Experimentation and Modeling of Soldier Target Search

Transcription

1 Calhoun: The NPS Insttutonal Archve Faculty and Researcher Publcatons Faculty and Researcher Publcatons 2009 Expermentaton and Modelng of Solder Target Search Chung, Tmothy H. Matthew Hastng, Tmothy H. Chung, and Paul Evangelsta. "Expermentaton and Modelng of Solder Target Search." Proceedngs of the Internatonal Conference on Industral Engneerng, Theory, Applcatons, and Practce. Anahem, Calforna,

2 Proceedngs of the 4th Annual Internatonal Conference on Industral Engneerng Theory, Applcatons and Practce Anahem, Calforna October 8-2, 2009 EXPERIMENTATION AND MODELING OF SOLDIER TARGET SEARCH Matthew Hastng, Tmothy H. Chung, and Paul Evangelsta 2 Department of Operatons Research Naval Postgraduate School Monterey, Calforna Tranng and Doctrne Command Analyss Center Monterey Naval Postgraduate School Monterey, Calforna Correspondng author s e-mal: matthew.hastng@us.army.ml, thchung@nps.edu, paul.evangelsta@us.army.ml Abstract: Ths paper nvestgates the vsual search process and the effect of contextual nformaton on the search process n an urban combat envronment. Hgh resoluton combat smulaton models mplement a parallel sweepng or wndsheld wper search process that s not representatve of human search behavor. Furthermore, combat models do not account for addtonal stuatonal awareness n the form of contextual nformaton. A dscrete myopc search model s proposed, a study of whch provdes a statstcal model based on human performance data. Ths model prortzes search effort where humans beleve that targets are most lkely to occur. Nneteen volunteers searched 6 statc urban scenes wth zero to fve targets. These data formed the probabltes that a target s located n each cell n each dscretzed scene. The dscrete myopc search model chooses the cell wth the hghest probablty for each dscrete look. Hypothess testng on expermental data revealed a nearly 20% ncrease n accurately predctng human search patterns usng the dscrete myopc search model over the wndsheld wper model. Further nvestgaton revealed a sgnfcant change n search behavor and detecton performance based on the addton of contextual nformaton. The major result of ths work ndcates that combat models need to bas search based on the stuatonal awareness of observer and propertes of the observer s envronment.. INTRODUCTION Search and target acquston (STA) n mltary models and smulatons attempts to abstract arguably the most mportant aspects of combat operatons. The process of a combatant observng another combatant, decpherng the combatant s propensty for hostlty, and then decdng whether or not to engage the combatant wth lethal force nearly summarzes ndvdual close order combat. The methods developed to represent ths process wll alter the outcome and valdty of a combat model. Modelng STA that occurs wth the assstance of a techncal devce such as a scope or radar sgnfcantly smplfes the modelng problem. Measured performance parameters of the techncal devce bound the STA process, and computer models can replcate these quantfable propertes. Representng human STA wth the unaded eye does not offer such a smple soluton. Human vson does not have easly bounded parameters. Although certan components of human vson are well understood, other components reman a mystery. Scentsts understand that humans perceve a fracton of the electromagnetc spectrum, extract meanng from color and shape, and see clearly at a two degree foveal pont but clarty degrades n the perphery. Other human vson phenomena are not entrely codfed. Consder the desre of a person to fxate at certan ponts wthn a scene but gnore others, antcpate threat or danger from partcular areas wthn vew but quckly surmse other areas as mpotent, or nterpret defned detecton goals from an envronment based on prevous nformaton or actvty. Psychologcal theores detal the lkely cogntve processes that lead to these types of behavors, but predctng human vsual behavor gven complex and natural vsual stmul remans an open problem (Doll and Home, 999, Jungkunz, 2009). Bruce et al. (2005) summarze vsual search as the product of two factors: the propertes of the surroundng envronment and the goals of the observer. A major flaw that currently exsts n combat models nvolves the applcaton of unbased search. The underlyng STA algorthm accepted and wdely used n many combat models can be traced to an experment performed by Johnson that essentally correlated and summarzed the dfference between target contrast and detecton by the observer (Johnson, 958). Ths experment, supported largely by the U.S. Army Nght Vson and Electronc Sensors Drectorate (NVESD), was developed to support modelng of aded eye detecton wth a restrcted feld of vew, such as lookng through a scope. The current detecton algorthm n most U.S. mltary combat models, known as ACQUIRE, mplements search patterns based Internatonal Journal of Industral Engneerng 495 ISBN #

