Signal Processing - March 2017 - 32
Moreover, the pedestrian detection task
target in Cartesian coordinates and correA realistic simulation
becomes
more challenging due to a smaller
sponding directional velocities is determined
setup should include
RCS of the human body [54]. To make the
based on the current observation (R, i, z)
radiation patterns of the
pedestrian detection robust, the radar based on
and previous state information.
transmit and receive
micro-Doppler estimation can be combined
A key step in tracking is to associate
antenna elements, which
with inputs from a vision sensor [55]. Also,
separately estimated parameters of Q tarthe tracking algorithms discussed previously
gets, particularly velocities (v 1, v 2, f, v Q)
count for the direction
can help predict pedestrian movement [56].
and ranges (R 1, R 2, f, R Q) with each other
dependent scaling of the
Let us discuss how the micro-Doppler
[(R 1, v 1) (R 2, v 2), f, (R Q, v Q)] . After linking
transmitted and reflected
signature is extracted using FMCW radar
estimated parameters with targets, the tarsignals according to the
processing in (5). First, 2-D signal samples
gets are associated with tracks. For example,
geometry of the system.
obtained across slow and fast time are conif each target follows a separate track, then
verted into single dimensional signals by
there are Q tracks in the system. The assorange gating. Typically, FFT is performed across fast time n
ciation problem becomes complex when two tracks cross each
other. Different methods to perform data association include joint
and only the frequency corresponding to the range of interest
probabilistic data association (JPDA), nearest neighbor (NN),
R 0 is retained (assume single target with micromotion at R 0 ).
and fuzzy logic [46].
Neglecting range-Doppler coupling and effect of finite length
Following the data association, tracking can be performed
FFT, (5) can be rewritten as
using well-known algorithms such as Kalman filtering. For
each track, a separate filter is implemented. These filters oper2fc
(14)
dt (p) . a 0 exp ' j2r c
[R 0 + X (pT0)] m1 + ~t (p) ,
ate in parallel. Since the observation vector (R, i, z) has a
c
nonlinear relationship with the state vector (x, y, z, v x, v y, v z),
an extended Kalman filter (EKF) is used. The state equation that
where X (.) is the function characterizing the micromotion of
captures the effect state transition over time [47] is given by
the target. As explained in [52], the short-time Fourier transform
(STFT) of (14) gives the instantaneous variation of Doppler
R
V R
R
V R
V
0 T 0 VWS x [n - 1] W S 0 W
S x [n] W S 1
across time. Detail analysis regarding micro-Doppler vibration
S y [n] W S 0 1 0 T WS y [n - 1] W S 0 W
measurements using FMCW radar is done in [57]. In addition to
S
W =S
WSv x [n - 1]W + Su x [n]W , (12)
1
0
0
0
v
n
[
]
x
pedestrian detection, micro-Doppler also can be used to identify
S
W
S
W S
W
Sv y [n]W SS 0 0 0 1 WWSv y [n - 1]W Su y [n]W
the type of a vehicle (truck, sedan, etc.) by characterizing its
44443X1T442443X T>X
T>X 1T44442
A
State
s [n ]
s [n - 1]
vibration pattern on top of Doppler shift produced by its bulk
Perturbation
motion [58], [59].
where T is the observation interval. The observation vector is
related with state vector via
FMCW radar EM simulation setup
Radar algorithms are often verified by means of simulations,
x 2 [n] + y 2 [n]
which reduces the cost of prototyping and testing. While modR [n]
~ R [n]
;
E =>
E.
y [n] H + ;
(13)
eling the radar systems, the targets and channels under considi [n]
~ i [n]
arctan
x [n]
=
>
x [n]
Observation
eration are assumed to be ideal. The targets are modeled as
14444244443
h (s [n])
noise
objects with perfect reflectivity, and the signals are assumed
to propagate through unobstructed paths. To verify the viaFrom the knowledge of the previous state, the present state is
bility of various radar estimation algorithms in the real
predicted based on the state equation (12). Using (13) and the presworld, it is necessary to use computational EM software to
ent observation, the predicted value is updated. The amount of corsimulate potential target RCSs and channels.
rection depends on the SNR of the observations; see [47] for more
A realistic simulation setup should include radiation patterns
details. Vehicle tracking problems are also addressed in [48]-[51].
of the transmit and receive antenna elements, which count for the
direction dependent scaling of the transmitted and reflected signals
Pedestrian detection
according to the geometry of the system. In addition, EM waves
Pedestrian, bike, and wild life detection is essential for a driver
undergo reflection, diffraction, and scattering, depending on the
assist and collision avoidance system. As a pedestrian walks, a
shape and size of the target with respect to its wavelength. To incorsmall change in range produces very low Doppler shift. In other
porate these phenomena, Maxwell's equations with appropriate
words, the micromotion of a target produces what is known as a
boundary conditions must be solved. Along with numerical commicro-Doppler [52]. Likewise, the periodic motion of limbs creputing, software packages such as MATLAB or MATHEMATates a periodic pattern in velocity over time, which is also
ICA and EM simulators such as ADS [60], FEKO [61] or Xpatch
known as the micro-Doppler signature. This signature, along
[62] can be used for the accurate modeling of the automotive radar
with other feature extraction and matching algorithms, can be
imaging. The effect of RF impairments such as phase noise, local
used to uniquely identify pedestrian walking. More details
oscillator leakage, and in-phase and quadrature imbalance can be
about an analysis of human gait using range-Doppler plots are
modeled either in MATLAB or an EM simulator such as ADS.
given in [53].
32
IEEE SIgnal ProcESSIng MagazInE
|
March 2017
|
Table of Contents for the Digital Edition of Signal Processing - March 2017
Signal Processing - March 2017 - Cover1
Signal Processing - March 2017 - Cover2
Signal Processing - March 2017 - 1
Signal Processing - March 2017 - 2
Signal Processing - March 2017 - 3
Signal Processing - March 2017 - 4
Signal Processing - March 2017 - 5
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Signal Processing - March 2017 - 120
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Signal Processing - March 2017 - 124
Signal Processing - March 2017 - Cover3
Signal Processing - March 2017 - Cover4
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