Through-the-Wall Small Weapon Detection Based on 

Polarimetric Radar Techniques

by Traian Dogaru and Calvin Le

ARL-TR-5041 December 2009

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Army Research Laboratory

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ARL-TR-5041 December 2009

Through-the-Wall Small Weapon Detection Based on 

Polarimetric Radar Techniques

Traian Dogaru and Calvin Le

Sensors and Electron Devices Directorate, ARL

Approved for public release; distribution unlimited. 
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Through-the-Wall Small Weapon Detection Based on Polarimetric Radar

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14. ABSTRACT

In this report, we investigate techniques for small weapon detection in the context of sensing-through-walls radar sensors. Our 

approach exploits the radar wave polarization in order to discriminate between a human carrying a weapon and an unarmed 

human. We first develop a polarization optimization procedure for a rifle-like target, but demonstrate that this technique does 

not perform as expected in the presence of a human. Next, we introduce a more robust discrimination procedure based on the 

ratio of the cross- to co-polarization radar signature. We show that this technique produces a measurable enhancement (of at 

least 8 dB) when the human carries a weapon versus the case of an unarmed human. We support this idea with numerical 

examples that include radar cross section evaluations, synthetic aperture radar images of humans in rooms of various 

complexities, and the Doppler spectrum of a moving human.

15. SUBJECT TERMS

Radar, concealed weapons

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Traian Dogaru

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iii

Contents

List of Figures iv

List of Tables v

Acknowledgments vi

1. Introduction 1

2. Polarization Dependent Optimization of the Radar Signature 2

3. Using the Cross-Polarization Signature for Target Discrimination 8

3.1 Basic Approach ...............................................................................................................8

3.2 Images of a Human Carrying a Rifle in Free Space........................................................9

3.3 Images of a Human Carrying a Rifle Behind Walls......................................................13

3.4 Rifle Detection Algorithm Based on SAR Images of Rooms.......................................20

3.5 Doppler Spectrum of a Walking Human Carrying a Rifle............................................23

3.6 Human Carrying a Rocket Propelled Grenade ..............................................................26

4. Conclusions 32

5. References 34

List of Symbols Abbreviations, and Acronyms 36

Distribution List 37
iv

List of Figures

Figure 1. A simple cylindrical rod tilted at angle α with respect to vertical, showing the 

electric field unit vectors in two different coordinate systems. ................................................3

Figure 2. ...Estimating the tilt angle of a cylindrical metallic rod of 1 m length and 6 cm diameter. 

(a) View of the FDTD mesh with 2-mm resolution and (b) the tilt angle estimation 

results. ........................................................................................................................................5

Figure 3. Different views of the AK-47 FDTD mesh with 2-mm resolution (the tick marks on 

the axes represent numbers of FDTD cells)...............................................................................5

Figure 4. Estimating the tilt angle of the AK-47 rifle using the procedure described in 

section 2. ....................................................................................................................................6

Figure 5. RCS of the AK-47 versus tilt angle showing the improvement obtained by the 

polarization optimization procedure described in section 2. .....................................................7

Figure 6. Attempting to estimate the tilt angle of the rifle carried by a human by the 

procedure described in section 2................................................................................................8

Figure 7. RCS of the standing human with and without a rifle versus azimuth angle showing: 

(a) the computational mesh, (b) RCS for V-V polarization, (c) RCS for V-H polarization, 

and (d) V-H to V-V RCS ratio.................................................................................................10

Figure 8. Co-polarization SAR images of a human with and without a rifle showing: 

(a) human without rifle, V-V polarization; (b) human with vertical rifle, V-V 

polarization; (c) human with rifle tilted at 45°, V-V polarization; and (d) human with 

horizontal rifle, H-H polarization. ...........................................................................................11

Figure 9. Cross-polarization (V-H) SAR images of a human with and without a rifle 

showing: (a) human without rifle, (b) human with vertical rifle, (c) human with rifle tilted 

at 45°, and (d) human with horizontal rifle..............................................................................12

Figure 10. Top view of the simple brick wall room showing a human carrying an AK-47, 

with the radar moving along a circular arc in the far-field, on the left side in the figure. .....14

Figure 11. Co-polarization (V-V) SAR images of a human placed in the middle of a brick 

wall room showing (a) human unarmed and (b) human with AK-47.....................................14

Figure 12. Cross-polarization (V-H) SAR images of a human placed in the middle of a brick 

wall room showing (a) human unarmed and (b) human with AK-47.....................................15

Figure 13. The complex room containing humans and furniture objects showing 

(a) perspective view (humans carrying rifles) and (b) top view (humans unarmed). .............16

Figure 14. Co-polarization (V-V) SAR images of the complex room in figure 13 showing: 

(a) aperture on left side, humans without rifles; (b) aperture on left side, humans with 

rifles; (c) aperture on bottom side, humans without rifles; and (d) aperture on bottom side, 

humans with rifles....................................................................................................................18
v

Figure 15. Cross-polarization (V-H) SAR images of the complex room in figure 13 showing: 

(a) aperture on left side, humans without rifles; (b) aperture on left side, humans with 

rifles; (c) aperture on bottom side, humans without rifles; and (d) aperture on bottom side, 

humans with rifles....................................................................................................................19

Figure 16. Normalized probability distribution functions of the pixel-by-pixel cross- to copolarization ratio in the complex room SAR images obtained from the left side, showing 

(a) the entire distribution and (b) detail of the distributions’ tails. ..........................................21

Figure 17. Detection maps for the complex room in figure 13 based on thresholding the

cross- to co-polarization ratio, for the cases where (a) the humans carry no rifles and 

(b) the humans carry AK-47 rifles...........................................................................................22

Figure 18. Detection map for the complex room in figure 13 containing humans with rifles, 

obtained after applying a pixel averaging procedure to the images in figures 14a and b 

and 15a and b. ..........................................................................................................................23

Figure 19. Human body mesh in walking motion showing (a) free walking motion and 

(b) walking with an AK-47......................................................................................................24

Figure 20. Doppler spectrum of a human walking with and without a rifle showing: (a) V-V 

polarization, 0° azimuth; (b) V-H polarization, 0° azimuth; (c) V-H to V-V ratio, 0° 

azimuth; and (d) V-H to V-V ratio, 30° azimuth....................................................................26

Figure 21. Different views of the RPG-7 mesh with 2-mm resolution (the tick marks on the 

axes represent numbers of FDTD cells)...................................................................................27

Figure 22. RCS of the standing human with and without an RPG versus azimuth angle 

showing: (a) the computational mesh; (b) RCS for V-V polarization; (c) RCS for V-H

polarization; and (d) V-H to V-V RCS ratio............................................................................28

Figure 23. Co-polarization SAR images of a human with and without an RPG showing: 

(a) human without RPG, V-V polarization; (b) human with vertical RPG, V-V 

polarization; (c) human with RPG tilted at 45°, V-V polarization; and (d) human with 

vertical RPG, H-H polarization................................................................................................30

Figure 24. Cross-polarization (V-H) SAR images of a human with and without an RPG 

showing: (a) human without RPG; (b) human with vertical RPG; and (c) human with 

RPG tilted at 45°......................................................................................................................31

Figure 25. Diagram illustrating the typical STTW target hierarchy in terms of interest for 

military operations, together with the strengths of their co- and cross-polarization radar 

signatures. ................................................................................................................................33

List of Tables

Table 1. Comparison of maximum pixel intensity in the human areas within the SAR images 

presented in sections 3.2 and 3.3, in co- and cross-polarization, for the cases with and 

without rifle (all values in dB).................................................................................................20
vi

Acknowledgments

This study was partially funded by the Communications-Electronics Research Development and 

Engineering Center (CERDEC), Intelligence and Information Warfare Directorate (I2WD) at Ft. 