3 Proceedngs of the 4th Annual Internatonal Conference on Industral Engneerng Theory, Applcatons and Practce Anahem, Calforna October 8-2, 2009 upon the search strategy recommended for the vson enhancng equpment that wll be modeled. Although there are recommended search strateges for the unaded eye, research supports that pre-attentve processng of envronmental surroundngs supported by human learnng, specfcally our ablty to aggregate and assocate to buld context, trumps any attempts to tran or control human search behavor (Medna, 2008, Hoffman, 998). The work presented n ths paper serves to motvate the problem of modelng unaded human search, specfcally appled to the task of target acquston n combat operatons. The prmary contrbutons of ths paper nclude () results from an experment conducted to explore search n combat envronments, and () dscusson and results from a model that refutes unbased search. An mportant aspect to remember throughout ths paper nvolves the task of model ntegraton. Impressve research has been performed n the area of STA, descrbed n an excellent survey by (Vaughn, 2006). Many of the models surveyed by Vaughn develop what appears to be a promsng approach to mprove the fdelty of STA representaton, however the careful reader wll notce shortcomngs. Most of these shortcomngs wll arse durng mplementaton, and Vaughn ponts out many of these. Examples of these shortcomngs nclude a requrement to nclude parameters or varables that are unreasonable to expect from current combat models, a requrement for free varables that preclude the ablty to generalze n closed form combat models, or computatonal complexty that creates an unacceptable processng burden. These advanced approaches need to be pursued n the hope and strong lkelhood that combat models wll contnue to progress and soon be able to accommodate ths level of complexty. However, t s also mportant that research and development efforts also focus on STA models that can make a near term mpact wth exstng combat models. In order for an STA model to make a near term mpact, strong potental for mprovement of STA representaton must be evdent and the modelng communty must be wllng to accept the representaton. The former requrement manfests from expermentaton and analyss; however, the latter requrement means that the proposed soluton should be mplemented wthn the exstng model archtecture and should not create an unreasonable processng burden. If these requrements are not met, even the best proposed solutons are unlkely to make a dfference n the near term. 2. AN EXPERIMENT TO MEASURE SEARCH BEHAVIOR IN TARGET ACQUISITION An experment desgned to measure target search n a combat envronment took place at the Naval Postgraduate School n December of The experment had a sngular objectve: determne the effect of stuatonal awareness (SA) on STA. Stuatonal awareness s a feld of research n and of tself (Endsley, 2000). Mller and Shattuck descrbe the effect of SA on decson makng as a dynamc model of stuated cognton. Ths model portrays data avalable as only a subset of ground truth, morphng data through lenses that synthesze and assocate nformaton based on ndvdual experence and local context, eventually becomng nformaton used to support a decson (Mller and Shattuck, 2006). Fgure. Ths cropped example scene has eye-trackng results overlayed. Ths partcular scene contaned an audble report referencng the car and a movng target behnd the car. Yellow dots represent hgh velocty eye movements (>2.5 per second), green dots represent low velocty eye movement (<2.5 per second), and crcles represent long fxatons, areas of sustaned low velocty eye movement for 20 µs or longer. Long fxatons are numbered n chronologcal order. 496

