Modified Space-Time Adaptive Processing for
Dismount Detection using Synthetic Aperture Radar
Ke Yong Li & Faruk Uysal
C & P Technologies, Inc.,
Closter, NJ
[email protected] & [email protected]
S. Unnikrishna Pillai
Polytechnic Inst. of NYU, Brooklyn, NY
and
C & P Technologies, Inc., Closter, NJ
Linda J. Moore
AFRL, Sensor Directorate,
WPAFB, OH
Abstract—This paper presents a new adaptive radar signalprocessing technique for dismount detection using SyntheticAperture Radar (SAR). The new approach uses the complexnature of the Doppler response scattering from the dismountsrotary motion to modify the conventional Space-Time Adap-tive Processing (STAP). This is used for dismount detectionwherein resolution is dictated by the sensor system platform.The feasibility of the modified STAP (M-STAP) is demonstratedby simulations and by applying it to the Gotcha data set fordismount detection.
I. INTRODUCTION
Synthetic aperture radar (SAR) has in recent years emerged
as a key sensory resource due to the deployability, all-
condition operating capacity, and safe stand-off distance of
SAR-equipped mobile systems. Currently, these systems are
used primarily for collecting high-resolution imagery of sta-
tionary ground scenes. The unique capabilities of the SAR-
equipped mobile system for interrogating otherwise inaccessi-
ble terrain, however, provide strong motivation for developing
signal processing techniques for utilizing SAR data for ad-
ditional applications such as ground moving target indication
(GMTI) and detection of dismounts. The focus of this paper
is using SAR data for the reliable detection and localization
of dismount targets.
A number of challenges exist in adapting SAR data for
detecting slowly-moving, low radar cross section (RCS) targets
such as dismounts. The clutter may include highly reflective
terrain features, windblown vegetation and other moving ob-
jects including vehicles. This highly challenging clutter scene
presents a host of difficulties for extracting useful information
about the presence and locations of dismounts in the scene.
Clutter cancellation through Doppler processing can be
extended for multi-element arrays, by an integrated model
weighting returns over the element (space) domain as well as
the Doppler (time) domain leading to Space-Time Adaptive
Processing (STAP) [1]–[3]. This approach takes advantage of
degrees of freedom in array element weighting as well as
pulse weighting to adaptively cancel clutter. The difficulty in
applying STAP to the dismount problem lies, as with pure
pulse-Doppler processing, in the fact that detection of the
The work described here was supported by the Air Force ResearchLaboratory (AFRL) Sensors Directorate under SBIR Contract FA8650-11-M-1127. (Approved for Public Release, Distribution Unlimited. Public ReleaseNumber: 88 ABW-12-1076)
Doppler return originating from only the linear component of
a dismount’s velocity is ineffective when buried in realistic
clutter. Present efforts take advantage of the fact that the
signature of a moving dismount is modulated by the cyclical,
non-linear micro-motions involved in ambulations [4]–[6]. The
complex space-time signature unique to dismount ambulation
is then included in the STAP formulation, generating a modi-
fied STAP (M-STAP) algorithm [7]. This algorithm optimally
detects the desired target motion class, while rejecting the
undesired classes (clutter, vegetation, vehicles, etc.) according
to their differences in the generalized coordinates that describe
the motion model.
II. SPACE-TIME ADAPTIVE PROCESSING
This section gives a brief summary of conventional STAP,
followed by our modification to accommodate the dismount
rotary motion.
A. Conventional STAP
Robust target detection depends on joint manipulation of
degrees of freedom in space and time. Towards this, consider
an N -element linear phased array, so that [1]–[3]
a(θ) =[
1, e−j2π d sin θ
λ , . . . , e−j2π(N−1)d sin θ
λ
]T
(1)
gives the spatial steering vector that results in a beam pointing
at an angle θ . Here, λ is the carrier wavelength and d is inter-
element spacing. This, together with the temporal steering
vector
b 1(ωd) =[
1, e−jπωd , e−jπ2ωd , . . . , e−jπ(M−1)ωd
]T
(2)
for a series of M pulses, with normalized Doppler component
ωd =2V Tr
λ/2, (3)
gives the MN × 1 conventional space-time steering vector
s(θ, ωd) = b1(ωd)⊗ a(θ) (4)
associated with angle θ and Doppler component ωd (or veloc-
ity V ) [1]–[3]. In (4), the notation ⊗ represents the Kronecker
product [2], [3]. In conventional STAP processing, the clutter
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c(t) is adaptively cancelled using the adaptive weight vector
given by [1]–[3],
wc = R−1c s(θt, ωdt
) Rc = E {c(t)c∗(t)} > 0 (5)
where Rc represents the clutter plus noise total interference
covariance matrix, and s(θt, ωdt) represents the space-time
steering vector associated with the target of interest located at
angle θt and Doppler ωdt. In practice Rc is unknown, and is
estimated from the neighbouring data sets, using the stationary
assumptions. To study the effect of the space-time adaptive
canceller in (5), the output
Pc(θ, ωd) = |w∗
c s(θ, ωd)|2
(6)
is generally used [1]–[3]. As remarked earlier, such an ap-
proach alone is inadequate to detect low RCS dismounts.