Monmouth, NJ. We would like to acknowledge our U.S. Army Research Laboratory (ARL)

colleagues, Kenneth Ranney and Anders Sullivan, for their advice regarding various parts of this 

report, and Getachew Kirose for his contribution to the computer modeling of the moving 

human.

.
1

1. Introduction

The U.S. Army Research Laboratory (ARL) has been recently involved in several programs 

related to sensing-through-the-wall (STTW) radar technology. A significant part of our efforts 

consisted of computer models of various scenarios relevant to this technology, with the purpose 

of predicting performance and analyzing the radar system parameter trade-off. Some of our 

previous studies included human radar signature analysis (1), modeling of radar imaging systems 

for building mapping (2), validating radar signature computational codes for STTW applications 

(3, 4), and Doppler radar signature analysis of a moving human (5). This report tackles another 

problem of great interest in the context of radar sensors for concealed environments—throughthe-wall detection of small weapons carried by humans.

Detecting concealed small weapons carried by people has received significant interest from law 

enforcement agencies as well as the military, most frequently for application in controlling

checkpoints (in airports, border crossing, public spaces, etc.). Imaging systems for concealed 

weapons based on radar or other sensor technologies have been recently developed and tested. 

Most of the existing electromagnetic (EM) sensors suitable for this application operate at very 

high frequencies, usually in the millimeter or terahertz frequency bands and produce highresolution images (6–8). Although these EM waves can penetrate through clothing (textile 

materials), they have very poor penetration properties through many common construction 

materials (such as brick or concrete). Most through-the-wall radars must operate at much lower 

frequencies, usually below 4 GHz (9), in order to “see” targets behind walls. However, at those 

low frequencies, the image resolution is degraded, so small weapons carried by humans may be 

difficult to detect directly in the image domain.

In this study, we propose an approach based on polarimetric radar techniques for through-thewall detection of certain weapons, such as rifles or rocket propelled grenade (RPG) launchers. In 

order to make these techniques effective, the targets must exhibit a large shape factor (the target 

must extend in one dimension much more than in the other two) and be made mostly of metal. 

The radar signature of these targets is very sensitive to wave polarization (more exactly, to the 

orientation of the electric field vector in the plane perpendicular to the direction of propagation) 

and we try to exploit that in the STTW imaging radar system. An additional requirement for our 

detection problem is being able to find a weapon placed close to a human body—in other words, 

we want to be able to discriminate a human carrying a weapon from one without a weapon. This 

presents an additional challenge to the system, since generally the human signature is 

significantly larger than that of a rifle or RPG. We would also like to be able to detect multiple 

weapon carriers within a scene, with the targets placed in arbitrary orientations.
2

In section 2, we introduce an approach based on optimizing the radar polarization for maximum

target signature. We demonstrate this idea on an isolated rifle placed in free space. At the same 

time, we show that this technique breaks down in the more complicated scenario where the rifle 

is carried by a human. Section 3 examines a more robust approach, based on comparing the 

cross- and co-polarization responses from targets. This method has a much better potential to 

allow discrimination between humans with and without weapons. We also show how this 

technique can be applied to various STTW scenarios (imaging or Doppler radar), for various 

targets in arbitrary orientations. All the numerical examples presented in this study are based on 

computer simulations. We draw conclusions in section 4.

2. Polarization Dependent Optimization of the Radar Signature

In this section, we investigate a procedure that optimizes the radar polarization in order to 

maximize the weapon signature. We consider the case of an AK-47 rifle. It is well known that an 

object with a rod-like geometry has the largest radar response when the electric field vector of 

the radar wave is aligned with the long axis of the target. In the case when the target orientation 

is unknown, the fully polarimetric scattering matrix (based on two orthogonal polarizations, 

usually vertical and horizontal) contains all the information necessary to find the optimal 

orientation of the polarization (or electric field) vector. Thus, by performing the required 

transformation on the scattering matrix (which consists of a rotation of the polarization vector in 

a plane perpendicular to the direction of propagation), we can enhance the response of a target 

with certain geometric characteristics that are polarization-sensitive.

Consider a metallic rod such as that shown in figure 1. The rod is placed in a plane perpendicular 

to the direction of propagation and tilted at angle α. We also assume that the radar is in the farfield and looks at 0° elevation (the propagation vector is in the x-y plane). The plane 

perpendicular to the propagation direction is defined by two orthogonal unit vectors, uv (vertical) 

and uh (horizontal). For instance, if the propagation direction is aligned with the x-axis, then uh is 

along the y-axis and uv along the z-axis. For a plane wave, the electric field vector E is contained 

in the plane defined by uv and uh, with components Ev and Eh. 





 



 = + =

h

v

v h E

E

E E uv E uh (1)

where 









 = 0

1

uv and 









 = 1

0

uh (2)
3

Figure 1. A simple cylindrical rod tilted at angle α with 

respect to vertical, showing the electric field unit 

vectors in two different coordinate systems.

In the most general case, Ev and Eh are complex numbers (elliptical polarization). However, for 

linear polarization, the Ev to Eh ratio is real. For this geometry, the scattering matrix S relates the 

incident and scattered electric field vectors Ei

and Es by the following equation:

s i E SE

r

e jk r − 0

= (3)

where 



 



 =

hv hh

vv vh

S S

S S

S (4)

It can be shown that the target’s backscatter signature is maximized when the electric field vector 

(both at the transmitter and at the receiver) is aligned with the eigenvector corresponding to the 

maximum eigenvalue (in magnitude) of the matrix S*S (11, 12). For a target with a rod-like 

geometry, we expect the polarization direction that maximizes the signature to coincide with its 

long axis, which forms an angle α with the vertical direction (figure 1). We also expect the 

components of the corresponding eigenvector to be real (linear polarization). Assuming that S*S

has distinct eigenvalues, let u1 and u2 the two normalized eigenvectors (u1 corresponding to the 

maximum eigenvalue). Then, we can write





 



 = + = α

α

α α

sin

cos

cos sin u1 uv uh (5)

uv

u1

uh

u2

α
4





 



− = − + = α

α

α α

cos

sin

sin cos u2 uv uh (6)

The unit vectors u1 and u2 form a new orthogonal basis in the E vector plane that is obtained 

from the original uv and uh basis by a rotation of angle α. The transformation (rotation) matrix is





 



 − = α α

α α

sin cos

cos sin

R (7)

Under this transformation, the scattering matrix becomes





 



 = = opt opt

opt opt

opt T

S S

S S

21 22

* 11 12 S R SR (8)

It can be shown (12) that the new scattering matrix is diagonal and that opt S11 is the square root of 

the maximum eigenvalue of matrix S*S. This element can be used in further processing of the 

radar data, yielding the maximum radar response of the target within the entire polarization 

space. Clearly, in order to compute opt S11 , we need to first obtain all four elements of the 

polarimetric matrix (through either modeling or measurements).

