Concept of Micro doppler shift and its implementation in MATLAB.

1. Micro Doppler Estimation
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Broadband Wireless -LTE 4G
(16EC72)
Sl No. Name USN
1. PAVAN HK 1RV18EC416
2. Sainath Urankar 1RV18EC422
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2. • A moving target introduces a frequency shift in the radar return due to
Doppler effect.
• However, because most targets are not rigid bodies, there are often
other vibrations and rotations in different parts of the target in addition
to the platform movement.
• For example, when a helicopter flies, its blades rotate, or when a person
walks, their arms swing naturally.
• These micro scale movements produce additional Doppler shifts, referred
to as micro-Doppler effects, which are useful in identifying target
features.
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3. • Understanding the Micro Doppler effects with reference to
LTE and wireless communication.
• To identify a pedestrian in an Automotive radar using Micro
Doppler Signatures in MATLAB.
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4. • Considering an ego car with an FMCW automotive radar
system whose bandwidth is 250 MHz and operates at 24
GHz.
• The ego car is traveling along the road. Along the way,
there is a car parked on the side of street and a human
is walking out behind the car. The scene is illustrated in
the following diagram.
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Pedestrian Identification in Automotive Radar
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Ego Car
Parked Car
Pedestrian

5. • The following figure shows the
range-Doppler map generated from
the ego car's radar over time.
Because the parked car is a much
stronger target than the pedestrian,
the pedestrian is easily shadowed by
the parked car in the range-Doppler
map.
• As a result, the map always shows a
single target.
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Pedestrian Identification in Automotive Radar
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6. • This means that conventional processing cannot satisfy our needs under
this situation.
• Micro-Doppler effect in time frequency domain can be a good candidate to
identify if there is pedestrian signature embedded in the radar signal.
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Pedestrian Identification in Automotive Radar
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7. • As an example, we are simulating
the radar return for 2.5 seconds.
• If we generate a spectrogram using
only the return of the pedestrian, we
obtain a plot shown in fig.
• Note that the swing of arms and legs
produces many parabolic curves in
the time frequency domain along
the way. Therefore such features
can be used to determine whether a
pedestrian exists in the scene.
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Pedestrian Identification in Automotive Radar
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8. • When we generate a spectrogram directly from
the total return, we get the following plot.
• What we observe is that the parked car's return
continue dominating the return, even in the
time frequency domain. Therefore the time
frequency response shows only the Doppler
relative to the parked car. The drop of the
Doppler frequency is due to the ego car getting
closer to the parked car and the relative speed
drops towards 0.
• To see if there is a return hidden behind the
strong return, we can use the singular value
decomposition.
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Pedestrian Identification in Automotive Radar
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9. • From the curve, it is clear that there are
approximately four regions. The region A
represents the most significant contribution to
the signal, which is the parked car.
• The region D represents the noise. Therefore, the
region B and C are due to the mix of parked car
return and the pedestrian return. Because the
return from the pedestrian is much weaker than
the return from the parked car.
• In region B, it can still be masked by the residue of
the return from the parked car. Therefore, we
pick the region C to reconstruct the signal, and
then plot the time frequency response again.
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Pedestrian Identification in Automotive Radar
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10. • With the return from the car
successfully filtered, the
micro-Doppler signature from
the pedestrian appears.
• Therefore, we can conclude
that there is pedestrian in the
scene and act accordingly to
avoid an accident.
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Pedestrian Identification in Automotive Radar
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