gaikwad 1

Study of Effect of Room Window on Through Wall Imaging in UWB range

Abhay N. Gaikwad, Dharmendra Singh and M. J. Nigam Department of Electronics and Computer Engineering

Indian Institute of Technology, Roorkee Roorkee, India

e-mail [email protected]

Abstract— In through wall imaging (TWI), detection is possible due to dielectric contrasts between target and room environment. Complexity increases if room consists of furniture’s and other objects beside desired target. Further detection of desired target becomes complex with the presence of window in back wall of room. Thus in this paper, effect of presence of window in back wall of room is investigated on detection and imaging. A detection technique is proposed in which signal processing technique is applied to extract the target information from clutter signal. A back projection imaging technique is applied to image the target. For this purpose, indigenously a TWI system based on step frequency continuous wave (SFCW) principle is developed in ultra wide band (UWB) range of frequency (i.e., 3.95 GHz to 5.85 GHz), plywood considered as wall and metallic plate is considered as target behind the plywood wall. The results are quite encouraging.

Keywords- Back projection, step frequency continuous wave radar, singular value decomposition, window effect.

I. INTRODUCTION Different technologies are applicable for through wall

imaging (TWI) out of which most suitable is radar based system which is still in pre-research stage. The brief outline of through wall imaging is given in [1]. The electromagnetic waves that are transmitted by the radar have to propagate through the air, non metallic wall and other objects. In TWI, radar must provide low frequencies for more penetration as well as resolution should be as high as possible. So the bandwidth of the radar must be large to obtain high resolution. Thus there is tradeoff between resolution, penetration and frequency. Radar based on step frequency continuous wave (SFCW) principle in ultra wide band (UWB) range can be used to achieve high range resolution along with synthetic aperture technique to provide better cross range resolution. According to FCC UWB systems can be defined as the system having fractional bandwidth of 20% or larger at -10 dB cut-off frequencies with a minimum bandwidth of 500MHz [2]. The other advantage of using UWB is that it will help in identification of target class and type because received signal carries information not only about the target as a whole but also about its separate elements.

The SFCW radar has some advantages over other radar such as wider dynamic range, higher mean power and lower noise figure.

Some of the problems posed to target detection in TWI system are, strong interfering signal due to radiation of radar transmitter, strong reflections from walls, absorption of electromagnetic energy by walls which will reduce signal strength of return signal, interference signals due to multiple reflections from other objects. Detection scheme is proposed in [3] using single and multiple view imaging. Detection becomes worse when the area to be image becomes complex. Presence of window in back wall of room will further complicate detection in unknown environment. Refraction, diffraction, dispersion, multipath and attenuation all affect through wall object detection process. The design challenges and issues related to through wall are discussed in [4]. Signal processing techniques is use to reduce unwanted signal (clutter) and enhance the target detection. Generally in signal processing terms the techniques used for clutter separation are called blind source/signal separation methods and is concerned with the separation of a set of signals called source signals from their mixtures signals, without acquaintance of any about mixing background and sources [5]. Blind source separation is the separation of a set of signals into a set of other signals, in which either the regularity between the signals is minimized by minimizing the decorrelation or the regularity between the signals by maximizing the statistical independence. Singular value decomposition (SVD) is used to separate data into complementary subspaces called signal and clutter subspaces in order to increase signal to clutter ratio [6]. In this paper we extend our technique used in [6] to construct images using back projection imaging algorithm.

The rest of paper is organized as follows: In section II, brief information about the setup and data collection is described. Section III describes methodology applied on data to detect target with and without presence of window in back wall of room. It presents algorithm with the help of flowchart in which different signal processing steps are describes to extract useful information about target from data and use it for imaging. The results obtained using proposed algorithm is discussed in section IV which is followed by conclusion in section V.

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II. EXPERIMENTAL SETUP AND DATA COLLECTION The measurement setup consists of step frequency

continuous (SFCW) radar as its transceiver. For measurement of A-scan and B-scan, a scanner which allows antenna to move in horizontal directions is used. The radar parameters used in experiment are given in table 1. Since the numbers of points are more, the time required to acquire one set of data for A-scan will be more. Though the maximum room dimension in down range is not more than 5 m, the maximum unambiguous range is taken more so that the other irrelevant signals will not affect target detection.

TABLE I. Radar system Parameters

Sr.No Parameters Value 1 Frequency range 3.95 to 5.85 GHz 2 Transmitted power 20 dBm 3 Number of frequency points 4001 4 Range resolution 7.5 cm 5 Cross range resolution* 12 cm, 13 cm 6 Polarization HH 7 Antenna Horn Antenna

*when synthetic array length is 1.2 and 1.3 respectively.