As a first verification of this approach, we apply it to a cylindrical metallic rod of 1 m length and 

6 cm diameter (figure 2a). In particular, we look at the following problem: given the scattering 

data from the rod placed in an unknown orientation, can we estimate the tilt angle (in the plane 

perpendicular to the radar line of sight)? Our approach consists of computing the fully 

polarimetric radar response of the rod at various angles between 0° and 90° (measured from 

vertical) and trying to infer the tilt angle α from the two components of the u1 vector by the 

procedure outlined above. All our simulations in this report use the AFDTD electromagnetic 

modeling code, which was entirely developed at ARL for radar signature calculations. This code 

is based on the Finite-Difference Time-Domain (FDTD) method (a comprehensive introduction 

of this technique can be found in reference 10). The simulations are run in a frequency band 

extending from 0.5 to 2.5 GHz. The estimated angle α should theoretically be independent of 

frequency. However, in order to improve the reliability of this procedure, we average the 

estimated angle over the entire frequency band. The tilt angle estimation results are shown in 

figure 2b, together with the real tilt angles. In this case, the estimation procedure works perfectly.

In the next step, we apply the same procedure to simulation data obtained on an AK-47 rifle 

placed in free space. Representations of the AK-47 computer meshes are shown in figure 3, 

where the metallic parts are painted brown and the wooden parts are painted yellow (the wooden 

parts are shown as transparent in order to evidence the internal structure including the continuous 

metallic barrel). Unless specified otherwise, the mesh has a resolution (cell size) of 2 mm. The 

wood dielectric constant is εr = 2. The simulations are run in the same frequency band as in the 

previous example. The estimated tilt angles (together with the real tilt angles) are shown in 
5

figure 4. We notice that in this case the procedure produces a small error (generally less than 5°), 

but the estimate is still fairly accurate. We can probably attribute the error to the more complex 

shape of the AK-47 (especially the bullet magazine, which is oriented almost perpendicularly to 

the barrel).

Figure 2. Estimating the tilt angle of a cylindrical metallic rod of 1 m length and 6 cm diameter. (a) View of the 

FDTD mesh with 2-mm resolution and (b) the tilt angle estimation results.

Figure 3. Different views of the AK-47 FDTD mesh with 2-mm resolution (the tick marks on the axes 

represent numbers of FDTD cells).
6

Figure 4. Estimating the tilt angle of the AK-47 rifle using the procedure described 

in section 2.

As a further step, we look at the enhancement in the radar cross section (RCS) when we use the 

opt S11 element instead of the vertical-vertical (V-V) or horizontal-horizontal (H-H) elements of the 

scattering matrix (figure 5). Obviously, the RCS improvement is dependent of the rifle tilt angle: 

at 0° tilt (vertical position), the V-V polarization yields the maximum response; whereas, at 90° 

tilt (horizontal position), the H-H polarization yields the maximum response. As expected, using 

the opt S11 element produces the same RCS no matter the tilt angle. The largest RCS improvement 

is noticed at 45° tilt and is about 4 dB. Again, the results were averaged over the 0.5 to 2.5 GHz 

band.

Although this polarization-dependent optimization of the radar signature seems to work well for 

a rifle-like object in free space, adding other targets with large signature in the vicinity of the 

object of interest can dramatically change the polarization dependence of its radar response. In 

particular, if the rifle is carried by a human (whose radar signature is largely polarization 

independent), the procedure described previously does not enhance the radar signature. The 

reason for this is that the rifle return is mostly obscured by the much larger human body 

signature. To illustrate this, we tried to estimate the rifle tilt angle in the presence of a human 

body, similarly to the procedure described in figure 4. The new results are shown in figure 6 and 

they indicate that our signature optimization attempt failed in this case. In general, we expect this 

procedure to be very sensitive to clutter (such as that produced by a human body), which renders 

it mostly useless in a real-life weapon detection scenario. Moreover, if the scenario involves two 
7

or more rifles oriented at different tilt angles with respect to the radar line of sight, the 

polarization-dependent optimization procedure cannot work either. Section 3 will describe a 

more robust approach for the weapon detection problem, also based on polarimetric techniques.

Note: Throughout this report, we use the human body model of the “fit man,” which was 

described in reference 1. This is a realistic computer mesh, where we modeled the body as a 

uniform dielectric with εr = 50 and σ = 1 S/m (see reference 1 for more details on the validity of 

this model).

Figure 5. RCS of the AK-47 versus tilt angle showing the improvement obtained 

by the polarization optimization procedure described in section 2.
8

Figure 6. Attempting to estimate the tilt angle of the rifle carried by a human by

the procedure described in section 2.

3. Using the Cross-Polarization Signature for Target Discrimination

3.1 Basic Approach

The approach introduced in this section is based on the observation that a rod-like object oriented 

at a tilt angle from the vertical and horizontal axes has a relatively strong cross-polarization 

response. Consider an idealized such object placed in vertical position. To simplify the 

formulation, we assume that the scattering matrix in this case has only one non-zero element, Svv. 

Now consider that we rotate the object by an angle α, which is equivalent to rotating the 

coordinate system (in the E vector plane) by an angle –α. With the rotation matrix R given by 

equation 7, the scattering matrix can be written in the new (rotated) coordinate system as





 



 =



 





− 



 









 



 − = =

vv

tilt T vv vv

S

S S

cos sin S sin

cos cos sin S

sin cos

cos sin

0 0

0

sin cos

cos sin

2

vv

vv

2

*

α α α

α α α

α α

α α

α α

α α

S R SR (9)

Thus, the target exhibits a sizeable cross-polarization response relative to the new coordinates in 

the E vector plane. It is easy to see that the cross-polarization elements reach a maximum when 

α = 45°. Also, in the new coordinate system, the ratio of the cross- to co-polarization elements 
9

(for instance, tilt Svh and tilt Svv ) is tanα. This is in contrast with targets that do not exhibit a 

preferential axis, these targets generally have a low cross- to co-polarization ratio that is not 

sensitive to a rotation in the E vector plane.

