Chapter 3: Vehicle-Mounted GPR System for Landmine Detection

3. VEHICLE-MOUNTED 

GPR SYSTEM FOR 

LANDMINE DETECTION 

3.1 System Concepts 

This radar system makes use of both a GPR and a metal detector, i.e., it is a dual 

sensor system, and the sensors work simultaneously. As described in Chapter 1, 

metal detectors can have responses to very small amounts of metal and results in a 

large number of false alarms. One would be able to identify whether any one of 

those responses is a landmine or not by observing the GPR images, because it 

contains information on object dimensions. If the detected metal object is small, e.g. 

a nail or bullet, GPR cannot reproduce or display a clear image, and if the object is 

large, e.g. “unexploded ordinance” (UXO), GPR can image its shape. This detection 

scenario is commonly used for dual sensor systems. Another concept is that radar 

antennas configured as an array are employed in this system. It can acquire 

multi-offset data sets and enables “common mid-point processing” (CMP). CMP 

processing can reduce noise and clutter, thus the obtained image would display a 

clear contrast between the surrounding medium and objects. In addition, this sensor

28 3. Vehicle-Mounted GPR System for Landmine Detection 

is scanned by a robot arm mechanically. Hence, data quality would be stable, and 

the sensor can be scanned automatically resulting in the system to become of high 

efficiency. The goal is for the surveying time including data acquisition, processing, 

and interpretation to be within 10 minutes for 1 sq. m. 

The image reconstruction uses synthetic aperture radar (SAR) technology, thus 

the system is named SAR-GPR. The development of SAR-GPR is under a program 

“Research and Development of Sensing Technology, Access and Control Technology 

to Support Humanitarian Demining of Antipersonnel Mines” founded by Japanese 

Science and Technology Agency (JST), which the competent authority is Ministry of 

Education, Culture, Sports, Science and Technology (MEXT). 

3.2 Development of the System 

3.2.1 Hardware configuration 

Fig. 3.1 shows the system diagram. When the sensor allocates a measurement 

position, the vehicle sends a trigger signal to the vector network analyzer (VNA) 

controller. The controller triggers the data acquisition by the VNA and the metal 

detector. The acquired data is buffered to the controller for the radar data and to PC 

Ethernet 

PC for 

MD 

PC for 

GPR 

RS232C 

Trg 

Ethernet 

Metal 

detector 

VNA #1 

VNA #2 

Trg 

VNA 

controller 

VNA #3 

Vehicle 

Antennas 

Fig. 3.1: System diagram of the vehicle-mounted GPR system, SAR-GPR. 

MD 

sensor 

head

3.2 Development of the System 29 

for the metal detector data. The system is a vector network analyzer based radar 

system, i.e., it is a stepped-frequency radar system. The reason why this type of 

radar system is employed is that the most critical parameter to investigate 

subsurface object is the operating frequency range, and it can easily be changed in a 

stepped-frequency radar system. 

The radar system except the PCs and VNA controller is assembled and installed 

in the case shown in Fig. 3.2. The lower blue box includes the six antennas, and the 

upper white box has three VNAs. The size of the case is 50×40×50 cm, and the 

weight is 14 kg. The case is attached to the tip of the robot arm on which the vehicle 

is developed by Fuji Heavy Industries Ltd., Japan and by the Department of 

Electronics and Mechanical Engineering, Chiba University, Japan as shown in Fig. 

3.3.


Fig. 3.2: Case and VNAs of SAR-GPR. Three VNAs shown in the right-hand side 

are installed in upper white part, and six antennas are in lower blue part of the case. 

Fig. 3.3: SAR-GPR mounted on Mine Hunter Vehicle (MHV) developed by Fuji 

Heavy Industries Ltd., Japan and by the Department of Electronics and Mechanical 

Engineering Chiba University, Japan.


3.2.2 Use of three VNAs 

In this system, three pairs of antennas are used to acquire multi-offset data. In 

general GPR measurements of geological surveys, the data acquisition is done over 

and over again with changing the separation of antennas or with switching the signal 

using array antennas. However, the acquisition must be done three times for one 

position and the goal cannot be accomplished by using these methods. Our solution 

is simultaneous usage of three vector network analyzers. This manner can ideally 

reduce the acquisition time by 1/3 compared to the case of using only one VNA. 

