ARL-TR-7846 ● OCT 2016

US Army Research Laboratory

Application of Hybrid Along-Track 

Interferometry/Displaced Phase Center 

Antenna Method for Moving Human Target 

Detection in Forest Environments

by DaHan Liao

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ARL-TR-7846 ● OCT 2016

US Army Research Laboratory

Application of Hybrid Along-Track 

Interferometry/Displaced Phase Center 

Antenna Method for Moving Human Target 

Detection in Forest Environments

by DaHan Liao

Sensors and Electron Devices Directorate, ARL

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4. TITLE AND SUBTITLE

Application of Hybrid Along-Track Interferometry/Displaced Phase Center 

Antenna Method for Moving Human Target Detection in Forest Environments

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6. AUTHOR(S)

DaHan Liao

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US Army Research Laboratory

Sensors and Electron Devices Directorate

ATTN: RDRL-SER-U

2800 Powder Mill Road

Adelphi, MD 20783-1138

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ARL-TR-7846

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14. ABSTRACT

Moving human target sensing for airborne foliage-penetration (FOPEN) radar applications is considered. Target detection, 

parameter estimation, and image refocusing—along with clutter suppression—are obtained with a hybrid along-track 

interferometry (ATI)/displaced phase center antenna (DPCA) technique that exploits the responses from a multichannel 

sensing configuration. The processing algorithm is evaluated using the scattering returns from large-scale, full-wave 

electromagnetic simulations of a scene consisting of a walking human embedded in a forest stand. Within the prescribed 

simulation framework, analysis is also performed to examine the effects of the trees and the human gait movement on target 

localization and image quality.

15. SUBJECT TERMS

electromagnetic scattering, tree scattering, foliage penetration radar, target localization, interferometry

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DaHan Liao

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Contents

List of Figures iv

1. Introduction 1

2. Hybrid ATI/DPCA Method 1

3. Numerical Experiments with Full-Wave Simulation Data 2

4. Conclusions 6

5. References 7

List of Symbols, Abbreviations, and Acronyms 8

Distribution List 9
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List of Figures

Fig. 1 Human target walking through a forest stand with 36 randomly 

generated trees .......................................................................................2

Fig. 2 GMTI-SAR images (hh-polarized) from hybrid ATI/DPCA method for 

scene in Fig. 1: a) with trees, moving human with variable pose (as 

constructed from motion-capture walk cycle); b) without trees, moving 

human with variable pose (as constructed from motion-capture walk 

cycle); c) with trees, moving human with constant pose; and d) without 

trees, moving human with constant pose ...............................................3

Fig. 3 GMTI-SAR images (hh-polarized) from conventional DPCA method 

for scene in Fig. 1: a) with trees, moving human with variable pose (as 

constructed from motion-capture walk cycle); b) without trees, moving 

human with variable pose (as constructed from motion-capture walk 

cycle); c) with trees, moving human with constant pose; and d) without 

trees, moving human with constant pose ...............................................5

Fig. 4 GMTI-SAR images (hh-polarized) from conventional ATI for scene in 

Fig. 1: a) with trees, moving human with variable pose (as constructed 

from motion-capture walk cycle); b) without trees, moving human 

with variable pose as constructed from motion-capture walk cycle); c) 

with trees, moving human with constant pose; and d) without trees, 

moving human with constant pose.........................................................6
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1. Introduction

Understandably, as forests cover approximately 30% of the globe’s land surface, 

the detection of moving targets concealed in forest environments is a problem of 

continued interest, especially as pertinent to airborne military surveillance 

applications. Although various clutter-suppression techniques have been developed

in the general area of ground moving target indication (GMTI), including change 

detection,1 along-track interferometry (ATI),2 displaced phase center antenna 

(DPCA),3,4 and space-time adaptive processing, the focus of most existing works 

has been on the detection of surface vehicles (from either spaceborne or airborne 

platforms). The detection of moving humans hidden under tree cover—which poses 

a greater challenge due to the need to discriminate responses that have both very 

weak intensities and very small Doppler components—has been addressed in far 

fewer studies. For example, the sensing of human targets in foliage has been 

considered by Sjögren et al.5

—wherein the signatures of the walking human in 

single-pass, single-channel synthetic aperture radar (SAR) images are examined, 

and by Jao et al.6—wherein adaptive array processing is applied to detect 

dismounts. Given that there has been an increased impetus in attaining SAR 

imaging and GMTI simultaneously, a hybrid ATI/DPCA method is exploited for 

moving human target detection in this work. The technique assumes the availability 

of single-pass data from a multichannel foliage-penetration (FOPEN) radar, and 

enables target velocity parameter estimation and, subsequently, target refocusing in 

the SAR imaging domain. Verification of the processing algorithm is obtained with 

scattering data from a numerical solver that fully characterizes the electromagnetic 

responses of a scene containing realistic tree structures and a walking human.