We illustrate this idea by computing the RCS of a standing human carrying an AK-47 rifle in the 

port-arm position (figure 7a). In this case, the rifle is tilted at 45°, and the radar elevation angle is 

0° (propagation in the x-y plane). The values displayed in figures 7b through 7d represent RCS 

averaged over a 400-MHz frequency band centered at 1 GHz (the averaging procedure basically 

eliminates some deep nulls in the RCS curves that would appear at specific frequencies and 

aspect angles). The RCS of the human with and without rifle for V-V polarization is shown in 

figure 7b, while figure 7c looks at the RCS for vertical-horizontal (V-H) polarization. From these 

graphs we infer that the cross-polarization response of the tilted rifle is much stronger than that 

of the human body. At the same time, the co-polarization return from the human body is not 

significantly altered by the presence of the rifle. In order to discriminate between the two 

scenarios, one would take the ratio (difference in dB) between the cross- and co-polarization 

signatures and compare that to a threshold (established through some calibration procedure). 

Although it is obvious that at 0° azimuth (when the human faces the radar) the cross- to copolarization ratio is much larger for the human carrying the rifle (since the human himself is 

mostly symmetric at this aspect angle, making its cross-polarization signature very low), the 

difference between the ratios in the two scenarios is still significant for most other aspect angles 

(figure 7d). Notice that, for azimuth angles beyond –90° or 90°, there is not much difference 

between the two scenarios, because for those angles the rifle is masked by the body. 

Interestingly, as shown in section 3.2, we found out that the strong cross-polarization signature 

of the rifle holds for virtually any tilt angle.

3.2 Images of a Human Carrying a Rifle in Free Space

In this section, we take the analysis one step further by creating synthetic aperture radar (SAR) 

images of the human with and without a rifle in free space. The SAR images are based on 

integrating FDTD simulated radar range profiles over a large range of frequencies (2-GHz 

bandwidth centered at 2.5 GHz) and azimuth angles (30° aperture centered at broadside). The 

scattering models are performed in the far-field (at 0° elevation) and the polar format algorithm 

(13) is employed in image formation. The images represent two-dimensional pseudo-color maps 

with downrange on the x-axis and cross-range on the y-axis. In all images, the human faces left, 

while the radar looks from the left side. The intensity scales are always in dB. More details on 

this procedure can be found in reference 2.
10

Figure 7. RCS of the standing human with and without a rifle versus azimuth angle showing: (a) the computational 

mesh, (b) RCS for V-V polarization, (c) RCS for V-H polarization, and (d) V-H to V-V RCS ratio.

We consider four cases: the man without a rifle, and the man carrying a rifle at 0°, 45°, and 90° 

tilt angles. Figure 8 shows the SAR images obtained for these cases for V-V polarization (H-H 

polarization for the 90° tilt), whereas figure 9 shows the images obtained for V-H polarization. 

The intensity of the brightest pixel in every image is marked in the figures. Based on these 

results, we notice that the cross- to co-polarization ratio of the brightest pixel is –23 dB for the 

human without rifle and between –13 to –15 dB for the human with rifle. Therefore, there is a 

gain of at least 8 dB in this ratio when the rifle is present, which can be exploited in a weapon 

detection scheme. The reason why the rifle still exhibits a relatively strong cross-polarization 

response at 0° and 90° tilt is not entirely clear to us at the time of this investigation. It is possible 

that, in these cases, the bullet magazine is mainly responsible for the cross-polarization signature.

(a) (b)

(c) (d)
11

Figure 8. Co-polarization SAR images of a human with and without a rifle showing: (a) human without rifle, V-V 

polarization; (b) human with vertical rifle, V-V polarization; (c) human with rifle tilted at 45°, V-V 

polarization; and (d) human with horizontal rifle, H-H polarization.

(a) (b)

(c) (d)

Max -17 dB

Max -21 dB

Max -22 dB Max -21dB
12

Figure 9. Cross-polarization (V-H) SAR images of a human with and without a rifle showing: (a) human without 

rifle, (b) human with vertical rifle, (c) human with rifle tilted at 45°, and (d) human with horizontal rifle.

(a) (b)

(c) (d)

Max -40 dB Max -35 dB

Max -35 dB Max -36 dB
13

3.3 Images of a Human Carrying a Rifle Behind Walls

The purpose of this study is to investigate weapon detection techniques for concealed 

environments, in particular, for through-the-wall radar sensors. In this section, we place the 

human and rifle targets in a room or a building and apply the ideas presented previously to detect 

the presence of weapons. It is interesting to mention that, as shown in reference 2, the walls are 

mostly absent from the cross-polarization image. This makes the cross-polarization mode well

suited in rejecting the walls, while emphasizing the human targets and possible weapons.

The room imaging techniques used in this section are the same as those described in reference 2.

The scattering data is based on computer modeling of the radar return. We use far-field 

geometry, with an elevation angle of 0°. The entire room is placed in free space, meaning there is 

no infinite ground plane underneath the walls. However, we add a flat ceiling and a floor with 

the exact same extent as the room perimeter. The polar format algorithm (13) is used in the 

image formation process. As in section 3.2, the images represent two-dimensional pseudo-color 

maps with downrange on the x-axis and cross-range on the y-axis (the radar looks from the left 

side). The mesh contours are overlaid onto the SAR images in gray to help locate the objects in 

the scene.

The radar return computations were performed with the AFDTD code. All the meshes in this 

section have a resolution of 5 mm (discussions on the model accuracy can be found in reference 

4). We consider two room sizes: one has dimensions 5 m by 3.5 m by 2.2 m (197 in by 138 in by 

87 in), while the other has dimensions 10 m by 7 m by 2.2 m (197 in by 138 in by 87 in). These 

represent very large meshes that can only be handled by large computer clusters running a 

parallel version of the AFDTD code. Thus, the larger of the rooms includes 1.68 billion cells and 

the simulation at one incidence angle requires 128 processors. Overall, obtaining one image of 

the large room took about 500,000 central processing unit (CPU) hours. All the simulations were 

performed at the U.S. Army Major Shared Resource Center (MSRC) (14) on Linux Networx 

Evolocity II clusters. The images were created with the Pioneer RCS software on desktop 

personal computers running Windows XP.

First, we consider the simple (smaller) room that is represented in figure 10. The walls are 8 in 

thick, made of brick, with εr = 3.8 and σ = 0.03 S/m (2). The ceiling and floor are represented as 

6-in-thick concrete slabs, with εr = 6.8, σ = 0.1 S/m. The fit man is placed in the middle of the 

room. The AK-47 rifle is carried at a tilt angle of 45°. The SAR images involve an effective 

bandwidth of 2 GHz centered at 2.5 GHz and an effective aperture of 30° centered at broadside 

(we use Hanning windows in both the frequency and angular domain). The images obtained for 

this room for V-V polarization are shown in figure 11: (a) human without rifle and (b) human 

with rifle. The differences between the two images are very small (the brightest pixels in various 

image areas are marked in the figure). However, if we look at the images obtained for V-H 

polarization (figure 12), we notice that the human carrying a rifle (b) is much brighter than that 

without a rifle (a). (Note: The second image that appears behind the correct position of the 
14

human is an artifact called “ghost image” and was explained in reference 2.) The cross- to copolarization ratio for the brightest pixel in the human image is –24 dB for the case without rifle 

and –16 dB for the case with rifle. Again, we find a difference of 8 dB between the two cases.