4 Proceedngs of the 4th Annual Internatonal Conference on Industral Engneerng Theory, Applcatons and Practce Anahem, Calforna October 8-2, 2009 The experment conssted of sxteen scenes presented to nneteen partcpants, and the desgn of experments (DOE) ncluded four bnary factors: a movng target, an audble report, a wrtten stuaton report (SITREP), and a mnmap. Each scene conssted of an urban envronment wth a unque feld of vew and array of structures. The movng target vares from degrees n sze, and t moved nto the scene for approxmately one second and then moved back out of the scene. The audble report conssted of a bref (5-0 seconds) report ndcatng a target locaton wth a semantc reference. The wrtten stuaton report was a sheet of paper that provded several bulleted remarks about the physcal nature of the envronment and lkely enemy courses of acton along wth an overhead schematc of the scene. Each scene lasted 20 seconds, and the partcpants ndcated detecton of a target wth a mouse clck on the target locaton. Eye trackng was recorded usng the Seeng Machnes FaceLab4 eye tracker. Partcpants sat 7 cm from a 24 nch TFT montor set to 60 Hz at a resoluton of 920 by 200 pxels. Only results from partcpants achevng an eye trackng screen calbraton error of one degree or better were ncluded n the analyss. An example scene along wth eye trackng results from a sngle partcpant s shown n fgure. Notce the strong center bas, presumably a result of the audble reference and movement, two basc aspects of SA ncluded as factors n ths experment. It s also nterestng to note the lack of nterest n the outer portons of the scene. Mnmal ambguty and close range contrbute to a person s ablty to pre-attentvely clear portons of scenes, resultng n the appearance of gnorng or payng mnmal attenton to a porton of the scene. Unambguous, uncluttered, close range components of scenes receved very lttle attenton durng ths experment. Assumng tme fxated represents areas of perceved threat, the perceved threat resdes n ambguous, cluttered areas and areas where external nformaton ndcated mmedate threat. Fgure 2. Ths cropped example scene has been overlad wth a heat map that reflects fxaton duraton aggregated over all the partcpants. Every fxaton from every partcpant that observed ths scene s shown, and a larger whte heat mark ndcates a longer fxaton. Clusters of fxatons renforces that search s not random. Consder the aggregated fxatons shown n fgure 2. Ths fgure renforces the concept that search s not random. Clusters of fxatons clearly form around wndows, edges of buldngs, and cluttered areas. Notce that there are entre rooftop areas and uncluttered wndows and doors where no fxatons occurred, suggestng that pre-attentve processng cleared these areas. Jungkunz renforces ths concept n hs thess wth an nterestng sngle target experment that systematcally vares a hdng locaton, clutter, target salency, and target eccentrcty (Jungkunz, 2009). 497

5 Proceedngs of the 4th Annual Internatonal Conference on Industral Engneerng Theory, Applcatons and Practce Anahem, Calforna October 8-2, APPLICATION OF A DISCRETE MYOPIC SEARCH MODEL Dscrete myopc search s a search technque that optmzes the probablty of detectng a target for each of k dscrete observatons. The gven search area s dscretzed nto n cells and a probablty that a target s wthn the th cell, denoted p, s assgned or derved from pror nformaton. Each successve look n cell has a probablty q of not detectng the target and ths probablty s assumed ndependent of all other looks. Gven k looks n the th cell, the model optmzes the n k n probablty of detecton p = = p ( q ) subject to k 0 and a constrant on = k K; k (0,,..., K) for n cells (Washburn, 2002). The model mplements a greedy algorthm n whch the k + st look s allocated to the cell wth the greatest ncrease to the overall probablty of detecton. The ncrease n probablty of detecton n the th cell s shown n equaton : k + k k p ( q ) p ( q ) = p ( q ) q () Ths mplementaton produces an optmal allocaton of dscrete looks because p ( k q ) s a concave functon of k (Washburn, 2002). A crtcal assumpton to ensure concavty and the optmalty of the resultng search pattern s the absence of false postve detectons. Gven the context n whch false alarms, such as engagng a cvlan, would ncur a prohbtvely hgh penalty, the assumpton of no false postve detecton errors s consdered reasonable. Further research, ether psychophyscal or otherwse, s left for future study. Common applcatons of a myopc search model nclude antsubmarne