Data was taken for two different scenes. In first scene, metal target is placed behind plywood wall (front wall) in a room in which back wall does not have window and in second scene metal target is placed behind plywood wall in a room in which back wall have window. A square metal sheet of dimension 0.58 m in side with thickness 0.002 m is used as target in both scenes.

In first scene the antenna mounted on the wooden scanner is moved along a scan line with a length of 1.3 m. The transceiver moves along the scan with a spacing of 0.05 m. i.e for 26 positions A-scan data is collected. The distance between front wall and target is 0.30 m, distance between target and back wall is 0.20 m and distance between antenna feed point and plywood wall is 2.08 m.

In second scene to demonstrate the effect of presence of window on target detection, a location was chosen in a room where window is present on back wall. The experimental setup was arranged accordingly. The dimension of window was 1 m width to 1.15 m height. Window was at a height of 0.90 m from ground plane. The synthetic array length of 1.2 m is used i.e., at 24 positions, A-scan data is collected at a height of 1.1 m. The distance between antenna feed point and plywood wall is 2.4 m, distance between plywood wall and target is 0.30 m and target and back wall is 0.20 m.

III. SIGNAL PROCESSING STEPS USED IN IMAGING The detail signal processing steps are given in flow chart as

shown in Fig.1.

Step 1. Filtering: SFCW radar generates the data which we named as raw data. When the data is collected at one particular position it is called as A-scan data. From the A-scan data, noise

Fig. 1 Flowchart showing the B-scan process.

and signal are separated in the frequency domain using filtering by applying a standard windowing function, such as the Hamming window [7].

Step 2. Frequency domain data to time domain: After filtering, data is converted into time domain by using Inverse Fast Fourier Transform (IFFT). Time domain data can be represented as

)2exp()()(1

tfjfSts k

K

kk π∑

=

= (1)

where K is maximum number of frequency points that are set to 4001. S(fk) is the received reflected signal in frequency domain at kth frequency and t varies from 0 to BWK /)1( − with step interval of BW/1 , BW is bandwidth of the system. Step 3. Time domain data to spatial domain:

)2exp()()(1

tfjfSzS k

K

kk π∑

=

= (2)

where 2/ctz = The maximum distance is calculated by

BWKcZ 2/)1(max −= . In this case it is approximately 315 m with range resolution fZ Kc Δ=Δ 2/ where fΔ is step size


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and calculated as KBWf /=Δ which is 0.475 GHz. Since the maximum room dimension is small, the distance for range profile is taken upto 5m. Step 4. Calibration using metal sheet: In order to identify the delay due to antenna system, calibration using metallic plate was carried out [8]. The metallic plate is kept at a known distance and the range profile is plotted from which delay due to antenna system is calculated. To find out the exact distance between antenna system and plywood wall the reflection from antenna system which was found through calibration should be subtracted. If the reference plate is located at a distance Rref then the two way propagation delay tref is given by:

cRt refref /2= (3) where c is the speed of light. The corrected range profile will be

∑=

+=K

krefkkref cRczfjfSRzs

1

))/2/2(2exp()(),( π (4)

It is found that due to delay, wall and target position are shifted by 48 cm in down range. Step 5. Velocity correction: For accurate position of target, velocity correction must be applied on received signal [9]. The delay time through walls compared to free space propagation is determined by

cDtdelay rwallinwall /)1( −= ε (5) Therefore this additional time which signal is taking due to different velocity of wave in wall than its velocity in air; must be subtracted from original range profile given by (6)

( ) ∑=

++=K

kdelayrefkkfinal czcRczfjfSzs

1

))/2/2/2(2exp()( π

(6) Step 6. Normal B-scan image: A-Scan (range profile) provides very limited amount of information, therefore the information in more than one A-Scan has to be combined. This set of 1D A-Scans, can thus be assembled together in a two dimensional structure, and visualized as an image known as B-scan image. It can be represented by (7) in which signal intensity is varying with the distances.

( ) ∑=

++=K

kdelayrefkkfinal czcRczfjfSzxI

1

))/2/2/2(2exp()(, π

(7) Where x is the antenna scanning positions that lies between 1 to 30 and zfinal is the downrange locations for all antenna positions and lies between 0 to 5m. Step 7. Target signal estimation (SVD): We can split normal B-scan data into three parts using SVD.

nbt MMMI ++= (8) Where Mt, Mb and Mn are the target, background and clutter signals, respectively. Target signal can be estimated directly using (9) and remaining components M3 to MN contain the clutter.