Figure 10. Top view of the simple brick wall room showing a 

human carrying an AK-47, with the radar moving

along a circular arc in the far-field, on the left side 

in the figure.

(a) (b)

Figure 11. Co-polarization (V-V) SAR images of a human placed in the middle of a brick wall room showing 

(a) human unarmed and (b) human with AK-47.

Max -30 dB Max -33 dB
15

(a) (b)

Figure 12. Cross-polarization (V-H) SAR images of a human placed in the middle of a brick wall room showing 

(a) human unarmed and (b) human with AK-47.

Next, we create the SAR images of the complex room represented in figure 13. The exterior 

walls are made of 8-in brick and are equipped with glass windows and a wooden door. There is 

also an interior wall made of 2-in-thick sheetrock (equipped with a door as shown in the figure). 

There are four humans in this mesh, placed at different azimuth orientation angles and tagged 

with labels 1 through 4, as in figure 13b. We added the following furniture objects: a bed, a 

couch, a bookshelf, a dresser, and a table with four chairs. These objects are made primarily from 

wood (except for the mattress and cushions where we used some generic fabric material). We 

also included 2-in-thick concrete slabs as ceiling and floor. The rifles (when present) are tilted at 

45°. All the objects included in this mesh were obtained by conversion from realistic computer 

aided design (CAD) models of the respective objects. Details on the dielectric properties of the 

materials used in the mesh can be found in reference 2. The SAR images are created using an 

effective bandwidth of 1.1 GHz centered at 1.4 GHz and an aperture of 30° centered at broadside 

on two sides of the building.

As mentioned in section 3.1, the strong contribution of the rifle to the cross-polarization 

signature of the human is evident only when the radar can “view” the rifle directly (the rifle is 

not masked by the human body). In order to achieve good aspect angle diversity, we image the 

room from two sides (left and bottom), meaning that we integrate separate apertures centered at 

broadside on two sides of the building. For the purpose of illustrating the discrimination 

technique, we placed the rifles on humans in positions that are favorable to the imaging scenario, 

such that the radar has direct line of sight to each rifle from either side of the building. If the 

Max -54 dB (a_ Max -49 dB (b)
16

radar could not see the rifle directly, then the discrimination procedure would not work. The 

angles between the humans’ boresight directions and the x-axis are as following: (1) 45°, (2) 0°, 

(3) 10°, and (4) –20°.

Figure 13. The complex room containing humans and furniture objects showing (a) perspective 

view (humans carrying rifles) and (b) top view (humans unarmed).

(a)

(b)

x

1

2

3

4

y
17

The images of the complex room are shown in figures 14 and 15. We present the images taken 

from the left and bottom sides separately. A top view of the mesh is overlaid on top of the 

images (in gray contours) in order to help with image interpretation. The images in figure 14 

show the V-V polarization as follows: (a) no rifles, radar on left side of the building; (b) with 

rifles, radar on left side of the building; (c) no rifles, radar on bottom side of the building; and 

(d) with rifles, radar on bottom side of the building. In figure 15, we show the same images 

obtained for V-H polarization. The maximum pixel intensities in the human areas are marked in 

the figures. Qualitatively, one can notice an increase in brightness in the cross-polarization 

images when the rifles are present, with increases in the cross- to co-polarization intensity ratios 

consistent with the previous results. The enhancement in the cross-polarization images is more 

evident when the aperture is placed on the left side of the page, since, in that case, the radar can 

“see” the rifles at angles close to broadside (figure 15a and b). When the aperture is placed at the 

bottom side of the page, the human bodies partially block the rifles and consequently the crosspolarization enhancement effect is significantly reduced (figure 15c and d). Other interesting 

effects in the images shown in figures 14 and 15 (such as the presence of ghost images of 

humans projected on walls behind them) were explained in reference 2 and are not particularly 

relevant to our problem.

We summarize all the quantitative results from the images presented in sections 3.2 and 3.3 in 

table 1. In this table, we record the brightest pixel in the image area around each human for the 

cases with and without rifle, for co- and cross-polarization. Then we calculate and compare the 

cross- to co-polarization ratios for all these cases. We notice that in most cases this ratio is 

consistently at least 8 dB larger for the case when the human carries a rifle. Therefore, this 

metric can be used in a discrimination procedure that searches for humans carrying this type of 

weapon.
18

Figure 14. Co-polarization (V-V) SAR images of the complex room in figure 13 showing: (a) aperture on left side, 

humans without rifles; (b) aperture on left side, humans with rifles; (c) aperture on bottom side, humans 

without rifles; and (d) aperture on bottom side, humans with rifles.

(a) (b)

(c) (d)

-35

-25

-25

-29

-31

-26

-25

-31

-40

-33

-26

-28

-40

-33

-27

-28
19

Figure 15. Cross-polarization (V-H) SAR images of the complex room in figure 13 showing: (a) aperture on left 

side, humans without rifles; (b) aperture on left side, humans with rifles; (c) aperture on bottom side, 

humans without rifles; and (d) aperture on bottom side, humans with rifles.

(a) (b)

(c) (d)

-42

-38

-36

-45

-50

-48

-44

-51

-50

-54

-45

-49

-44

-52

-46

-49
20

Table 1. Comparison of maximum pixel intensity in the human areas within the SAR images presented in sections 

3.2 and 3.3, in co- and cross-polarization, for the cases with and without rifle (all values in dB). 

Max Pixel No Rifle

Rifle Tilt Angle 

0° 45° 90°

Free-space

Co-pol –17 –21 –22 –21

Cross-pol –40 –35 –35 –36

Ratio –23 –14 –13 –15

Simple room

Co-pol –30 –33

Cross-pol –54 –49

Ratio –24 –16

Complex 

room

Human #1

Co-pol –31 –35

Cross-pol –50 –42

Ratio –19 –7

Human #2

Co-pol –26 –25

Cross-pol –48 –38

Ratio –22 –13

Human #3

Co-pol –25 –25

Cross-pol –44 –36

Ratio –19 –11

Human #4

Co-pol –31 –29

Cross-pol –51 –45

Ratio –20 –16

3.4 Rifle Detection Algorithm Based on SAR Images of Rooms

In this section, we develop a behind-the-wall weapon detection algorithm based on the simulated 

SAR images in section 3.3. The decision is made by comparing the pixel-by-pixel map of the 

metric developed in the previous sections (the cross- to co-polarization signature ratio) with a 

threshold. The threshold is established by studying the metric’s probability distribution function 

obtained for simulated SAR images.
21

Consider the images in figures 14a and b and 15a and b (the complex room imaged from the left 

side). Before we compute the pixel-by-pixel cross- to co-polarization ratio map, we need to make 

sure that very low intensity pixels in the original images (which represent noise and can take 

random values) are not directly used in building the ratio map. Thus, we assign to every pixel in 

the original images below a certain threshold (–40 dB in the co-polarization images and –60 dB 

in the cross-polarization images) the value of that threshold. The thresholds were chosen by 

direct examination of the SAR images in figures 14a and b and 15a and b, such that all the 

targets of interest appear with intensities above those numbers. We also need to make sure that 

the “noise” pixels display a cross- to co-polarization ration below the detection threshold.

