Chapter 3: Vehicle-Mounted GPR System for Landmine Detection

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38 3. Vehicle-Mounted GPR System for Landmine Detection 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.
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Chapter 3: Vehicle-Mounted GPR System for Landmine Detection

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