There are two problems in the use of three VNAs simultaneously. One is the size 

and weight. In general, a VNA is a huge and heavy instrument, e.g. 35×22×46 cm 

and 25 kg (Anritsu MS4622). It is too large and heavy to carry three of them on a 

robot arm or even on a vehicle. Thus, the team of Tohoku University developed a 

new VNA shown in Fig. 3.4 together with Anritsu, Japan and USA under support of 

JST. It is very small, 20×30×5 cm, and very light weight, less than 1 kg without 

case, but it has almost the same performance as the general one, especially for the 

sweep speed and accuracy. The specifications are listed in Table 3.1. 

Fig. 3.4: Vector network analyzer employed in SAR-GPR.


The second problem is interference of the signals. Use of some VNAs 

simultaneously means the same signals are emitted and measured by each VNA, 

yielding a signal radiated by the transmitter #1 may be received by not only the 

receiver #1 but also the receiver #2 and #3, for instance. To fix this problem, slightly 

different frequency ranges are used in each of the VNA as shown in Fig. 3.5. The 

offset frequencies are defined by trial and error. Fig. 3.6 shows the power spectra of 

100 ground surface reflections measured by the system with offset frequencies of 

100 kHz and 300 kHz and an IF bandwidth 1 of 30 kHz. The obvious fluctuations 

can be observed, and those for VNA #2 are more than 10 dB. Fig. 3.7 shows the 

spectra with offsets of 500 kHz and 1.25 MHz and an IF bandwidth of 3 kHz. They 

have almost no fluctuations. From the trials, it is found that an offset frequency must 

not be a multiple of the other offset, because the harmonics may be radiated by the 

transmitter and it can be measured by a receiver of another pair. Moreover, a sweep 

of a VNA can catch up with another sweep since each VNA has its own individual 

characteristics. In SAR-GPR, the offset frequencies of 500 kHz and 1.25 MHz are 

used. 

Table 3.1: Specification of the VNA, Anritsu MS6223 (commercial) 2 . 

Measurement mode 

Frequency sweep range 

Amplitude accuracy 

Frequncy accuracy 

Output level 

Dynamic range 

S21 

100 MHz to 4 GHz 

± 1 dB (at -20 dB) 

± 5 kHz (at 2GHz) 

-5 dBm to +10 dBm 

70 dB 

Number of frequencies 69/137/275/551 

Sweep Speed 

Frequency resolution 

Weight 

Size 

0.86 s (551 points) 

< 100 kHz 

< 3.5 kg (with case, battery) 

313× 211× 77 mm (with case) 

Operating temperature -10 to +50 ºC 

1 IF (intermediate frequency) bandwidth is the filter bandwidth for an input signal of a VNA. 

2 Actual installed VNA is modified for the system.


Frequency 

Offset 2 

Offset 1 

VNA #1 

VNA #2 

VNA #3 

Time 

Fig. 3.5: Frequency sweeps of VNAs. Three VNAs use slightly different frequency 

ranges with two offset frequencies. 

Power [dB] 

Power [dB] 

0 

−10 

−20 

−30 

−40 

−50 

−60 

−70 

0 

−10 

−20 

−30 

−40 

−50 

−60 

−70 

1000 2000 3000 4000 

Frequency [MHz] 

(a) 

1000 2000 3000 4000 

Power [dB] 

0 

−10 

−20 

−30 

−40 

−50 

−60 

−70 

1000 2000 3000 4000 


(b) 


(c) 

Fig. 3.6: Power spectra of 100 ground reflections measured by VNA #1 (a), VNA 

#2 (b), and VNA #3 (c) with offset frequency of 100 kHz for VNA #2 and 300 kHz 

for VNA #3, and an IF bandwidth of 30 kHz.


Power [dB] 

Power [dB] 

0 

−10 

−20 

−30 

−40 

−50 

−60 

−70 

0 

−10 

−20 

−30 

−40 

−50 

−60 

−70 

1000 2000 3000 4000 


(a) 

1000 2000 3000 4000 

Power [dB] 

0 

−10 

−20 

−30 

−40 

−50 

−60 

−70 

1000 2000 3000 4000 


(b) 


(c) 

Fig. 3.7: Power spectra of 100 ground reflections measured by VNA #1 (a), VNA 

#2 (b), and VNA #3 (c) with offset frequency of 300 kHz for VNA #2 and 1.25 

MHz for VNA #3, and an IF bandwidth of 3 kHz. 

3.2.3 Antennas 

Vivaldi antennas are employed in this system. It is a kind of microstrip antenna, 

which possesses a wide bandwidth and a high cross-polarization ratio (Guillanton et 

al., 1998). Since it can be made on a thin and lightweight circuit board, it has 

advantages, for example easy to be manufactured and to be configured as an array. 