2. Hybrid ATI/DPCA Method

With the sensing scenario of consideration as depicted in Fig. 1, an airborne 

platform of the following configuration is assumed: the radar has 2 transmitters and 

3 receivers; the receiver array—with interelement separation distance d—is linearly 

arranged in the along-track (or azimuth) direction; furthermore, the 2 transmitters 

are colocated with the first 2 receivers. Over coherent processing time T, the 

platform is assumed to be flying with velocity vp along a linear flight path, and the 

sensing geometry is akin to that of the stripmap mode; and the human target, located 

at reference position (xt, yt) at the midpoint of the coherent processing time, is 

walking with constant velocity vector (vx, vy) along a straight line. The scattering 

data collection proceeds as follows: at each azimuth sampling time tk, the scene is 

illuminated by Transmitter 1, and the scattered fields are captured at Receivers 1 

and 2; then at azimuth sampling time tk + d/vp, the scene is illuminated by 
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Transmitter 2 and the scattered fields are captured at Receivers 2 and 3. In essence, 

4 sets of scattered fields are generated, viz., c11, c21, c22, and c32, where in each case, 

the first subscript index denotes the receiver number and the second denotes the 

transmitter number. For DPCA processing, the operation c11 − c22 is performed, the

result of which can be estimated as a linear FM signal. The parameters of this linear 

FM signal—which are defined by the unknown target velocity and reference 

position vectors—are determined using a fractional Fourier transform procedure.

7

The target reference position vector itself is deduced with the phase of the output 

from a clutter-suppressed ATI operation of the form (c11 − c22)(c21 – c32)*. Once the 

linear FM parameters and reference position vector are found, the target velocity 

vector (vx, vy) can be calculated. Subsequently, having estimated the target 

parameters, phase compensation on the clutter-suppressed signal c11 − c22 can be 

carried out to de-blur the target response—as well as to relocate it to its correct 

position—in the imaging domain.

Fig. 1 Human target walking through a forest stand with 36 randomly generated trees

3. Numerical Experiments with Full-Wave Simulation Data

The scattering responses of the scene in Fig. 1 are obtained with the full-wave 

solver presented by Liao and Dogaru8,9; specifically, the large-scale simulation 

framework employs a parallelized finite-difference time-domain algorithm for 

deriving the far-field responses of the scene over the 300–400 MHz band. The 

forest stand is composed of 36 randomly generated trees situated directly over a 

flat, finitely conducting ground. A human target is assumed to be moving across 

the scene, with poses for its walk cycle synthesized from motion-capture data.

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Essentially, frame-by-frame simulation of the scene is performed over the coherent 

processing time to capture the effects of both the displacement and the gait 

movement of the target. The overall dimensions of the computational domain are 

approximately 29 m × 29 m × 9 m. The trees, ground, and human model are 

assumed to be dielectrically homogeneous with relative dielectric constant and 

conductivity (εr, σd) of (13.9, 39 mS/m), (5.45, 20 mS/m), and (48.8, 662 mS/m), 

respectively.
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The hh-polarized imaging results from the hybrid ATI/DPCA method discussed in 

the previous section are displayed in Fig. 2. For this sensing scenario, vp = 100 m/s, 

(vx, vy) = (1.0 m/s, 0.75 m/s), and T = 16.9 s; the along-track direction is taken to 

be parallel to the x-axis; the radar is pointed toward yˆ with a depression angle of 

15°; and the average slant range to the target is approximately 5.8 km.

Fig. 2 GMTI-SAR images (hh-polarized) from hybrid ATI/DPCA method for scene in Fig. 

1: a) with trees, moving human with variable pose (as constructed from motion-capture walk 

cycle); b) without trees, moving human with variable pose (as constructed from motioncapture walk cycle); c) with trees, moving human with constant pose; and d) without trees, 

moving human with constant pose

It is seen from Fig. 2a that the target is detected and localized (the path of the target 

is indicated with the white arrow in the images); however, there is also a significant 

amount of imaging artifact spreading in the cross-range direction. The main cause 

of this smearing does not stem from the presence of the trees: as evident from Fig.

2b, for the same sensing setup, similar defocusing appears in the image for the scene 

without the trees and with only the walking human. It is surmised that the 

manifestation of the artifact arises from the fact that the processing algorithm here 

presupposes a simple target moving with a single velocity vector, whereas in 

actuality, the walking human is a fluctuating complex target, with many body 

components moving with different velocity vectors. To verify this conjecture, the 

simulation scenario is reconsidered using a human target with a constant pose—
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that is, from one frame to the next, the target is simply displaced along its walking 

path, without its gait movement. The results in Figs. 2c and 2d confirm the moving 

target, whether it is in the open or in the trees, can be well focused without the 

smearing effect if the movements of individual body parts are excluded. At least in 

this example and for the hh response, although the multiscattering interactions of 

the trees and the target do introduce some additional image degradation, those 

effects seem to play a less significant role than the fluctuating nature of the 

composite target.

