
)
Radar System¶
Use the defined transmitter and receiver to create the radar system.
radar = Radar(transmitter=tx, receiver=rx)
Target Model¶
Load the stl model of the target and set the location, speed, rotation, and rotation_rate of the target.
location: (x, y, z) mspeed: (x, y, z) m/srotation: (yaw, pitch, roll) degreerotation_rate: (yaw, pitch, roll) degree/s
In this simulation, the target is a 3-blade turbine. The turbine is rotating along the y-axis with 50 degree/s.
target_1 = {
"model": "../models/turbine.stl",
"unit": "m",
"location": (8, 0, 0),
"rotation": [0, 50 * radar.time_prop["timestamp"], 0],
"rotation_rate": (0, 0, 0),
"speed": (0, 0, 0),
}
targets = [target_1]
Plot the model
import pymeshlab
import plotly.graph_objs as go
from IPython.display import Image
ms = pymeshlab.MeshSet()
ms.load_new_mesh(target_1["model"])
t_mesh = ms.current_mesh()
v_matrix = np.array(t_mesh.vertex_matrix())
f_matrix = np.array(t_mesh.face_matrix())
fig = go.Figure()
fig.add_trace(
go.Mesh3d(
x=v_matrix[:, 0],
y=v_matrix[:, 1],
z=v_matrix[:, 2],
i=f_matrix[:, 0],
j=f_matrix[:, 1],
k=f_matrix[:, 2],
intensity=v_matrix[:, 2],
colorscale="Viridis",
)
)
fig["layout"]["scene"]["aspectmode"] = "data"
fig["layout"]["height"] = 800
# uncomment this to display interactive plot
# fig.show()
# display static image to reduce size on radarsimx.com
img_bytes = fig.to_image(format="jpg", scale=2)
display(Image(img_bytes))
Radar Scene Simulation¶
Use the simulator.sim_radar module to simulate the baseband samples from the defined radar system and targets. level='sample' defines ray tracing for each sample point. This is the most time-consuming setup but the most accurate.
The output baseband data is a dict including the timestamp and baseband. Both of them are 3-D matrix:
[channels, pulses, ADC samples]
from radarsimpy.simulator import sim_radar
import time
tic = time.time()
data = sim_radar(radar, targets, density=2, level="sample")
baseband = data["baseband"] + data["noise"]
timestamp = data["timestamp"]
toc = time.time()
print("Exec time :" + str(toc - tic) + "s")
Exec time :130.13297295570374s
Plot baseband samples
fig = go.Figure()
fig.add_trace(
go.Scatter(
x=timestamp[0, 0, :],
y=np.real(baseband[0, 0, :]),
name="I",
)
)
fig.add_trace(
go.Scatter(
x=timestamp[0, 0, :],
y=np.imag(baseband[0, 0, :]),
name="Q",
)
)
fig.update_layout(
title="I/Q Baseband Signals",
yaxis=dict(title="Amplitude (V)"),
xaxis=dict(title="Time (s)"),
)
# uncomment this to display interactive plot
# fig.show()
# display static image to reduce size on radarsimx.com
img_bytes = fig.to_image(format="jpg", scale=2)
display(Image(img_bytes))
Short-Time Fourier Transform¶
from scipy.signal import stft
spec = stft(
baseband[0, 0, :],
fs=radar.radar_prop["receiver"].bb_prop["fs"],
nperseg=128,
noverlap=120,
nfft=512,
return_onesided=False,
)
Plot spectrogram
idx_neg = np.where(spec[0] < 0)
idx_pos = np.where(spec[0] >= 0)
dopp_ordered = spec[0][np.concatenate([idx_neg[0], idx_pos[0]])]
spec_shifted = spec[2][np.concatenate([idx_neg[0], idx_pos[0]]), :]
fig = go.Figure()
fig.add_trace(
go.Heatmap(
z=20 * np.log10(np.abs(spec_shifted)),
x=spec[1],
y=dopp_ordered,
hoverongaps=False,
colorscale="Jet",
)
)
fig.update_layout(
yaxis=dict(title="Doppler (Hz)"),
xaxis=dict(title="Time (s)"),
margin=dict(l=0, r=0, b=0, t=20),
)
# uncomment this to display interactive plot
# fig.show()
# display static image to reduce size on radarsimx.com
img_bytes = fig.to_image(format="jpg", scale=2)
display(Image(img_bytes))
