Fuzzy Automatic Detection of Landmines from Sensors Data

Transcription

1 Fuzzy Automatc Detecton of Landmnes from Sensors Data ZAKARYA ZYADA* AND TOSHIO FUKUDA** * Faculty of Mechancal Engneerng, Unverst Teknolog Malaysa, Johor, MALAYSIA; ( On leave Department of Mechancal Engneerng, Tanta Unversty, Tanta, EGYPT) ** Department of Mechatroncs Engneerng, Mejo Unversty, Shmogamaguch, Tempakuku, Nagoya , JAPAN *zakarya@utm.my, (zzyada@f-eng.tanta.edu.eg); **tofukuda@mejo-u.ac.jp Abstract: - In ths paper, two fuzzy algorthms for automatc decson makng for antpersonnel landmne detecton form sensors data. The frst s a feature n-decson out fuzzy fuson algorthm for two sensors measurements, namely a ground penetratng radar (GPR) and a metal detector (MD). The nputs to the fuzzy fuson algorthm are features extracted from both GPR and MD measurements whle the output s a decson f there s a land mne and at what depth t would be. Fuzzy fuson rules are extracted from tranng data through a fuzzy learnng algorthm. The second s a 3D fuzzy template based automatc detecton algorthm from a sngle sensor data, (GPR). A 3D template s chosen and another 3D fuzzy template s desgned. The 3D fuzzy template, n whch a data pont s expressed as a trapezodal fuzzy set, s extracted from expermental data. Landmne smlarty for both the 3D template as well as the learnt fuzzy template s examned by a crsp smlarty measure and a fuzzy smlarty measure respectvely. Results of both the two fuzzy decson makng algorthms are presented whch show ther promses n landmne detecton and ts dscrmnaton from other objects. Key-Words: - Fuzzy decson makng, landmne detecton, sensor fuson 1 Introducton Landmne detecton has attracted much attenton by many research teams around the world durng the last two decades. It s estmated that more than 70 mllon actve landmnes are scattered n 62 countres around the world. One of the most major sensors appled for current humantaran demnng s the metal detector. It s smple and cost effectve. It s also relable to fnd an ant-personal mne (APM) n a shallow subsurface. However t suffers from the hgh false alarm rate, (about 99.95%), as t senses all metal objects ncludng metal fragments n the feld other than land mnes. Another sensor s the ground penetratng radar whch s a promsng technque for sensng objects underground, [1], [2], based on delectrc propertes. However t senses a land mne object as well as any other object as t senses delectrc dscontnutes n metallc and nonmetallc objects. Fuson of GPR wth MD s expected to mnmze ts false alarm rate sgnfcantly. Also, automatc detecton based on one sensor s data would mnmze the detecton cost and tme. In ths research work, automatc detecton based on fuzzy fuson algorthm of both MD and GPR features as well as automatc detecton based on a fuzzy template matchng algorthm of GPR Fg. 1. Metal detector manpulaton data, for APM detecton n a shallow subsurface, are presented. As a smulaton for a completely automated demnng process, both MD and GPR are robot manpulated, Fgures 1 and 2. Most sensors for the detecton of bured landmnes are nfluenced by sol propertes ncludng the sol texture and water content. Uncertanty s nevtable assocated wth most sensors as clarfed n [3]. Uncertanty n sensor measurements for most of land mne detecton decson makng systems s normally modeled as probabltes or confdence factors, [4]. Sometmes the sensor measurements and/or decsons outputs take on a hgher level of uncertanty. For example the reflected ntensty by a bured object s hgh or the locaton of detecton s about the spatal ISBN:

2 coordnates (3, 4). In these cases t s would be more approprate to model the values not as numbers but as fuzzy sets. It s well known that fuzzy logc based approaches are powerful n representng measurements wth uncertanty, [5], lke the problem n hand. For fuson based decson-makng system for mne detecton applyng MD and GPR sensors, the fuson rule can be descrbed n smple terms usng sentences of a natural language. Fuson rules would be expressed as: IF the MD feature s hgh and the GPR feature s hgh THEN there s a Land mne and ts depth s medum. Even though an expert would easly do desgnng fuzzy fuson rules for smple cases, [6], t would be very dffcult to be desgned by a normal operator or a demner. It would be better for a demner to perform a seres of steps to extract these fuzzy fuson rules whle workng n the feld. The fuzzy fuson rules would be more relable to be extracted through learnng for an-envronmental dependent problems lke the problem n hand. In ths paper, fuzzy fuson rules learnt from expermental wll be presented. The appled learnng algorthm s smlar to that presented n [7], and appled by the authors n a dffcult modelng problem for a hydraulc parallel lnk manpulator control, [8]. On the other hand, Ground penetratng radar (GPR) provdes a promsng technque, for sensng objects underground, based on delectrc propertes. It s expected to be a good alternatve sensor for landmne detecton. The output of the sgnal processng algorthms applyng a stepped frequency GPR s a spatal dstrbuton of subsurface reflectvty, [9]. The spatal dstrbuton of subsurface reflectvty s normally presented as C- scan mages. The common scenaro for landmne detecton applyng a stepped frequency GPR s manual nspecton of those output mages by a demner, [10]. The demner looks at the processed mages and compares many dfferent mages and fnally takes a decson f there s a landmne or not. The decson s based on the demner s experence and hs cleverness. It s not only a dffcult but also a tme consumng task. There s a need to automate decson makng of landmne avalablty to save efforts as well as tme, [11]. The need for automatng decson makng of landmne avalablty has motvated researchers to apply some computatonal methods on sensed data. Hasegawa et al, [2], have appled a 2D template matchng method stepped frequency GPR data. Realzng that t s dffcult to determne the sgnal attenuaton n practcal stuaton, they ntroduced an adaptve template Fg. 2. GPR-manpulaton matchng procedure to compensate for sgnal attenuaton at dfferent depths. However, ther ntroduced results show a probablty of false alarm (PFA) more than 60%. In ths work, an automatc decson makng for ant-personal landmne detecton from a stepped frequency GPR processed data s presented. The proposed method, based on 3D fuzzy template matchng, s composed of manly three steps: 1- the choce of 3D reference template; 2- obtanng a 3D fuzzy template; 3- fuzzy matchng based on a fuzzy smlarty measure. Ths paper s organzed as follows: the expermental system ncludng the appled sensors, GPR and MD, are presented n secton 2. Fuzzy fuson algorthm of both MD and GPR features and ts expermental evaluaton s presented n secton 3. The 3D fuzzy template matchng based algorthm of GPR data and ts expermental evaluaton s presented n secton 4. Conclusons and prospects are presented n secton 5. 2 Expermental System In ths secton, robot-manpulaton system and the expermental test feld are presented. The appled sensors, metal detector and ground penetratng radar, are specfed. 2.1 Robot-Based Manpulaton of Sensors It s mportant to replace a human demnng task by an automated robot task. As a smulaton of a complete automated demnng process, a sx-degree of freedom seral manpulator of type PA10-7C, manufactured by Mtsubsh Heavy Industres, Japan, s appled n ths study as a manpulator of sensors. Manpulator based scannng facltates a regular step scannng better than a manual based scannng, whch leads to better sgnal processng results. Another advantage s the safety acheved by automatc scannng as an operator can do hs task from a remote place. ISBN:

3 PA10 manpulator holdng a metal detector and ground penetratng radar are shown n Fgures 1 and 2 respectvely. Avodng the manpulator sngularty ponts, t was possble to desgn the same path for both sensors. The test feld s a tank full of dry and homogeneous rver sand, as shown n Fgure 1 and 2. Its water content s 4.0 %, (relatve permttvty of about 3.29). EM wave absorber covers all the sdewalls and the bottom of the tank to suppress the tank walls reflecton durng GPR measurements. A dummy land mne of type PMN2 s the appled one for demonstratng the methodology of ths study, Fgure 3b. It has the same delectrc constant and the same metal content as the real one. Its dameter and heght s 122 mm and 54 mm, respectvely. The feld s relatvely flat and both the GPR antenna and MD sensng head scanned n a path parallel to the surface wth a gap between the sensor head and the ground of 10 mm. GPR system and MD are shown n Fgure 3. The stone shown n Fgure 3c s used for evaluaton. 2.2 Ground Penetratng Radar Sensor A three-element vector stepped-frequency GPR system, Fgure 3a, developed by Mtsu Engneerng and Shp Buldng Company, (Japan), s appled n ths study. It s an ultra-wde bandwdth vector type GPR. Its frequency bandwdth s MHz 2.0 GHz. Its frequency s changed n 256 steps. 2.3 Metal Detector Sensor A dual frequency metal detector of type MINEX 2FD 4.500, Fgure 3d, manufactured by Forster, (Germany), s appled. The operatng prncple s based on contnuous wave technque, comprsng a transmtter col and two symmetrcal recever cols n a gradent arrangement. The transmtter col sends one sgnal contnuously at two frequences. As a result of the nducton effect n a conductng object and ts return effect on the col system, the col mpedance changes. Ths change s evaluated and returned n the form of an acoustc sgnal. In the current measurement system, the output sgnal s acqured through a drect wrng nterface. 3 Fuzzy Sensor Fuson Algorthm In ths secton, an automatc sensor-fuson based detecton algorthm of an ant-personnel land mne s presented. A feature n-decson out fuzzy sensor fuson algorthm for a ground penetratng radar, (GPR), and a metal detector, (MD), for ant- (a) (b) (c) (d) Fg. 3. Expermental system components: a) a GPR system, b) a dummy PMN2 AP landmne, c) a stone and d) a MD. personnel landmne detecton s ntroduced. The nputs to the fuzzy fuson system are features extracted from both GPR and MD measurements. The output from the fuzzy fuson system s a decson f there s a land mne and at what depth t would be. Fuzzy fuson rules are extracted from tranng data through a fuzzy learnng algorthm. Expermental test results are presented to demonstrate the valdty of the proposed fuzzy fuson algorthm and hence ts nfluence n mnmzng the false alarm rate for humantaran demnng. 3.1 Features Extracton Sgnal processng for both GPR and MD sgnals wll be presented here. Extracton of GPR as well as MD features from the processed data s also presented. Scannng s descrbed frst, followed by GPR processng and MD processng Scannng Durng the experments, the scannng area s 400x500 mm2. The manpulator movement s n 20 mm steps n both Y-Z drectons comprsng a grd of 26x21 measurement ponts by both GPR and MD. The scannng path s as shown n Fgure GPR Processng GPR sgnal processng ncludes two man steps to obtan an mage spatal dstrbuton from whch reference template s extracted. These steps are: ISBN:

4 Z Manpulator base coordnates Ob 500 mm Y Y Drecton of moton 400 mm Fg. 4. Scannng path local average subtracton and mgraton. The output of the processng stage s C-scan slces lke that shown n Fgure 5. A local average subtracton s appled for better clutter suppresson. The local average subtracted sgnal () t s gven by ( t ) ( t ) ( t ) where () t s the raw data and () t s local average sgnal at sensng pont ; 1 ( t) k ( t) where K s a set of sensng n kk ponts n the neghborhood of sensng pont and n s the number of members of K. A tme seres sgnal s obtaned through Inverse Fast Fourer Transformaton. A 3-D GPR spatal sgnal s reconstructed from the tme sgnal. Krchhoff mgraton s adopted to reconstruct the spatal dstrbuton of subsurface reflectvty from a set of tme seres sgnals acqured on the ground surface by three-element vector radar. The krchhoff mgraton algorthm gves the output wave feld () r at a subsurface scatter pont of poston r ( x, y, z ) T from the nput wave feld, mt, r whch s measured at the surface s TR sensed ponts of poston r ( x, y, z ) T for a Z s s s s sgnal mode m wth transmt on tmet TR. For 3- mode