For SAR-GPR, the antenna shown in Fig. 3.8(a) has been designed and 

manufactured. The size is 19×20 cm, and the antennas are aligned with a spacing of 

6 cm as shown in Fig. 3.8(b). Return loss of the antennas with the array 

configuration is shown in Fig. 3.9.


(a) 

60 

200 

Rx #3 

Rx #2 

Rx #1 

Tx #1 

Tx #2 

Tx #3 

190 

(b) 

Fig. 3.8: Antenna array used in SAR-GPR. Picture of the single Vivaldi antenna (a), 

and Vivaldi antennas aligned as the array (b). 

Return loss [dB] 

0 

−10 

−20 

−30 

−40 

−50 

1 2 3 4 

Frequency [GHz] 

Fig. 3.9: Return loss in antennas arrayed. The solid, broken, and dotted-broken 

lines show that of antenna #1 (inner pair), #2 (middle pair), and #3 (outer pair), 

respectively.


3.3 Image Reconstruction 

In this system, well-established and fast imaging algorithms are implemented in 

order to satisfy the time limitation. The followings are the processing flow of the 

acquired data as summarized in Fig. 3.10 (Feng and Sato, 2004): data D 

( r, ω) 

acquired at a certain position r = {, x yz ,} includes direct coupling components 

D 

c 

( ω) 

. The components can be acquired prior to a measurement pointing the 

antennas to the air space and is subtracted from the measured data. 

S (, r ω) = D (, r ω) −D c 

( ω) 

(3.1) 

Then inverse Fourier transform is taken to construct time domain signals. 

1 

s(,) r t = F − ⎡ S 

⎣ 

(, r ω) 

⎤ ⎦ 

(3.2) 

Signals acquired by different antennas have different amplitudes. Their amplitudes 

should take a balance to make the weights equal for CMP stacking. The balanced 

traces can be obtained by weighting the original traces, and the weighting factor is 

calculated from the averaged amplitudes. First, a time average of a trace by an 

antenna combination is calculated as 

1 

Qn() r = dt sn(,) t n= 

1, , N 

T 

∫ r , (3.3) 

where N denotes the number of antenna combinations, N = 3 in the system, and 

T is a length of the time window. Then the averaged amplitude for all 

combinations is calculated as 

Frequency 

domain data 

Direct coupling 

subtraction 

IFT 

Trace 

balancing 

NMO 

CMP 

Migration 

3D image 

Fig. 3.10: Processing flow chart in SAR-GPR.

3.3 Image Reconstruction 37 

N 

1 

Q() r = ∑Qn 

() r . (3.4) 

N n = 1 

According to Eqs. (3.3) and (3.4), the weighting factor is defined as the ratio 

Q() 

r 

Wn 

() r = 

Q () r 

n 

(3.5) 

With the weighting factor, the balanced traces sˆ( r ,) t are calculated as 

sˆ (,) r t = s (,) r t ⋅W 

() r . (3.6) 

n n n 

Next, these traces for each antenna combination are stacked to reduce incoherent 

clutter and noise. Since these traces have different offset (antenna separation), their 

time axes are not the same. To compensate the axes, normal move out (NMO) is 

performed, and then the traces are stacked. 

where 

s (,) r t = ∑ sˆ 

(, r τ ), (3.7a) 

CMP n n 

n= 

1 

N 

air soil 

⎛Ln 

L ⎞ 

n 

τ 

n 

= 2⎜ 

+ ⎟, (3.7b) 

⎝ c 1 ε ⎠ 

air 

L and L soil are the path lengths in the air and soil, respectively. c is the 

velocity of the electromagnetic wave in the air, and ε is a permittivity of the soil. 

This process virtually constructs a monostatic radar signal measured at the CMP 

position from bistatic signals as illustrated in Fig. 3.11. Then migration is performed 

for the stacked signals. Here diffraction stacking is used as a migration technique. 

The amplitude at a position r ′ = { x′ , y′ , z′ 

} in the three-dimensional image is given 

by 

where 

i( r′ ) dxdys ( r , τ ′), (3.8a) 

= ∫∫ 

CMP 

2 ′ − 

τ ′ = r r , (3.8b) 

1 ε 

and r = { x, yz , } is the position signal acquired, i.e., the CMP position.


CMP 

Tx N 

Tx 1 

Rx 1 

Rx N 

L air 

L soil 

Fig. 3.11: Concept of CMP stacking. It constructs a virtual monostatic radar signal 

measured at the CMP point. 