Additional a-priori information such as dismount’s non-linear
motion model should be integrated with the STAP formulation
[4] [8].
B. Modified STAP
In this section, we modify the space-time adaptive process-
ing approach for dismount detection. In conventional STAP,
the space-time steering vector is a function of two variables:
the expected angle θ and a single Doppler component ωd as
given by (3). However, as Fig. 1 demonstrates, the instanta-
neous velocity of a walking person is more complex than just a
constant linear velocity –the complex dynamics of ambulation
should contribute additional terms to a target’s Doppler profile
[4]–[6], [8].
Fig. 1. Illustration of linear and rotational motion in ambulation [4].
If the additional dynamics are modelled as a single rotary
motion with radial distance r, constant rotary speed νo, and
an unknown initial phase φ, then the round-trip delay τ(t)generated by a reference point on the dismount has the form
[4], [5], [8], [9]
τ(t) = 2V t+ r sin(νo t+ φ)
c. (7)
In this case, after pulse compression a typical transmit
waveform centered at frequency ωo will generate a baseband
signal of the form,
x(t) = Ae−jωoτ(t) + c(t) + n(t) (8)
where c(t) and n(t) represent the clutter and noise components
in the received signal. In a multi-pulse situation, for the kth
pulse, the target return signal is given by
x(kTr) = e−jkωde−β sin(kνoTr+φ) where β =2πr
λ/2. (9)
From (7) and (9), the temporal part of the dismount motion
has two components. The Doppler component involving ωd
can be captured by the temporal Doppler steering vector in
(2). To incorporate the body motion in Fig. 1, define rotary
based temporal steering vectors
b 2(νo) =[
1, e−jβ1 sin νo Tr , . . . , e−jβ1 sin(M−1)νo Tr
]T
(10)
and,
b 3(νo) =[
1, e−jβ2 cos νo Tr , . . . , e−jβ2 cos(M−1)νo Tr
]T
(11)
where β1 = β cosφ and β2 = β sinφ.
Notice that effect of the rotary-type body motion is a
modulation of the temporal Doppler component as in (9) so
that the overall temporal steering vector b(ωd, νo) is given by
b(ωd, νo) = b 1(ωd)� b 2(νo)� b 3(νo), (12)
where � represents the element–wise Schur product between
two vectors. Using (1) and (12), we obtain the modified space-
time steering vector to be
s̃(θ, ωd, ν) = b(ωd, ν)⊗ a(θ) (13)
and the modified adaptive weight vector is given by,
w̃t = R−1c s̃(θt, ωdt
, ν) (14)
Notice that from (14), the modified beamformer is also given
by
w̃b = s̃(θ, ωd, ν). (15)
The signal to interference plus noise ratio loss (SINRloss)
defined as,
SINRloss =|w̃∗
t s̃(θ, ωd, ν)|2
w̃∗
tRcw̃t
(16)
can be used to evaluate the performance of the proposed mod-
ified STAP algorithm. However, given a data set containing
dismounts, to test the effectiveness of the proposed approach
it is best to verify how detection results match with the ground
truth. Hence, to study the the effect of the modified STAP on
the actual data for dismount detection, the modified STAP
output,
P (θ, ωd, ν) = |w̃∗
x|2
(17)
is preferred. In (17), the weight vector w̃ can be the modified
adaptive weight vector w̃t; or the modified beamformer w̃b.
In principle, (10) to (17) involve searching over four dimen-
sions. However, for the purpose of analyzing the Gotcha data,
we have set φ = 0 in (7) and r = 0.25 meters [7], [8]. In that
case, the temporal steering vector in (12) reduces to
b(ωd, νo) = b 1(ωd)� b 2(νo). (18)
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III. SIMULATION RESULTS
For the purpose of simulation, the design parameters shown
in Table I (same as in [4]) are used.