warfare and Coast Guard search and rescue. A hstorc example s the search for the sunken USS Scorpon n 968. Searchers constructed a pror probablty map as a composte of nne separate prors. Searchers developed the nne prors based on dfferent scenaros explanng how and where the snkng occurred (Wagner et. al, 999). Ths thess proposes a novel applcaton of ths model n the form of vsual search. Ths study slghtly modfes the model to accommodate zero to sx targets. Normalzaton of the pror probablty map ensures that the sum of probabltes n all cells equals one. If detecton occurs, the cell or cells n whch the detected target resdes are elmnated from future consderaton. Ths change allows the model to fnd the optmal search pattern wth multple targets. Dscretzaton of each scene nto approprately szed cells s the frst step n mplementng the Dscrete Myopc Search model. Dscretzaton allows for nvestgaton of each partcpant s fxatons and provdes some robustness to error n the eye-trackng hardware. The sze of each cell s determned by the average fxaton sze across all partcpants for each 2 n scene. Ths number of pxels n each cell s fx where fx = fx _ sze n = / for n fxatons n a scene across all partcpants. Fxaton sze s determned by the tme duraton of the fxaton but translates to a number of pxels, each pxel representng 6µs of low velocty eye movement. For example, every green pxel n fgure represents 6µs of low velocty eye movement. Each scene has an average fxaton sze of 76 pxels, resultng n 375 square cells. The objectve of ths research s to develop a model to predct an ndvdual s search pattern. The hypothess s that an ndvdual wll search n a manner n whch to optmze hs ablty to detect targets durng each successve fxaton. He prortzes hs fxaton pattern based on where he beleves targets wll most lkely be located. Ths also corresponds to proposed schemes, such as salency-based methods for vsual attenton n whch the scan pattern s prortzed based on decreasng salency (Itt and Koch, 200). Snce partcpant fxatons were used to determne the pror probablty map, t s necessary to prortze the fxatons usng weghts. Search performance s not constant even over a short perod of tme (Cooke, 983). Ths rate of change s not well defned, but can be estmated from the expermental data. As a partcpant locates targets or fals to locate targets, hs belef that targets reman n the scene decreases. 4. EXPERIMENTAL RESULTS There s one p value and one q value for each cell n the dscretzed mage resultng n two matrces, P and Q. Thrteen of the nneteen partcpant data sets were used to buld P and Q and data from sx partcpants was reserved to test the model - essentally measure the ablty of the model to generalze. A comparson between the dscrete myopc search model and wndsheld wper search should reveal whether or not search s an unbased actvty. Strong performance of the dscrete myopc search model ndcates smlar search patterns across a sample of partcpants. If stmul and nformaton 498

6 Proceedngs of the 4th Annual Internatonal Conference on Industral Engneerng Theory, Applcatons and Practce Anahem, Calforna October 8-2, 2009 bases search patterns, smlar search behavors wll result and the dscrete myopc search model mplementaton proposed n ths paper wll outperform wndsheld wper search, n terms of better predctng true human vsual search behavor. The probablty of a target n cell, p, s based upon data taken from fxatons of half of the subjects. Fxaton duraton n each cell, aggregated across all partcpants, generates p. The order of fxatons s also ncluded n ths model. Analyss of detecton statstcs ndcated that most of the detectons occurred n the frst few seconds of scene presentaton, and t s mportant to nclude ths result n the constructon of P. Analyss of the data revealed that target detecton over tme followed a postve skewed gamma dstrbuton, ndcatng that very early fxatons were unsuccessful, quckly followed by spke n successful fxatons, then degradng exponentally over tme. Weghtng fxatons based on when they occurred n the scene resulted n hgher p values n the cells where early fxatons tended to occur. Normalzaton of the weghted aggregate fxatons results n p summng to unty across all cells for a gven scene. Detecton statstcs buld the probabltes assgned to q. The probablty of not detectng the target gven a look n cell, q, s derved from detecton data taken from the same subjects. q s the probablty that a target was not detected n cell gven a fxaton n cell. The focus of ths research s the modelng of the search process and not detecton performance. It s paramount to ths study that the detecton probabltes are consstent for each model so that detecton order and not detecton performance forms the bass for comparson. The tranng data from thrteen partcpants provdes the nputs mplemented by the models below. The desred outcome s not to predct the probablty of detecton for the test set, but to nstead predct the partcpants search patterns by examnng the order n whch partcpants locate targets. Gven that ths s the goal of ths research, measures of effectveness (MOEs) become a bt more complcated. Two MOEs are consdered, and both focus on target detecton order. The frst MOE (MOE) s the probablty of detecton order gven that a detecton occurs. It s defned n formula 2 as the probablty of detectng target j n order k gven a detecton n the scene. Let d,j,(k) be a bnary varable that equates to one f partcpant detects target j n order k for a gven scene, else t equates to zero, and let T equal the total number of targets n a scene. MOE s defned n equaton 2. P(d,j,(k) = detecton) = T k= 3 = T j= d, j,( 3 = d, j,( (2) MOE2 (equaton 3) s the absolute probablty of detecton order for each target. It s the condtonal probablty of detectng target j n order k gven that target j s detected. T P(d,j,(k) = d ) = k=, j,( = k = 3 d, j,( = T 3 = d, j,( These condtonal probabltes could each be consdered the probablty of success n a bnomal process. Gven ths, t s possble to buld models from the tranng data and test model performance aganst the actual human performance n the test data. Both dscrete myopc search and wndsheld wper search were tested. Nether model performed exceptonally well, however there was a consderably better performance observed from the dscrete myopc search model. Usng the bnomal hypothess test at an alpha of 0.05, wndsheld wper search was rejected as an equvalent bnomal process 85% of the tme compared to the dscrete myopc search model whch was rejected 70% of the tme. Gven that ths percentage resulted from 000 teratons of both wndsheld search and the dscrete myopc search model, ths dfference s sgnfcant. Wth regard to the four factors ncluded n ths experment, the audble report created the most sgnfcant effect. Over 93% of the frst fve fxatons n scenes wth an audble report were drected at the target ndcated by the report. Although study contnues wth ths data, the other three factors do no appear to create a sgnfcant change n search behavor. (3) 499

7 Proceedngs of the 4th Annual Internatonal Conference on Industral Engneerng Theory, Applcatons and Practce Anahem, Calforna October 8-2, CONCLUSION Modelng human vson s a challenge. It s wdely accepted that nformaton, goals, experence, and envronmental condtons affect STA. Quanttatvely descrbng how these varables affect search and developng a concse method to mplement ths behavor n combat models remans as open research. (Doll and Home, 999) renforce many of these challenges and present recommendatons for future research that reman relevant ten years later. Doll and Home clearly state that wndsheld wper search s smply a poor assumpton. Ther dscusson renforces that scene structure (edges, corners, clutter) attracts attenton and temporal sequence, the same concept explored by the MOEs n ths paper, matters. However, ten years later wndsheld wper search s stll the domnant search method used n combat models. The dscrete myopc search model presented n ths paper llumnates several aspects wth regard to search. The most sgnfcant result that emerges from the analyss of ths model s that search s not random. Ths model ndcates that underlyng patterns wthn scenes drve search behavor. Uncoverng these patterns n a concse, generalzng form remans a challenge. Furthermore, quantfyng the effect of nformaton also remans as an open research topc. Smple, overt cues, such as gunfre or dsruptve nose, have long been known to affect attenton. The audble reports n ths study take a small step forward n cogntve analyss, requrng some level of nterpretaton from the subjects to apply the nformaton. It s the opnon of these authors that a holstc yet smple approach s necessary to mprove search behavor n combat models. Ths holstc approach wll lkely rse from smple roots, such as the two factors that (Bruce et. al. 2005) clam to shape search: the propertes of the surroundng envronment and the goals of the observer. Encodng of ths behavor must occur wth a smple yet emprcally supported method to garner support of the model development communty. Promsng avenues of research to address these open problems nclude representaton of pre-attentve processng, ncorporatng perpheral detecton, surmsng threat from nformaton, and extractng semantc meanng from physcal surroundngs (eg, hdng locatons). 6. ACKNOWLEDGEMENT Ths work would not have been possble wthout the support of the US Army Tranng and Doctrne Command Analyss Center (TRAC) and the Naval Postgraduate School. Sgnfcant expermental support was provded by Chrs Darken, Patrck Jungkunz, Larry Shattuck, and Nta Mller. Ths acknowledgement does not mply approval of the results n ths paper by these organzatons or ndvduals. The results and opnons n ths paper have been formulated and expressed solely by the authors. 7. REFERENCES Cooke, K. J. Modelng of Vsual Search Performance. Brtsh Aerospace Dynamcs Group Darken, C. and Jones, B. "Computer Graphcs-Based Target Detecton for Synthetc Solders", Proceedngs of Behavoral Research n Modelng and Smulaton, Doll, Theodore J. and Rchard Home. Lessons Learned n Developng and Valdatng Models of Vsual Search and Target Acquston. Proceedngs of the RTO SCI Workshop on Search and Target Acquston, Utrecht, Netherlands, June 999. Eaton, Jeffrey P. Analyss of the Effects of Dynamc Characterstc Dmenson Calculatons on FLIR Performance Predcton Models. Naval Postgraduate School Master s thess, March 997. Endsley, Mca R., Leonard D. Holder, Bruce C. Lebrecht, Danel J. Garland, Rchard L. Wampler, and Mchael D. Matthews. Modelng and Measurng Stuaton Awareness n the Infantry Operatonal Envronment. U.S. Army Research Insttute for the Behavoral and Socal Scences, Research Report 753, June Glumm, Monca M., Kathy L. Kehrng, and Tmothy L. Whte. Effects of Tactle, Vsual, and Audtory Cues About Threat Locaton on Target Acquston and Attenton to Vsual and Audtory Communcatons. ARL-TR-3863, August Glumm, Monca M., Kathy L. Kehrng, and Tmothy L. Whte. Effects of Vsual and Audtory Cues About Threat Locaton on Target Acquston and Attenton to Audtory Communcatons. ARL-TR-3560, August Hastng, Matthew D. Combat Smulaton of Indvdual Solder Search n Urban Terran. Naval Postgraduate School Master s thess, June Hoffman, Donald D. Vsual Intellgence. W.W. Norton and Company, New York, New York,

8 Proceedngs of the 4th Annual Internatonal Conference on Industral Engneerng Theory, Applcatons and Practce Anahem, Calforna October 8-2, 2009 Itt, L., and Koch, C. A Salency-based Search Mechansm for Overt and Covert Shfts of Vsual Attenton. Vson Research, 40, Johnson, J. Analyss of Image Formng Systems. Proceedngs of the Image Intensfer Symposum, pp , U.S. Army Engneer Research and Development Lab, Ft. Belvor, VA. (DTIC report AD ), Oct. 6-7, 958. Jungkunz, Patrck. Modelng Human Vsual Percepton for Target Detecton n Mltary Smulatons. Naval Postgraduate School Ph.D. dssertaton, June Mazz, John, Regna W. Kstner, and Wllam T. Pbl. Detecton of Low-Contrast Movng Targets. ADP00547, June 999. Medna, John. Bran Rules. Pear Press, Seattle, O Kane, Barbara L., Irvng Bederman, and Erc E. Cooper. Modelng Parameters for Target Identfcaton: A Crtcal Features Analyss. Proceedngs of the IRIS Passve Sensors Symposum, Monterey, CA, March 996. Ratches, James A., Rchard H. Vollmerhausen, and Ronald G. Drggers. Target Acquston Performance Modelng of Infrared Imagng Systems: Past, Present, and Future. IEEE Sensors Journal, Vol., No., June 200. Shattuck, Lawrence G., and Nta L. Mller. Naturalstc Decson Makng n Complex Systems: A Dynamc Model of Stuated Cognton Combnng Technologcal and Human Agents. Organzatonal Behavor: Specal Issue on Naturalstc Decson Makng n Organzatons. Vol. 27, No. 7, Vaughan, Barry D.. Solder n the Loop Target Acquston Performance Predcton Through 200: Integraton of Perceptual and Cogntve Models". ARL-TR-3833, July Vollmerhausen, Rchard H., Edde Jacobs, and Ronald G. Drggers. New Metrc for Predctng Target Acquston Performance. Optcal Engneerng, Vol. 43, No., November Wagner, D. H., Mylander, C. W., & Sanders, T. J. (999). Naval Operatons Analyss, 3 rd ed. Naval Insttute Press. Washburn, A. R. Search and Detecton, 4th ed. Insttute for Operatons Research and the Management Scences,