3.4 Quick Detection Algorithm 

In general, landmines are found out from the GPR images by carefully checking 

the slices with changing the depths. It requires knowledge and experiences and is 

time consuming. Since actual demining operations will be done by non-specialists 

of GPR, the interpretation must not be complicated. Here a quick detection 

algorithm for landmines from GPR image is proposed. The algorithm is designed for 

the quick operation, and it permits that some number of false alarms may occur. 

The processed image i( r ) has high amplitudes at locations where something is 

buried or medium properties have changed. To make the interpretation easy, the 

amplitudes are mapped on two-dimensional space in this algorithm. At first, 

envelopes of the traces with respect to depth are taken 

[ ] 

e() r = env i() 

r (3.9) 

z 

Here the envelopes are taken by using the Hilbert transform 

j i( r′ 

) 

env[ i() r ] = i() r + H [ i() r ] = i() 

r + dz′ 

z 

π 

∫ 

r− 

r′ 

(3.10) 

Then moving averages of the envelopes are taken with a certain window width w .

3.4 Quick Detection Algorithm 39 

1 z′+ 

w 2 

e( r′ ) = dze( ) 

w 

∫ r (3.11) 

z′−w 

2 

The appropriate window width may depend on the wavelength of the system, and 

the most effective width is a semi-period of a trace. The data are normalized by the 

maximum value in each depth slice, and summed values with respect to depth are 

projected to a map. 

pxy ( , ) = ∫ dz 

e () r 

max ( ) 

xy , 

[ e r ] 

(3.12) 

In the actual demining test for a sensor, detected targets are rated with the 

confidence. The confidence rating is defined as shown in Table 3.2. To calculate the 

confidence ratings, the map p( xy , ) is normalized again and is thresholded. 

xy , 

( , ) − min [ pxy ( , )] 

xy , 

[ p xy] − [ pxy] 

pxy 

pxy ˆ( , ) = 

max ( , ) min ( , ) 

xy , 

⎧ 0 0 ≤ p( xy , ) ≤th1 

⎪ 

25 th1 < p( x, y) 

≤th2 

⎪ 

prate( xy , ) = ⎨ 50 th2 < pxy ( , ) ≤th3 

⎪ 70 th3 < p( x, y) 

≤ th4 

⎪ 

⎪ ⎩100 th4 

< p( x, y) ≤1 

(3.13) 

(3.14) 

The four thresholds should be defined by analyzing the stochastic distribution of 

p( xy. , ) 

This algorithm is not a trace-by-trace process, because the algorithm uses the 

migrated data and the normalization in Eq. (3.12) is taken in a two-dimensional 

space. Thus, the rating in Eq. (3.14) is not an absolute evaluation. It compares the 

energy of the trace with neighboring ones meaning a relative evaluation. If the 

ground surface is relatively flat and no large terrain changes, this algorithm might 

work well. If not, it however could output errors.


Table 3.2: Definition of the confidence ratings. 

Ratings 

Criteria 

0 I am 100 % sure that there is nothing here 

25 It seems that there is something here 

50 I am 100 % sure that there is something here 

75 

I am not totally sure, 

but I would say the detected object seems to be a landmine 

100 I am 100 % sure that the detected object is a landmine 

3.5 Experimental Results 

An example of the experimental result is shown here. The measurement was 

carried out at a sand pit in the GPR Lab., Tohoku University, Japan. The targets 

were landmine models of Type72 and PMN2 shown in Fig. 3.12, and a mine-like 3 

stone buried as Fig. 3.13. They are buried at a depth of 2 cm, and the landmine 

model PMN2 is buried vertically. The soil is dry sand mixed with gravels whose 

diameters are around 2 cm. The GPR antennas were scanned by an x-y stage 

(Device Co., Ltd., Japan) with a velocity of 100 mm/s as shown in Fig. 3.14. The 

measurement interval is 30 mm in both x and y directions. 

The raw time domain data acquired by VNA #1 (inner pair) is shown in Fig. 3.15 

as a horizontal slice at a time of 2.4 ns, which corresponds at a depth of about 2 cm. 

The three targets are visible in this profile, however also clutter can be seen and the 

responses from the targets are not so clear. Thus, they cannot be recognized without 

a priori knowledge. Fig. 3.16 shows vertical and horizontal slices of the processed 

data at x =110 cm and at a depth of 2 cm. The three targets are clearly imaged and 

they have obviously different shapes and dimensions to clutter imaged at left on the 

horizontal slice. Note that the depth in the vertical slice is defined from the tip of the 

antennas, thus the ground surface is imaged at a depth of 10 cm. The measurement 

was a blind test, i.e. the operator did not know the types, positions, depths, and 

numbers of buried objects prior to the measurement. From the image, the operator 

3 The term mine-like object is defined as an object whose shape and dimension resemble that of a 

landmine.