TvuR 222 ××= σ (9)

Step 8. Imaging using backprojection: Back projection imaging algorithm [10] is widely used in through wall imaging for image generation because of high image quality and being compensated easily. The data obtained after applying SVD is used as a input for the back projection imaging algorithm.

IV. RESULT AND DISCUSSION For two different situations results are obtained using

proposed algorithm. From first scene, after comparing Fig. 2 and 3, it is observed that front wall reflection strength get reduced in presence of target. Due to presence of metal target there is no reflection from back wall as observed in Fig. 3. To see the effect of window, data collected from second scene is analyzed. Fig. 4 represent the range profile when target is not present behind front wall and window is present in back wall but is not in line of sight of antenna (i.e., at position 23) whereas Fig. 5 represent the range profile when window is in line of sight of antenna (at position 8) . Due to presence of window in back wall, back wall reflection as well as multiple reflection are absent where as when window is absent in back wall, reflection from back wall, multiple reflection are present which may be treated as target reflection i.e false target. Fig. 6 represents range profile when target is placed behind plywood wall and is in front of window. Fig. 7 (a) shows image formed by back-projection on raw data collected in second scene. It is observed that, target is located further from its actual position. Cross talk reflection, wall reflections are strong compared to target reflection.

Fig. 2 Range profile at position 8 when window absent in back wall of room and target is absent.

Fig. 3 Range profile at position 8 when window is absent in back wall of room and target is present.


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Fig. 4 Range profile at position 23 when window is absent in back wall of room and target is absent.

Fig. 5 Range profile at position 8 when window is present in back wall of room and target is absent.

Fig. 6 Range profile at position 8 when window is present in back wall of room and target is present.

If instead of metal other material having low dielectric constant is used as a target then reflection will be very weak. From Fig. 7 (b) which is image after applying processing technique we can clearly observe the metal target having high intensity values at location of 2.529 m after correction and which is near to ground truth value.

V. CONCLUSION In presence of window, reflection from back wall is

absent as expected. There will be constant reflection from back wall in absence of window. Presence of window in scanned area creates ambiguity in detection of target. It is observed that the proposed algorithm can eliminate this ambiguity and detect target. Drawback of back-projection is that target spread in cross range direction but advantage is that it gives accurate location and focused image of target. In future further processing will remove spread and more accurate detection in cross range will be possible.

(a)

(b)

Fig. 7 Image formed when window is present in back wall of room. (a) Raw image, (b) Image after target signal estimation.

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[3] C. Debes, M. G. Amin, and A. M. Zoubir, “Target detection in single and multiple view through the wall radar imaging,” IEEE Trans. Geosci. Remote Sens., vol. 47, no. 5, pp. 1349–1361, May 2009.

[4] M. Farwell, J. Ross, R. Luttrell, D. Cohen, W. Chin, and T. Dogaru, “Sense through the wall system development and design considerations,” J. Franklin Inst., vol. 345, pp. 570–591, Jan. 2008.

[5] N. Delfosse and P. Loubaton, “Adaptive blind separation of independent sources a deflation approach,” Signal Processing, vol. 45, pp. 59-83, Jul. 1995.

[6] R. Chandra, Abhay N. Gaikwad, Dharmendra Singh, and M. J. Nigam, “An approach to remove the clutter and detect the target for ultra-wideband through-wall imaging,” Journal of Geophysics and Engineering, vol. no. 5, pp. 412–419, Oct. 2008.

[7] A. Muqaibel, A. Safaai, A. Bayram, A. M. Attiya, and S. M. Riad, “Ultrawideband through the wall propagation,” in IEE Proc. Microw. Antennas Propag., vol. 152, no. 6, Dec. 2005, pp. 581-588.

[8] I. Nicolaescu, P. V. Genderen, and J. Zijderveld , “Archimedean spiral antenna used for stepped frequency radar footprint measurements,” in Antenna Measurement Techniques Association AMTA 2002, USA, Nov. 2002, pp. 555-560.

[9] P. K. Verma, A. N. Gaikwad, D. Singh, and M. J. Nigam, “Analysis of clutter reduction techniques for through wall imaging in uwb range,” Progress In Electromagnetics Research B, Vol. 17, 29−48, 2009.

[10] M. Dehmollaian and K. Sarabandi, “Refocusing through building walls using synthetic aperture radar,” IEEE Trans. Geosci. Remote Sens., vol. 46, no. 6, pp. 1589-1599, Jun. 2008.


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