TABLE ISIMULATION PARAMETERS
Design Parameters
Frequency 10 GHz (λ=3cm)
Antenna Dimensions 0.45 m x 0.11 m
Spatial Channels 4
Height 5 km
Platform Velocity 100 m/sec (side looking)
Pulse repetition frequency (PRF) 800 Hz
Fig. 2 shows the results of simulation using conventional
STAP in (6) for a dismount with an average linear speed
of V = 3m/ sec (runner) and rotational speed of νo =3 steps/sec, radial distance r = 0.5 and φ = 0 with the target
portion of the data generated as in (9) using the parameters
described above. The dismount signal to noise ratio is set at
0 dB, while the clutter to noise ratio is 40 dB. In this case,
the number of pulses M is set equal to 800. Notice that the
conventional STAP output in Fig. 2 using (5) exhibits multiple
Doppler jitter components, although the data contains a single
dismount with rotational velocity. This behaviour coincides
with the micro-Doppler effect described elsewhere, and it fails
to discriminate the rotary motion of the dismount [5], [7].
Fig. 2. Conventional STAP output.
To rectify this situation, it is necessary to introduce the
rotary aspect into the STAP modeling. The rotary component
can be captured by implementing the new steering vectors in
(10) - (13). Fig. 3 shows simulation results using the modified
STAP in (17) with the same data set used to generate Fig. 2.
Notice that in this idealistic simulation the dismount is
detected here, indicating that the new method has the potential
to detect weak dismounts in strong clutter when the model
matches with the underlying data set. In that case, the modified
adaptive processor is able to separate out the dismount from
the clutter as illustrated in Fig. 3.
IV. M-STAP APPLIED TO GOTCHA DATA
The Air Force Research Laboratory (AFRL) Gotcha pro-
gram has released a set of SAR data to provide researchers
and engineers with a source for performing realistic scene
imaging and other applications [10]. Results presented here are
(a)
(b) (c)
Fig. 3. Modified STAP output and side-views: (a) Modified STAP output.(b) Output power vs Velocity, (c) Output power vs steps/sec.
based on another data set (of the same data collection as [10])
provided by AFRL for moving target (dismount) indication,
and coherent change processing. This dataset is comprised
of continuous video phase history (VPH), together with air
truth data describing the aircraft position, heading, velocity,
and acceleration; and meta-data containing information about
the scene being surveilled. Data collection took place using an
aerial X-band SAR platform over several hours, illuminating
a stationary-center scene in spotlight mode.
Using the phase history from the SAR data (18,000 pulses),
an image of the target scene has been formed by using
the backprojection method and it is shown in Fig. 4 [11].
The resultant image appears to the eye much like an aerial
photograph of the scene. Obviously, it is difficult to identify
a low RCS object such as a dismount using SAR imaging
only, and an adaptive clutter cancellation algorithm should be
employed for this purpose.
Fig. 4. SAR image – Dismount ground truth and observation scene.
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Towards this purpose, after a coordinate system transfor-
mation as indicated in [7], let xx, y, z refer to the space-time
data set corresponding to a point (x, y, z) at time tn. Data
set xx, y, z can be used in (17) to obtain the STAP output
Px, y, z(ωd, ν). There are only a few channels in the current
Gotcha data set. The number of pulses (M = 1, 000) is ad-
justed to minimize range walk during the coherent processing
interval (CPI).
In this case, the clutter covariance matrix Rc in (5) needs
to be estimated using the data xx, y, z from surrounding range
bins using (6). As remarked above, with the number of degrees
of freedom in the temporal domain set to 1, 000, assuming the
clutter is wide sense stationary, the number of neighbouring
range bins required for estimating Rc needs to be around
2MN for optimal performance [1], [3], [12]. However, there
are not enough range bin data available in the current Gotcha
data set, and moreover the data scene is not stationary.
The Gotcha data was collected over a region with multiple
terrain types and varying elevation. In general, heterogeneous
terrain, large discretes such as hills and paved roads, and addi-
tional targets all contribute to nonstationarity. As a result, the
clutter in the Gotcha collection is expected to be nonstationary.
To fully take advantage of adaptive clutter cancellation, the
number of nearest range bins used needs to be large enough
while maintaining the stationary assumption so that Rc can be
accurately estimated. For various reasons, the useful number
of range bins turns out to be around 250 for the Gotcha data
set, and we have used 150 range bins to estimate the clutter
covariance matrix Rc. The rank deficiency was rectified by
using diagonal loading to accomplish the matrix inversion in
(14). The modified STAP output for a single location in Fig.