3.5 Experimental Results 41 

could detect all of the objects including the stone on site. 

The quick detection algorithm is applied to this data. The window width w in Eq. 

3.11 has to be defined first from the wavelength. Fig. 3.17(a) shows traces at 

( x, y ) = (1050, 1000) mm (at Type72 buried), and (800, 1000) mm (at nothing 

buried). Since the semi-period seems 20 mm, the width w is defined as 20 mm. In 

these traces, the reflection from the target cannot be seen at the depth of the targets 

buried, 120 mm, since it is masked by the reflection from the ground surface. 

Whereas, it can be seen below a depth of 200 mm as a tail of the reflection. The 

envelope and moving averaged envelope of the traces are shown in Figs. 3.17(b) and 

(c). By moving average, the envelope is smoothened. Fig. 3.17(d) shows the 

normalized envelope. The tail of the reflection at a depth of 200 mm is enhanced. 

The histogram of the value pˆ( xy , ) in Eq. 3.13 is shown in Fig. 3.18. Here the 

thresholds to rate the confidence are simply defined as 

(a) 

(b) 

Fig. 3.12: Landmine models of Type72 (a), and PMN2 (b).


⎧ th1 

= 0.2 

⎪ th2 

= 0.4 

⎨ 

⎪ th3 

= 0.6 

⎪ 

⎩ th4 

= 0.8 

, (3.15) 

i.e., the distribution is divided into five classes with the same widths. The image of 

confidence ratings is shown in Fig. 3.19. The two landmine models successfully are 

rated as 100 % confidence, and the stone is as 50 %. The stone has a curved surface, 

thus the back scattering might not be as strong as for the landmine models and it 

does not have the tail of the reflection. It might cause a low confidence rating. From 

the result, the survey may output three or four false alarms in the left side of the area. 

It can be reduced by defining the more appropriate thresholds with stochastic 

analyses. 

Experimental results of an evaluation test for the system are summarized in 

Appendix A. 

1200 

x 

Type72 

PMN2 

900 

Stone 

300 

y 

900 

700 1600 

Fig. 3.13: Layout of the buried landmine models. PMN2 landmine model was 

buried erectly.


Fig. 3.14: Scene of the measurement in GPR Lab., Tohoku University, Japan. 

1200 

1.5 

X Position [mm] 

1000 

800 

600 

400 

800 1000 1200 1400 1600 

Y Position [mm] 

Fig. 3.15: Horizontal time slice at 2.4 ns acquired by VNA #1 (inner pair of the 

antennas). The time of 2.4 ns corresponds to a depth of 2 cm. 

1 

0.5 

0 

−0.5 

−1 

−1.5


0 

4 

Depth [mm] 

100 

200 

300 

2 

0 

−2 

400 

800 1000 1200 1400 1600 


(a) 

1200 

−4 

2 


1000 

800 

600 

400 

800 1000 1200 1400 1600 


(b) 

Fig. 3.16: Processed images. Vertical slice at x=1100 mm (a), and horizontal depth 

slice at a depth of 2 cm (b). 

1 

0 

−1 

−2


10 

Amplitude 

0 

−10 

0 200 400 600 

10 

Depth [mm] 

(a) 

Amplitude 

5 

10 

0 

0 200 400 600 

Depth [mm] 

(b) 

Amplitude 

5 

Amplitude 

0 

0 200 400 600 

1 

0.5 

Depth [mm] 

(c) 

0 

0 200 400 600 

Depth [mm] 

(d) 

Fig. 3.17: Depth traces at ( x, y ) =(1050, 1000) mm where a Type 72 landmine 

model is buried (solid lines) and at ( x, y ) =(800, 1000) mm where nothing is 

buried (broken lines). (a) Traces after migration, (b) envelopes of the traces, (c) 

moving averaged envelopes, and (d) normalized envelopes.


800 

Frequency 

600 

400 

200 

0 

0 0.5 1 

Amplitude 

Fig. 3.18: Histogram of the distribution of pˆ( xy. , ) The dotted-broken lines show 

the thresholds defined at th 

1 

= 0.2, th 

2 

= 0.4, th 

3 

= 0.6, and th 

4 

= 0.8 to rate the 

confidences. 


1200 

1000 

800 

600 

400 

100 

75 

50 

25 

0 

800 1000 1200 1400 1600 


Fig. 3.19: Image of the confidence ratings.

Chapter 3: Vehicle-Mounted GPR System for Landmine Detection

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