vector radar, the soluton used n mgraton s gven by: 2 1 cos cos ( r) 2 s, m, ttr s, m, ttr da 2 S r r m 0 r vr t (1) where v s the RMS velocty at the scatter pont and r s the dstance between the nput pont and the scatter pont. cos s the oblquty factor, 1 vr s the sphercal spreadng factor. The summaton s to nclude effect of the three modes. For more detals about GPR sgnal processng, reader s advsed to refer to [12]. Fg. 5 C-scan of GPR processed data MD Processng The captured MD tme-doman sgnal s transformed nto frequency doman then the peak around the workng frequency s captured. Ths sgnal s reformed to the cumulatve sum n X-D, Fgure 6, so as to make t easer n decdng the poston of scanned object whch s drectly at the peak. We take maxmum of ths cumulatve sum as an MD feature, [13]. Cumulatve sum, CS at pont, s defned as: CS y I (2) x, y x 1,..., x max y 1 It s the summaton of the ntensty, I x, y from the ntal pont y 1 to the current pont y n y drecton. That s to be repeated for all values of x. As n Fgure 6, the peak ampltude as well as ts poston can be easly extracted. 3.2 Fuzzy Fuson Rules Learnng We use a fuzzy rule base for fuson of both GPR and MD sensors for humantaran landmne detecton. The learnng algorthm apples Wang- Mendel method, [7], for fuzzy rule learnng from expermental data. Fg. 6. Cumulatve sum of metal detector sgnal ampltude. ISBN:

5 3.2.1 Learnng Fuzzy Rules from Expermental Data The chosen algorthm for our study, to learn rules from expermental data, s a smplfed fuzzy algorthm. It presents three characterstcs that make t a good choce n vew of our objectves: smplcty, smple one-pass to extract the rules, and flexblty wth fast computatonal tme to operate n a demnng system. Also, t s possble to collect the learnt rules from numercal data as well as heurstc rules n the same frame of work whch may be needed n future development of the current work. Ths learnng algorthm s developed and appled to a dfferent applcaton, [8]. Fuzzy rules are frst learnt from examples then the number of assocated membershp functons for every varable s optmzed for the all learnt rules. The best group of rules expressng data s then selected based on the overall average rules truth degree. Fuson rules can be extracted n the same way as shown n Fgure 7. The output learnt fuson rules would be as n Table 1 MD measured data MD Processng MD features landmne depth Learnng algorthm Fuzzy rules GPR measured data GPR Processng GPR features Fg. 7. Fuzzy rules learnng algorthm for MD and GPR sensors fuson Expermental Evaluaton A decson makng system, Fgure 8, s proposed for evaluatng the learnt fuzzy fuson rules. The nputs of the decson makng system are the extracted features for both MD and GPR, the features postons as well as the learnt fuzzy fuson rules. A tested object should fulfll three condtons to be decded as a land mne: 1) poston of features of both GPR and MD should be near from each other. MD feature poston, ( MD _ Pos ), to be near from GPR feature poston, GPR _ Pos, s defned as the followng crsp expresson wth a specfc offset. MD _ Pos Offset GPR _ Pos MD _ Pos Offset (3) In the decson makng algorthm, the offset s chosen to be the land mne radus, 2) the object MD Varable Table 1. Learnt fuzzy fuson rules after nterpolaton B1 B2 B3 B4 B5 Fuzzy fuson rule base A1 C3 C2 A2 C3 C1 GPR Varable A3 C9 C8 C5 C4 A4 C7 C6 MD feature, GPR feature A5 C7 C6 Fuzzy rule base assocaton decson makng system A6 C7 C6 Decson Fg. 8. Decson makng system A7 C6 Features postons should be detected a landmne suspect by a GPR. It means that GPR feature should be assocated n the learnt fuzzy rule base, 3) the object should be detected as a land mne suspect by MD too. The MD feature should be assocated wthn the fuzzy rule base. Three tests are carred out wth dfferent objects. The frst object s the dummy land mne at a depth dfferent from that specfed n the tranng phase, Fgure 9a. The second object s a plastc case havng the same shape of a land mne n whch a metal object, (bolt), s nserted, Fgure 9b. The thrd object s a metal bolt only. The three objects can be sensed by both metal detector and ground penetratng radar. Each object s scanned by both MD and GPR. Data s processed and the features as well as ther postons are obtaned. The features, ther postons, learnt fuson rules for a specfc tested object are nput to fuzzy decson makng system, Fgure 8. The postons of landmne suspect features are checked frst. If they were near from each other accordng to defnton of (3), the decson makng system proceed for fuzzy fuson. (a) (b) Fg. 9. Expermental objects: a) specfyng depth of a dummy landmne, b) a plastc case wth a landmne shape. ISBN:

6 The features are fuzzfed wth the same membershp functons obtaned n the learnng phase. The fuzzfed values are compared wth the fnal learnt fuson rules of Table 1 through an mplemented MATLAB program. It s based on nput fuzzy sets assocaton, [14]. If there s an assocaton then there s a PMN2 landmne and ts depth s the output fuzzy rule. If there s no assocaton for any of the features, t means that the object s not the specfed PMN2 landmne. Performance The proposed algorthm could easly classfy the frst object, (the dummy land mne), and expect ts depth to be around 2.3 cm (ts surface was actually at a depth of 2.5 cm). The second object, (a case wth an nserted metal bolt), as well as the thrd object, (a metal bolt only), could be classfed as a non-land mne object. The second object was detected as a land mne suspect wth GPR but not a land mne suspect wth MD. There was no assocaton of the MD feature. Also, the thrd object was not detected as a landmne suspect wth ether MD or GPR. There was no assocaton of both MD feature and GPR feature. The fulfllment of the decson makng condtons as well as the fnal decson are shown n Table 2, where O means the condton s fulflled and X means the condton s not fulflled. Object Table 2. Fuson-based detecton results Dummy land mne Plastc case + a metal bolt A metal bolt Features poston Feature Fulfllment MD feature GPR feature O O O O X O O X X Decson A land mne at an expected depth Not a land mne Not a land mne 4 Fuzzy Template Based Automatc Landmne Detecton from GPR Data In ths secton, a 3D fuzzy template based antpersonal landmne automatc detecton from GPR data s presented, Fgure 10. A 3D template s chosen and a 3D fuzzy template s desgned. The Reference GPR Sensor Data for PMN2 at dfferent depths Feld GPR Sensor Data for PMN2 at other depths And for a stone GPR Processng 3D Templates Fuzzy Set desgn Algorthm Search for 3D Data GPR Processng of a landmne suspect 3D Templates 3-D Fuzzy Template Matchng Algorthm 3D Data of Template Sze Possblty of a land mne Fg. 10 Proposed algorthm for automatc landmne detecton from GPR Data choce of the 3D template s decded based on smooth changng poston of the maxmum ampltude at every C-scan as well as the threshold of ts background average ntensty. The 3D fuzzy template s extracted from 3D template crsp data. A data pont n the 3D fuzzy template s expressed as a trapezodal fuzzy set whch s extracted from expermental data. Landmne smlarty for both the 3D template as well as the learnt fuzzy template s examned by a crsp smlarty measure and a fuzzy smlarty measure respectvely. The cross correlaton s appled as a smlarty measure n crsp case whle a membershp degree of a fuzzy set s appled as a smlarty measure, n the fuzzy template case. Results of smlarty applyng both methods for automatc landmne detecton from GPR processed data are presented. 4.1 Three-Dmensonal Reference Template Scannng an APM applyng GPR and processng ts data presented n secton.1, 3D reference template s chosen based on smooth changng as well as repeatablty of horzontal poston of the maxmum ntensty ampltude. The horzontal poston of peak ampltude pont dffers from one slce to another, Fgure 11. Accordng to the smooth changng of the peak postons from slce to the above or lower slce, the depth range of data s chosen. Defnng the poston of maxmum ampltude wthn the specfed 3D mage to be ( xmax, y max ), and the poston of the peak wthn a gven slce to be ( x, y ), the selected mage volume havng the characterstc of smooth changng of peak poston from one slce to the above or lower slce s governed by the followng: x x x x x, max 1 max offset y y y y y (4) max 1 max offset where x offset and y offset s a prescrbed offset n both x and y drectons respectvely. In the current evaluatng algorthm these offsets are chosen to be 2 cm n both drectons. ISBN:

7 Fg. 11 Spatal poston of ntensty peak ampltudes The repeatablty of horzontal poston of C- scans peaks for the chosen depth range s shown n Fgure 12. The color of the fgure expresses the repeatablty. It s easly extracted that maxmum probable horzontal poston for the selected data range s at (23, 23 [cm, cm]). Other probable peaks are to the neghborhood of the maxmum probable peak poston. Smooth changng of peak postons are rechecked agan, (applyng equatons of (4)), and the fnal depth range of data s chosen. To obtan a 3-D reference template of a landmne, a threshold at the background average ntensty at dfferent slces s appled. The result s a 3D reference template as shown n Fgure D Fuzzy Template Havng obtaned a 3D template, Fgure 13, dfferent templates at varous landmne depths can be obtaned. The 3-D fuzzy template s extracted from dfferent 3D templates. The dfference between a fuzzy template and a crsp one s that the element of a fuzzy template s a fuzzy set. To extract a fuzzy template, GPR scans of a dummy landmne s executed at dfferent depths. Some of the GPR-data are appled for defnng the fuzzy sets and hence obtanng the 3D fuzzy template whle the rest of Fg. 13 Three dmensonal template GPR-data are appled for checkng the fuzzy template. A trapezodal fuzzy set, Fgure 14, has been chosen to express analogous elements, (.e. smlar spatal poston), of the dfferent crsp 3D templates. The defnng parameters of the trapezodal fuzzy set, a, b, c and d are estmated from expermental GPR-processed data for a landmne scanned at dfferent depths. Defnng the ntensty to be I j ( x, y, z ) at a gven spatal poston ( x, y, z ) n a jth template, then: max,, mn j,,, 1,2,...,,, max j,,, 1,2,... max, c x, y, z b x, y, z b x y z b x y z I x y z j j c x y z I x y z j j a x, y, z,,, 2 j 1,2,... j max,, b x, y, z c x y z d x, y, z c x, y, z, 2 j 1,2,... jmax (5) Defnng trapezodal functon parameters, the fuzzy set at every spatal poston of the template can be defned and 3D fuzzy template s obtaned. At a specfc spatal poston, x, y, z, of the 3D template, the ntensty, I x, y, z, would be expressed as: w A ~ Fg. 12 Repeatablty of poston of maxmum ampltude ponts n the selected range 0 a b c d Fg. 14 Intensty fuzzy set X ISBN:

8 x, y, z s THEN,, A x, y, z ;,, IF poston s I x y z s A x y z s a trapezodal fuzzy set whose parameters are defned as above. The output of ths stage s a 3D fuzzy template. Even though only fuzzfcaton of ntensty varable s appled here, the spatal poston would be fuzzfed n future as well. 4.3 Template Matchng Template matchng s the process of fndng the locaton of a sub mage, called a template, nsde an mage. Template matchng nvolves comparng a gven template, (reference template), wth wndows of the same sze n an mage and dentfyng the wndow that s most smlar to the reference template. In the followng, dfferent smlarty measures appled n ths study are revewed, and then, smlarty results are presented Smlarty measures Dfferent smlarty measures are adopted n the lterature for both crsp and fuzzy template smlarty evaluaton. Smlarty measures for crsp template would be sum of absolute dfferences, cross correlaton coeffcent, geometrc dstance, nvarant moments and others. Knowng that cross correlaton coeffcent s resstant to some ntensty dfferences between mages we apply cross correlaton coeffcent as a smlarty measure n crsp template case. Denotng the template by f 1 and an mage by f 2, and assumng the sze of the template s n 1 m 1 p 1 and the sze of the mage s n 2 m 2 p 2, where n n, m m, p p. Applyng a smlarty measure, we wll get and ntermedate mage, called a smlarty mage denoted bys c. Entry ( x, y, z ) n the smlarty mage s ndcatng the smlarty between