For comparison purposes—and for the sake of completeness—imaging results 

derived from conventional DPCA and ATI techniques are also included here, in 

Figs. 3 and 4. It is evident that the target is detected in all cases, and the clutter 

response is suppressed—when the simulated scene does contain the trees. In 

general, for a target in motion, its DPCA or ATI imaging response is displaced from 

the actual target location; in these examples, this offset distance along the crossrange direction is estimated as

p

y

offset o v

v

x = −R

 

where Ro is the broadside slant range of the target. Of course, the theoretical result, 

previously mentioned, does not include the effects of the gait movement. Other than 

spreading the response in the cross-range direction, the gait movement can also 

introduce an additional radial velocity component that can cause the main image of 

the target to be displaced by an additional amount. This can be readily observed, 

for instance, by comparing Figs. 3b and 3d.
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Fig. 3 GMTI-SAR images (hh-polarized) from conventional DPCA method for scene in 

Fig. 1: a) with trees, moving human with variable pose (as constructed from motion-capture 

walk cycle); b) without trees, moving human with variable pose (as constructed from motioncapture walk cycle); c) with trees, moving human with constant pose; and d) without trees, 

moving human with constant pose
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Fig. 4 GMTI-SAR images (hh-polarized) from conventional ATI for scene in Fig. 1: a) with 

trees, moving human with variable pose (as constructed from motion-capture walk cycle);

b) without trees, moving human with variable pose as constructed from motion-capture walk 

cycle); c) with trees, moving human with constant pose; and d) without trees, moving human 

with constant pose

4. Conclusions

A hybrid ATI/DPCA method is investigated for moving human target detection, 

parameter estimation, and localization, as relevant to airborne FOPEN sensing. The 

processing algorithm is demonstrated with scattering data from an electromagnetic 

solver that fully characterizes the responses of a scene consisting of a moving 

human target in a forest stand. Numerical experiments indicate that the gait 

movement of the target can lead to image defocusing and has to be included in order 

to completely understand the imaging response.
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5. References

1. Ulander LM, Flood B, Frölind P-O, Gustavsson A, Jonsson T, Larsson B, 

Lundberg M, Murdin D, Stenström G. Change detection of vehicle-sized 

targets in forest concealment using VHF- and UHF-band SAR. IEEE Aerosp 

Electron Syst Mag, 2011.

2. Kapfer R, Davis ME. Along track interferometry for foliage penetration 

moving target indication. Proc. IEEE Radar Conf., 2008. Rome, Italy.

3. Sun H, Zhang L, Jin X. Parameter estimations based on DPCA-FrFT algorithm 

for three-channel SAR-GMTI system. Proc. Intelligent Comput. Tech. 

Automat. Conf., 2011.

4. Wang W-Q, Shao H. Two-antenna SAR with waveform diversity for ground 

moving target indication. IEEE. Geosci Remote Sens Lett, 2011.

5. Sjögren TK, Ulander LMH, Frölind P-O, Gustavsson A, Stenström G, Jonsson

T. Detection of moving humans in UHF wideband SAR. Proc. SPIE. 2014.

Baltimore, MD.

6. Jao JK, Bessette LA, Evans PR. Adaptive GMTI processing of FORESTER 

field test data and performance results. Proc. 54th Tri-Services Radar 

Symposium. 2004. Monterey, June 2008.

7. Narayanan VA, Prabhu KMM. The fractional Fourier transform: theory, 

implementation and error analysis. Microprocessors and Microsystems. 2003.

8. Liao DH, Dogaru T. Large-scale, full-wave scattering phenomenology 

characterization of realistic trees. Proc. IEEE Antennas Propagat. Soc. Symp.

2015. Vancouver, Canada.

9. Liao DH, Dogaru T. Full-wave scattering and imaging characterization of 

realistic trees for FOPEN sensing. IEEE. Geosci Remote Sens Lett. 2016 

Jul;13(7):957–961.

10. CMU, Graphics Lab Motion Capture Database, http://mocap.cs.cmu.edu/;

[accessed 2015 Sep].
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List of Symbols, Abbreviations, and Acronyms

ATI along-track interferometry

DPCA displaced phase center antenna

FM frequency modulation

FOPEN foliage-penetration (radar)

GMTI ground moving target indication

SAR synthetic aperture radar
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