5(a) is shown in Fig. 5(b).
(a) (b)
Fig. 5. Application of M-STAP for single point: (a) Observation Scene, (b)Modified-STAP output for one location
For dismount detection, every location (x, y, z) within the
observation scene in the image in Fig. 5(a) is similarly
processed using the modified STAP in (17) with w̃ =R
−1c s̃(θx, y, z, ωd, ν) as in (14). Here θx, y, z refers to the look
angle corresponding to the ground location (x, y, z). Thus, for
each location in the image in Fig. 5(a), the modified STAP
output in Fig. 5(b) can be computed.
Ideally, the maximum peak should occur in the modified
STAP output when the search parameters match the dismount’s
Doppler and rotary motion. To identify this, the peak value of
(a) (b)
Fig. 6. Scene-mapping procedure.: (a) Modified-STAP output as in Fig. 5(b),(b) Detector output
the modified STAP output in Fig. 6(a) (same as Fig. 5(b))
is then mapped back to the observation scene to create the
final detector output in Fig. 6(b). In our approach, for each
point (x, y, z), a search is carried out in the (ωd, ν) domain
to generate a single value
Qx, y, z(tn) = maxωd
maxν
Px, y, z(ωd, ν) (19)
that represents the 2-D detector output value corresponding to
time tn. Thus, the modified STAP output Px, y, z(ωd, ν) shown
Fig. 5(b) or Fig. 6(a) for point (x, y, z) can be used to generate
Qx, y, z(tn) in Fig. 6(b) for every point within the observation
scene. Thus, a high value in Fig. 6(b) at a given location
can be interpreted as an increased likelihood of a dismount-
like object at that location. High values along the diagonal
ridge in Fig. 6(b) show the lack of cross range resolution for
the Gotcha data set due to its platform geometry. Fig. 7(a)
shows the top view of the final detector output Qx, y, z(tn) for
time tn =18:36:36 when a dismount is known to be present.
The elevation z in (19) is fixed to the corresponding value
of the dismount at this time instant. In practice, the dismount
elevation may be unknown and local elevation may be used
in its place. Ideally, the final output so generated should peak
at the dismount location. However, as Fig. 6(b) shows, that
is not the case here, and instead of a peak, a diagonal ridge
(spread) is observed. As shown later (Fig. 10), this spread
can be attributed to the poor cross-range resolution due to the
Gotcha data collection platform geometry.
(a) (b)
Fig. 7. Modified STAP final outputs top-view: (a) Dismount is present, (b)Dismount is absent
To illustrate our approach further, the final 2-D output of the
observation scene Qx, y, z(tn) without the dismount present,
can be computed using the data corresponding to the same
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(a) (b)
Fig. 8. True and estimated dismount path using M-STAP when the dismountis present: (a) Dismount path recorded by GPS, (b) Detected Dismount Path
location at a different time instant. Fig. 7(b) shows the 2-
D detector output of the same observation scene (as in Fig.
4) for the time instant tn =18:39:00 when the dismount is
known to be absent. Notice that when the dismount is present
(Fig. 7(a)), the final detector output exhibits different features
when compared with the output generated when the dismount
is absent (Fig. 7(b)). Further, when the dismount is present,
the final output peak is about 10 to 15dB higher than when
the dismount is absent (around 150dB vs. 135dB). The ideal
dismount response should be a narrow peak at the dismount’s
location, however this depends on the inherent cross range
resolution projected by the platform.
Finally, the above procedure can be used to create the
detector outputs of the observation scene in Fig. 4 for various
time instants when the data is available (with and without
dismount present). When a dismount is detected, for each time
instant, a weighted average of the location associated with
the relative peaks in Fig. 7(a) is generated (details omitted)
and displayed in Fig. 8(b) to show an estimated location of
the dismount corresponding to that time instant [7]. Fig. 8(b)
shows the estimated dismount locations for ten different time
instants and for the purpose of comparison the corresponding
dismount GPS recordings are shown in Fig. 8(a). In principle,
these outputs can be used to obtain an estimated track of the
dismount, provided the resolution is of the order of the range
bin.
Compared with the actual dismount location shown in Fig.
8(a), the estimated path in Fig. 8(b) is not very accurate using
the current modified STAP. These estimates are poor in the
cross-range direction (see Fig. 6(b), Fig. 7(a)) due to the small
aperture of the platform used for Gotcha data collection. It is
noteworthy that although the location accuracy is poor, the
ability for detection is promising.