After the pre-screening procedure we build the pixel-by-pixel cross- to co-polarization ratio map. 

The probability distribution functions (PDF) of the ratio for the cases without rifles (figures 14a 

and 15a) and with rifles (figures 14b and 15b) are shown in figure 16. Notice that the PDF is 

always based on pixel intensities on linear (not dB) scales. The plots in figure 16 clearly 

demonstrate that there are more pixels with a high cross- to co-polarization ratio for the case with 

rifles, whereas for the case without rifles this ratio never exceeds 0.6. If we choose a detection 

threshold of –10 dB (corresponding to about 0.3 on linear scale), we obtain the detection maps in 

figure 17. Here, we notice that all four humans carrying rifles are detected correctly (figure 17b), 

but we also obtain a false alarm in the image representing humans without rifles (figure 17a). If 

we increased the detection threshold (say to 0.6 on linear scale), we would eliminate the false 

alarm, but in that case we would also miss detecting the rifle on human #4 in figure 17b. 

Figure 16. Normalized probability distribution functions of the pixel-by-pixel cross- to co-polarization ratio in the 

complex room SAR images obtained from the left side, showing (a) the entire distribution and (b) detail 

of the distributions’ tails.

Notes: • Only pixels above –40 dB for co-polarization and above –60 dB for cross-polarization were considered in 

these distributions.

• The pixel intensities were converted to linear scale.

(a) (b)
22

Figure 17. Detection maps for the complex room in figure 13 based on thresholding the cross- to co-polarization 

ratio, for the cases where (a) the humans carry no rifles and (b) the humans carry AK-47 rifles.

Note: In this case, the thresholding procedure was applied directly to the pixel maps in figures 14a and b and 

15a and b.

There are certain refinements that can be applied to the detection procedure outlined previously. 

For instance, a constant false alarm rate (CFAR) (15) algorithm can be employed in adaptively 

setting the detection threshold within the SAR image. Another technique, which we demonstrate 

here, “smears” the original SAR images by means of a moving average spatial filter, in order to 

eliminate spurious spikes that can lead to false alarms (16). It turns out that the images shown in 

figures 14 and 15 have very fine pixel size (1.9 by 1.9 cm), which is much below the expected 

image resolution (about 15 by 20 cm). In order to eliminate spurious pixel variations within a 

resolution cell, we apply a square moving average filter with size 10-by-10 pixels to the original 

cross- and co-polarization SAR images. We then create the new detections maps via the 

algorithm presented earlier in this section, this time setting the detection threshold at –15 dB 

(since the entire cross- to co-polarization ratio PDF is modified by the pixel averaging 

procedure). As seen in figure 18, all four humans carrying rifles are now clearly detected in the 

SAR image, whereas all false alarms (for the case of personnel without rifles) are eliminated 

(since for that case the detection map is blank, it is not shown here).

(a) (b)
23

Figure 18. Detection map for the complex room in figure 13 containing 

humans with rifles, obtained after applying a pixel averaging 

procedure to the images in figures 14a and b and 15a and b.

In practice, a radar system would need extensive training data in order to determine the detection 

algorithm parameters, such as the pre-screening and detection thresholds or the pixel averaging 

filter size. These would depend on the specific radar system and imaging parameters, but should 

be relatively constant for various target classes. Subject to further investigation, we conjecture 

that most common objects encountered in a STTW scenario exhibit a low cross- to copolarization signature ratio that is significantly below the ratio displayed by a rifle-like object. 

We also expect this result to hold independently from wall attenuation, clutter, or noise level in 

the image. Another important assumption which needs to be verified is that the cross- to copolarization ratio is relatively stable (within a few dB) for the radar signature of humans of 

various sizes and shapes.

3.5 Doppler Spectrum of a Walking Human Carrying a Rifle

In reference 5, we investigated the Doppler spectrum of walking human carrying an AK-47. We 

compared the Doppler spectrograms obtained in that case with those of a human walking without 

any weapon. We noticed some slight difference between the two cases, mainly the fact that the 

arm swinging (high velocity components in the spectrogram) does not show up in the case where 

the human carries the rifle. However, while the arm swinging motion can be clearly detected in a 

Doppler spectrogram, its absence does not necessarily indicate the presence of a weapon (the 
24

human may walk with the hands in the pockets, for instance). A more reliable method for rifle 

detection by Doppler radar would exploit the same polarimetric signature differences as 

explained in section 3.1.

In this section, we perform the analysis of the Doppler signature outlined in reference 5, by 

modeling the frame-by-frame radar return from a human in walking motion. After computing the 

in-phase-quadrature (I-Q) samples corresponding to each received pulse, we take a Fourier 

transform across the slow time in order to obtain the Doppler spectrum. The human walks 

straight at the radar (the azimuth and elevation angles are both 0°). In this example, the human is 

placed in free space. The motion of the human carrying the AK-47 differs from that of the free 

walking motion (figure 19a) only by the fact that the arms are fixed, as shown in figure 19b (the 

rest of the body, including the head, torso, and legs, has the same frame-by-frame position). In 

this section, we simply compute the cross- to co-polarization ratio of the Fourier transform 

magnitudes (there is no need to perform a time-frequency analysis such as in reference 5). The 

length of the Fourier transform is not critical in this case—we consider the I-Q data 

characterizing one full walking cycle. Other parameters relevant to the problem are as follow: 

walking cycle period, 2 s; average velocity of the human, 0.6 m/s; center frequency, 1 GHz; 

bandwidth, 80 MHz; coherent processing interval, coherent processing interval (CPI) = 0.3 s; and 

pulse repetition frequency, PRF = 40 Hz. 

 

(a) (b)

Figure 19. Human body mesh in walking motion showing (a) free walking motion and 

(b) walking with an AK-47.
25

The plots in figure 20 show the Doppler spectrum for V-V polarization (a), the V-H polarization 

(b), and the difference (in dB) between the V-H and V-V magnitudes (c) for both the walking 

human carrying a rifle and not carrying a rifle. Notice that, before we plot the difference between 

V-H and V-V magnitudes, we threshold out the spectrum data below –40 dB (for V-V) and 

–55 dB (for V-H), in order to eliminate spurious data points that characterize the difference 

between very low signal levels. The graphs in figure 20c show that, for the main part of the 

Doppler spectrum (between 0 and 1 m/s), the V-H to V-V ratio is significantly larger for the 

human carrying a rifle than for the human without a rifle. However, the Doppler response at the 

edges of the spectrum (between –0.5 to 0 m/s and between 1 and 2 m/s) shows a larger V-H to 

V-V ratio for the human without a rifle, which can be explained by the dominance of the 

swinging arm response in those regions of the spectrum.

We should mention here that the large cross- to co-polarization ratio difference between the two 

cases at 0° azimuth angle is easily explained by the simple fact that the human body alone is a 

highly symmetric target at this aspect angle; therefore, its cross-polarization signature is very low 

(in fact, the only asymmetry is introduced by the arm and leg motion; however, the dominant 

response comes from the torso which is exactly symmetric in the model). Therefore, we need to 

examine a more challenging case, such as when the azimuth angle is 30° (figure 20d). Even for 

that case, we notice a similar pattern in the cross- to co-polarization ratio, namely, a significantly 

larger ratio for the case with weapon in the main part of the Doppler spectrum.
26

Figure 20. Doppler spectrum of a human walking with and without a rifle showing: (a) V-V polarization, 

0° azimuth; (b) V-H polarization, 0° azimuth; (c) V-H to V-V ratio, 0° azimuth; and (d) V-H to 

V-V ratio, 30° azimuth.

3.6 Human Carrying a Rocket Propelled Grenade

In this section, we examine another target of interest, the RPG. Here, we call an RPG the entire 

assembly formed by the rocket-propelled grenade together with the launcher. Similarly to a rifle, 

this object exhibits a large shape factor; therefore, the same polarimetric radar techniques should 

apply in this case. Since we already proved that the cross- to co-polarization ratios of the target 

images are largely preserved upon transmission through a homogeneous wall, here we 

investigate only the free space case (as in sections 3.1 and 3.2). In our FDTD simulations, we 

(a) (b)

(c) (d)
27

used a high-fidelity Russian-made RPG-7 mesh, with a 2-mm resolution (figure 21). We 

considered that the entire assembly is made of metal (although some small parts are made of 

wood or ceramic materials, those are not important for the radar signature).

Figure 21. Different views of the RPG-7 mesh with 2-mm resolution (the tick marks on the 

axes represent numbers of FDTD cells).

In figure 22, we make an RCS comparison of a human alone versus a human carrying an RPG 

(similar to figure 7). We assume that the RPG is carried in an oblique position (45° tilt), as in 

figure 22a. The RCS values were computed in an angle interval of –30° to 30°, centered at 

broadside, and averaged over a 400-MHz frequency band centered at 1 GHz. Figure 22b shows 

the RCS for V-V polarization, figure 22c for V-H polarization, and figure 22d plots the ratio 

between the two polarizations. The conclusion is very similar to that in section 3.1, namely that 

the cross-polarization signature of the human is strongly enhanced by the presence of the 

weapon.
28

Figure 22. RCS of the standing human with and without an RPG versus azimuth angle showing: (a) the 

computational mesh; (b) RCS for V-V polarization; (c) RCS for V-H polarization; and (d) V-H to V-V 

RCS ratio.

(a) (b)

(c) (d)
29

Similar to section 3.2, we create SAR images of a human “carrying” the RPG in two positions, 

vertical and at 45° tilt. The images in figures 23 and 24 are based on the same algorithm and the 

same parameters as those in figures 8 and 9. One conclusion that we derive from figure 23b is 

that the V-V response of the RPG in vertical position is very strong, and it clearly enhances the 

V-V response of the human body (as opposed to the AK-47, which decreased the co-polarization 

response of the human body). This is made possible by the fact that the RPG has an RCS much 

larger than a rifle at broadside. Also, from figure 24b, we do not see a significant enhancement in 

the cross-polarization response of the human with the vertical RPG. In fact, the ratio of the 

brightest cross- to co-polarization pixels in the images is –27 dB, which is worse than the –23 dB 

in the case of the human body alone. Obviously, this metric cannot be used to discriminate a 

human carrying an RPG in vertical position. However, in this case the V-V polarization signature 

of the RPG becomes significantly larger than that of the human alone, so this signature alone can 

be used as a metric in discriminating the human carrying the weapon. Additionally, notice that 

the H-H image of the human with the vertical RPG (figure 23d) also shows a strong radar 

response for this polarization as well.

For the RPG tilted at 45°, the cross- to co-polarization ratio is –17 dB, which, although not as 

good as in the case of the AK-47, gives a 6 dB enhancement over the human body alone. In this 

case, the SAR images (both V-V and V-H) display a peculiar shape, caused by the significant 

cross-range extent of the RPG. This suggests that a detailed analysis of the image based on shape 

recognition can reveal the presence of the weapon more reliably than the polarimetric techniques 

introduced in this study.
30

Figure 23. Co-polarization SAR images of a human with and without an RPG showing: (a) human without RPG, 

V-V polarization; (b) human with vertical RPG, V-V polarization; (c) human with RPG tilted at 45°, 

V-V polarization; and (d) human with vertical RPG, H-H polarization.

(a) (b)

(c) (d)

Max -17 dB Max -11 dB

Max -19 dB Max -13 dB
31

Figure 24. Cross-polarization (V-H) SAR images of a human with and without an RPG showing: (a) human 

without RPG; (b) human with vertical RPG; and (c) human with RPG tilted at 45°.

(a) (b)

(c)

Max -40 dB Max -38 dB

Max -36 dB
32

4. Conclusions

This study presented polarimetric radar approaches that could be used in detecting small 

weapons concealed behind walls. The techniques rely on the fact that the radar signature of 

targets with a large shape factor is strongly polarization-dependent. We exploited this property 

for weapons such as rifles (AK-47) or RPGs. The emphasis was on weapons carried by humans. 

In this context, the problem could be reformulated from detecting a weapon to discriminating a 

human carrying a weapon from a human without a weapon. It is important to emphasize that the 

approaches presented in this report do not apply to small weapons (such as handguns) that do not 

satisfy the large shape factor requirements. Also essential to our approach is that the weapons are 

passively carried by humans (as, for instance, in the port-arm position), such that the radar 

illuminates the target mostly at broadside. These techniques do not apply to a rifle or RPG held 

in the shooting position, where the radar illumination is straight along the barrel. 

We first introduced a signature optimization procedure based on polarization direction 

transformations. Our computer models showed that this technique works as expected on an 

isolated rifle in free space, but breaks down in a more complex scenario where the rifle is carried 

by a human. A more robust approach is based on the strong cross-polarization response of a 

tilted rifle. In order to maximize the signature difference between a human carrying a weapon 

and a human without a weapon, we used the cross- to co-polarization return ratio. By simulating 

multiple STTW radar imaging scenarios, we demonstrated that this ratio is enhanced by at least 

8 dB in the presence of an AK-47 rifle. The major advantage of using the ratio of two different 

signatures of the same target for discrimination is that this procedure is largely independent of 

target size (in other words, by scaling up the target dimensions we do not change the cross- to 

co-polarization ratio). We developed a weapon detection algorithm based on SAR images and 

successfully applied it to the simulated image of a complex room containing personnel with or 

without rifles. We also applied this idea to a Doppler radar scenario that looks at a walking 

human, where the discrimination is based on magnitude differences in the Doppler spectrum.