the template and the wndow of the same sze at locaton ( x, y, z ) n the mage. Smlarty mage s c wll be of sze n2 n1 1 m m 1 p p Cross correlaton coeffcent s defned by, [15] N sc ( x, y, z ) D1 D2, (6) where n m p N f (, j, k ) f ( x 1, y j 1, z k 1) 1 j 1 k n m p 2 D1 f (, j, k ) j k n m p 2 D2 f ( x 1, y j 1, z k 1) j k ( x, y, z ) expresses the coordnates of the front upper left corner of a 3D wndow n GPR mage. In ths formula, the denomnator s a normalzaton factor. As the template and the wndow become more smlar, s c becomes larger. Accordng to the above formula and because the ntensty s only postve values, sc wll have a value between 0 and 1. On the other hand, there are many works on the smlarty measures for fuzzy data. These works nclude the smlarty of two elements n a fuzzy set, smlarty between fuzzy sets and smlarty of an element to a fuzzy set, [16]. The algorthm adopted to calculate the fuzzy smlarty measure s shown n Fgure 15. For a 3D template of a mne suspect, a membershp degree n ts approprate fuzzy set, (be known from the 3D fuzzy template), s calculated. Then the fuzzy smlarty measure, S f, s expressed as the average of membershp degrees of a template elements as follows: max, j max, k max S ( x, y, z ) / N ; (7) f j k, j, k 1,1,1 N : number of elements 4.4 Smlarty Results Both 3D crsp and fuzzy templates are appled for matchng GPR mages. Images for a dummy landmne at dfferent known depths and of a stone at a specfc known depth are tested. The matchng algorthm s shown n Fgure 10. The smlarty results presented here are based on cross correlaton coeffcent for 3D crsp template and fuzzy smlarty for fuzzy template case. Both the 3D crsp template and fuzzy template are extracted as explaned above. X Z w ( x, y, z) 3D Template of a mne suspect A( x, y, z) 0 Y a b c d 1/2 X X Z Y 3D Fuzzy Template Fg. 15 Calculatng a membershp degree of an element n a 3D template 1/2 ISBN:

9 4.4.1 Smlarty results n crsp case Applyng the matchng algorthm, we would obtan a value for smlarty for matchng two template at a gven spatal poston. The output wll be a 3D smlarty matrx. For smplcty, the smlarty results at a specfc slce of the scanned mages, (that s the slce number 9), are presented. That slce ncludes the maxmum smlarty results. Cross correlaton smlarty results are shown n Fgures 16 and 17 for the mages of a stone and a landmne prototype respectvely. The reference template n all cases s that of the landmne prototype template presented n secton 2. Smlarty s hgh as 80% for a stone and as 90% for a dummy landmne. The cross correlaton smlarty results show the confuson of a stone as f t s a suspected landmne. There s no clear dscrmnaton between a landmne and a stone result. scanned at 7 dfferent depths. The data of 4 depths are appled for extractng the fuzzy template. The data of the other 3 depths are appled for testng the extracted fuzzy template. Also the data of scannng a stone, Fg. 3c, s appled for checkng the valdty of the template and the matchng algorthm for dscrmnatng a landmne from a stone object. These testng smlarty results are shown n Fgure 18. The presented result s the maxmum smlarty value for each case, (a landmne at three dfferent depths and a stone). Fgure 18 shows that smlarty value n case of a landmne s more than 80%, whle for a stone case t s about 32%. Ths result shows that applyng the fuzzy template and the proposed matchng algorthm, t s possble to dscrmnate a landmne from a stone. It would enhance the capablty for automatc detecton of landmnes and decrease the false alarm arte Lanmne prototype Stone Smlarty Fg. 16 Crsp smlarty results for a stone Object depth, [cm] Fg. 18 Fuzzy smlarty results for a dummy landmne at dfferent depths and a stone Fg. 17 Crsp smlarty results for a dummy landmne Smlarty results n fuzzy case The matchng algorthm, shown n Fgure 10, wth the fuzzy template desgned n secton 4.2 s appled n ths evaluaton. A dummy landmne has been 5 Conclusons In ths nvted paper, two fuzzy decson makng algorthm for APM detecton are presented. The frst s based on fuzzy fuson rules learnt from measured data features. The prescrbed features for both GPR and MD were enough n expressng a land mne through fuson. The proposed fuson method s easy to be mplemented n a real feld and easy to be executed by a normal operator. It was possble to dfferentate between a land mne and other objects whch would mnmze the false alarm rate sgnfcantly. The second s based on fuzzy template matchng. The smlarty results of a stone and a landmne prototype show the promse of the 3D fuzzy template matchng applyng a fuzzy ISBN:

10 smlarty measure. In contrast to a cross correlaton smlarty measure, a landmne suspect can be dfferentated from a stone applyng the proposed algorthm wth a 3D fuzzy template matchng and a fuzzy smlarty measure. Prospects: A more general template for a group of landmnes, (havng smlar characterstcs), would enhance the proposed automatc detecton method n a practcal applcaton. Acknowledgment The frst author would lke to thank Japan Socety for Promoton of Scence, JSPS, and Tanta Unversty of Egypt for lettng hm the chance to conduct ths research durng hs fellowshp program n Nagoya Unversty. References: [1] T. Fukuda, K. Yokoe, Y. Hasegawa and T. Fuku, Land mne detecton algorthm usng ultra wde band GPR, Proceedngs of the 1st Internatonal Symposum on Systems and Human Scence, pp , [2] Y. Hasegawa, Y. Kawa, K. Yokoe, and T. Fukuda, Automatc extracton for mne suspects from GPR, Proceedngs of IARP Internatonal Workshop on Robotcs and Mechancal Assstance n Humantaran Demnng (HUDEM2005), pp , June [3] R. L. Van Dam, B. Borchers and J. M. H. Hendrckx, Strength of landmne sgnatures under dfferent sol condtons: mplcatons for sensor fuson, Internatonal Journal of Systems Scence, Vol. 36(9), pp , [4] S. Auephanwryakul, J. M. Keller, and P. D. Gader, Generalzed choquet fuzzy ntegral fuson, Informaton Fuson, Vol. 3, pp , [5] L. A. Zadeh, Outlne of a New Approach to the Analyss of Complex Systems and Decson Processes, IEEE Transactons on Systems, Man, and Cybernetcs, Vol. SMC-3, No. 1, pp , January [6] P. D. Gader, M. Keller, and B. N. Nelson, Recognton technology for the detecton of bured land mnes, IEEE transactons on fuzzy systems, Vol. 9, No. 1, pp , [7] L. X. Wang and J. Mendel, Generatng fuzzy rules by learnng from examples, IEEE Trans. Syst., Man, Cybern. Vol. 24, pp , Feb [8] Z. Zyada, Y. Hasegawa, G. Vachkov and T. Fukuda, Implementng fuzzy learnng algorthms n a 6 DOF hydraulc parallel lnk manpulator: actuators fuzzy modellng, Journal of Robotcs and Mechatroncs, Vol. 14, No. 4, pp , [9] T. Fukuda, Y. Hasegawa, Y. Kawa, S. Sato, Z. Zyada and T. Matsuno, Automatc landmne detecton system usng adaptve sensng wth vector GPR, Proceedngs of 32nd Annual Conference of IEEE Industral Electroncs Socety, IECON06, Pars, 7-10 Nov. 2006, pp [10] M. Sato, J. Fujwara, X. Feng, Z. Zhou and T. Kobayash, Development of a hand-held GPR MD sensor system (ALIS), Proceedngs of SPIE-the Internatonal Socety for Optcal engneerng, Vol. 5794, pp , June [11] Z. Zyada, Y. Hasegawa, T. Matsuno and T. Fukuda, Fuzzy sensor fuson for humantaran demnng, Journal of Advanced Computatonal Intellgence and Intellgent Informatcs, Vo. 11, No. 7, [12] T. Fukuda, Y. Hasegawa, Y. Kawa, S. Sato, Z. Zyada and T. Matsuno, GPR sgnal processng wth geography adaptve scannng usng vector radar for ant-personal landmne detecton, Internatonal Journal of Advanced Robotc Systems, Vol. 4, No. 2, pp , [13] Z. Zyada, Y. Kawa, T. Matsuno and T. Fukuda, Fuzzy sensor fuson for mne detecton, Jont 3rd Internatonal Conference on Soft Computng and Intellgent Systems, 7th Internatonal Symposum on Advanced Intellgent Systems, SCIS-ISIS 2006, Tokyo, pp [14] J. Yen, Fuzzy logc-a modern perspectve, IEEE transactons on knowledge and data engneerng, Vol. 11, No. 1, pp , [15] W. K. Pratt, Correlaton technques of mage regstraton, IEEE Transactons Aerospace and Electronc Systems, Vol. 10, No. 3, pp , [16] S. J. Chen and S. M. Chen, Fuzzy rsk analyss based on smlarty measures of generalzed fuzzy numbers, IEEE Transactons on Fuzzy Systems, Vol. 11. No. 1, pp , ISBN: