Modified Space-Time Adaptive Processing for

Dismount Detection using Synthetic Aperture Radar

Ke Yong Li & Faruk Uysal

C & P Technologies, Inc.,

Closter, NJ

kli@cptnj.com & faruk@cptnj.com

S. Unnikrishna Pillai

Polytechnic Inst. of NYU, Brooklyn, NY

and

C & P Technologies, Inc., Closter, NJ

Linda J. Moore

AFRL, Sensor Directorate,

WPAFB, OH

linda.moore2@wpafb.af.mil

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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