The through-the-wall imaging examples presented in this report included mostly homogeneous 

walls (although some included windows and doors). It is very possible that the additional amount 

of clutter introduced in an image by inhomogeneous walls (such as those built from cinder 

blocks) may degrade the discrimination potential of our approach. Another problem of great 

interest that we have not investigated here is searching for weapon caches hidden inside walls or 

other concealed building enclosures. We expect this application to benefit again from operating 

the radar in cross-polarization mode, since the walls mostly disappear from the images created in 

this mode.

It is important to mention that operating a through-the-wall imaging radar in cross-polarization 

could prove very challenging. One obvious issue is that the cross-polarization signature of targets 
33

behind walls is usually very weak, especially after the radar waves incur a round-trip through the 

wall attenuation. In order to obtain signals above the noise floor in this mode, the radar design 

must include a low noise figure and a high dynamic range. Another difficult problem is the fact 

that typical wide-beam antennas (such as horn antennas), used in SAR systems at frequencies 

characteristic to STTW radar, do not display a pure polarization (either vertical or horizontal) 

away from boresight (17). That means it is impossible to obtain pure cross-polarization 

signatures of targets directly from radar measurements using these antennas (notice that this is 

not an issue for higher frequency SAR systems, where small integration angles about boresight 

are required, so the pure polarization assumption is mostly valid). A possible solution to this 

problem is to perform a polarimetric calibration of the radar by introducing a post-processing 

compensation procedure (18). Another possible approach would involve designing a wide-beam 

antenna with very low secondary polarization pattern at angles away from boresight.

Finally, we present a diagram (figure 25) meant to illustrate the benefits of using the crosspolarization mode in the wider context of STTW radar for military urban operations. We 

considered four types of targets that are frequently present in a STTW scenario: walls, furniture 

objects, humans and rifle-like weapons. The interest in detecting these targets usually increases 

from walls and furniture objects (which are considered clutter) to humans and eventually 

weapons carried by humans. As illustrated in figure 25, the cross-polarization signature of these 

targets also increases in the same order (from walls to weapons), whereas the co-polarization 

signature strengths follow the exactly opposite order. This suggests the use of the crosspolarization to co-polarization ratio as a good way to enhance the response from the most 

interesting targets (humans and weapons) while at the same time rejecting the clutter (walls and 

furniture in the scene). This metric can also be used as a discrimination tool in STTW imaging 

radar (as shown in sections 3.3 and 3.4) or Doppler radar (as shown in section 3.5). Although the 

operation of the radar in fully polarimetric mode presents a series of practical challenges, it 

would be very interesting to see our ideas (supported here by modeling results) confirmed by 

experimental data.

Figure 25. Diagram illustrating the typical STTW target hierarchy in terms of 

interest for military operations, together with the strengths of their coand cross-polarization radar signatures.
34

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Processing Algorithms; Artech House: Boston, MA, 1995.

14. ARL MSRC Web page. http://www.arl.hpc.mil (accessed August 2007).
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15. Gandhi, P. P.; Kassam, S. A. Analysis of CFAR Processors in Homogeneous Background. 

IEEE Transactions on Aerospace and Electronic Systems July 1988, 24 (4), 427–445.

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Kirose, G.; Martone, A.; Smith, G.; Sichina, J.; Kappra, K. Change Detection Using the 

Synchronous Impulse Reconstruction (SIRE) Radar. Proceedings of SPIE 2008, 6947.

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18. Barnes, R. M. Polarimetric Calibration Using In-Scene Reflectors; MIT Lincoln Laboratory 

Project Report TT-63: Lexington, MA, September 1986.
36

List of Symbols Abbreviations, and Acronyms

ARL U.S. Army Research Laboratory

CAD computer aided design

CERDEC Communications-Electronics Research Development and Engineering Center

CFAR constant false alarm rate

CPI coherent processing interval

CPU central processing unit

FDTD Finite-Difference Time-Domain 

EM electromagnetic

H-H horizontal-horizontal

I2WD Intelligence and Information Warfare Directorate

I-Q in-phase-quadrature

MSRC Major Shared Resource Center

PDF probability distribution function

RCS radar cross section

RPG rocket propelled grenade

SAR synthetic aperture radar

STTW sensing-through-the-wall

V-H vertical-horizontal

V-V vertical-vertical
37

No. of

Copies Organization

1 ADMNSTR

ELEC DEFNS TECHL INFO CTR

ATTN DTIC OCP

8725 JOHN J KINGMAN RD STE 0944

FT BELVOIR VA 22060-6218

1 DARPA

ATTN IXO S WELBY

3701 N FAIRFAX DR

ARLINGTON VA 22203-1714

1 CD OFC OF THE SECY OF DEFNS

ATTN ODDRE (R&AT)

THE PENTAGON

WASHINGTON DC 20301-3080

1 US ARMY RSRCH DEV & ENGRG 

CMND

ARMAMENT RSRCH DEV & ENGRG 

CTR

ARMAMENT ENGRG & TECHNLGY 

CTR

ATTN AMSRD AAR AEF T J MATTS

BLDG 305

APG MD 21005

2 US ARMY RDECOM CERDEC I2WD

ATTN AMSRD CER IW IM W CHIN

(2 COPIES)

BLDG 600 MCAFEE CENTER

FT MONMOUTH NJ 07703

1 PM TIMS PROFILER (MMS P)

AN/TMQ 52

ATTN B GRIFFIES

BUILDING 563

FT MONMOUTH NJ 07703

1 US NAVAL RSRCH LAB

RADAR DIV ADAPTIVE

PROCESSING SECTION

ATTN A DESROSIERS

4555 OVERLOOK AVE SW

WASHINGTON DC 20375-5312

1 US ARMY INFO SYS ENGRG CMND

ATTN AMSEL IE TD F JENIA

FT HUACHUCA AZ 85613-5300

1 COMMANDER

US ARMY RDECOM

ATTN AMSRD AMR W C MCCORKLE

5400 FOWLER RD

REDSTONE ARSENAL AL 35898-5000

No. of

Copies Organization

1 US ARMY RSRCH LAB

ATTN RDRL CIM G

TECHL LIB T LANDFRIED

BLDG 4600

APG MD 21005-5066

1 US GOVERNMENT PRINT OFF

DEPOSITORY RECEIVING SECTION

ATTN MAIL STOP IDAD J TATE

732 NORTH CAPITOL ST NW

WASHINGTON DC 20402

11 US ARMY RSRCH LAB

ATTN RDRL CIM P TECHL PUB

ATTN RDRL CIM L TECHL LIB

ATTN RDRL SER M W O COBURN

ATTN RDRL SER U A SULLIVAN

ATTN RDRL SER U C LE

ATTN RDRL SER U G KIROSE

ATTN RDRL SER U K KAPPRA

ATTN RDRL SER U M RESSLER

ATTN RDRL SER U

T DOGARU (2 COPIES)

ATTN IMNE ALC HRR

MAIL & RECORDS MGMT

ADELPHI MD 20783-1197

TOTAL 23 (1 ELEC, 21 HCS, 1 CD)
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INTENTIONALLY LEFT BLANK.