To validate the proposed approach further, the M-STAP
algorithm has been applied to the same observation scene for
ten time instants when the dismount is known to be absent.
Using the observation in Fig. 7, a threshold level of 145 dB has
been used for dismount detection. The results are summarized
in Fig. 9. Notice that only one false detection is observed (see
Fig. 9(b)) through this point-by-point processing over ten time
instants.
In summary, Fig. 8 and Fig. 9 illustrate the use of M-STAP
(a) (b)
Fig. 9. True and estimated dismount path using M-STAP when the dismountis absent: (a) Dismount is out of scene, (b) Detected dismount path
for dismount detection. Both figures correspond to the same
observation scene. Fig. 8 shows ten time instants when the
dismount is known to be present and Fig. 9 shows ten time
instants when the dismount is absent. Notice that in Fig. 8(b),
the dismount has been detected nine out of ten time instants,
although the location estimates are not accurate. However, with
no dismount present, only one false alarm is observed in Fig.
9(b). This is a modest success for the modified STAP algorithm
in terms of detecting a dismount in presence of poor cross-
range resolution accuracy.
Gotcha Data Limitations
As Fig. 8 and Fig. 9 demonstrate, the modified STAP algo-
rithm appears to have modest success in detecting whether the
dismount is present or absent. However, the estimated location
is not currently accurate. The poor cross-range resolution of
the Gotcha data set limits the detection of dismount’s exact
location. As seen from Fig. 7(a) it is hard to make a location
estimation over the cross-range direction, because the final
output is smeared in the cross-range direction.
Fig. 10. Normalized array gain pattern corresponding to the sensor arrayused for Gotcha data collection.
To understand the reason for the poor cross range resolution,
notice that in the current Gotcha data collection, the ground
range between the platform and the aim point is of the
order of 10km. Even for a large observation scene (100m x
100m), 120m cross-range corresponds to just 0.95◦ azimuth
variation. The receiver pattern for the sensor array used for
data collection is shown in Fig. 10. Notice that for the direction
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of the observation scene, the array gain is nearly constant over
0.95◦. The 3dB beamwidth in fact corresponds to over 200
meters in cross range, hence it results in poor cross-range
resolution and the modified STAP algorithm is not able to
estimate the location of the dismount accurately in the cross-
range direction. This however, should not to be confused with
the resolution obtained by SAR, where the integration angle
dictates the resolution and can lead to excellent cross-range
resolution (in centimeters). But as Fig. 4 shows, detection
of slow moving low RCS targets such as dismounts using
conventional SAR is an even bigger challenge! STAP-like
adaptive algorithms are required for clutter suppression and
to amplify the ambulatory motion of the dismount.
V. CONCLUSIONS
This paper presents a modification to the conventional
space-time adaptive processing that improves the dismount
detection ability using SAR data. The current approach mod-
ifies STAP with a dismount-like rotary motion based on that
presented by Hersey et. al [4] and Tahmoush et. al [8]. A
modified STAP formulation using two generalized temporal
steering vectors is presented here along with clutter can-
cellation. The detection performance using two key velocity
components - linear and rotary - are highlighted. The new
algorithm demonstrates promising dismount detection ability
when tested on aerial spotlight-mode SAR data in a clutter
scene, at a stand off distance of over 10km, despite using
a simplified motion model and small aperture processing.
Increasing the cross-range resolution accuracy should result
in accurate determination of the dismount location.
It will be interesting to study the performance of the other
robust STAP methods that have been suitably modified to
accommodate for the rotary effect. The differences between
the hypothesized signal model in (7) and the actual dismount
features/parameters in Gotcha data have no doubt contributed
to the mismatch between the actual and observed outputs in
Fig. 8 and Fig. 9. One approach to minimizing this effect is to
combine the proposed algorithm with target tracking so that
detection is not performed on a case by case basis (as we have
done here), but those outputs are used to make a posteriori
prediction on the latest target location. Such a combined
detection/tracking approach might be more promising in such
situations. We hope to say more about it in the near future.
ACKNOWLEDGMENT
The authors wish to thank Mr. LeRoy Gorham and Mr.
Steven Scarborough, AFRL for sharing the backprojection al-
gorithm for image formation and pointing out various technical
challenges during the course of the work presented here. In
addition, the authors wish to acknowledge the six anonymous
reviewers for pointing out several suggestions for improving
the original manuscript.
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