./msr-- MEASUREMEN SCIENCE REVIEW, Volume., No., Hidden Markov Model-based Pedesrian Navigaion Sysem using MEMS Inerial Sensors Yingjun Zhang, Wen Liu, Xuefeng Yang, Shengwei Xing Navigaion College, Dalian Mariime Universiy,, Dalian China Navigaion College, Dalian Mariime Universiy,, Dalian China, dmuliuwen@sina.com (corresponding auhor) In his paper, a foo-mouned pedesrian navigaion sysem using MEMS inerial sensors is implemened, where he zero-velociy deecion is absraced ino a hidden Markov model wih saes and observaions. Moreover, an observaions exracion algorihm has been developed o exrac observaions from sensor oupus; sample ses are used o rain and opimize he model parameers by he Baum-Welch algorihm. Finally, a navigaion sysem is developed, and he performance of he pedesrian navigaion sysem is evaluaed using indoor and oudoor field ess, and he resuls show ha posiion error is less han % of oal disance ravelled. Keywords: Pedesrian navigaion sysem, hidden Markov model, zero velociy deecion, inerial sensors. G. INRODUCION PS IS AN IMPORAN componen in he posiioning sysem and plays a key role in oudoor posiioning. However, GPS coninues o sruggle indoors due o he failure of saellie signals o penerae buildings []. Furhermore, recen developmens in he field of smar mobile erminals have led o an increased ineres in indoor posiioning and navigaion. In mos recen sudies, indoor posiioning and navigaion has been discussed in wo differen ways, one is Local Posiioning Sysem (LPS), and he oher one is Pedesrian Dead Reckoning (PDR) []. Compared wih LPS, PDR approach has a number of aracive feaures: auonomy, cos-effeciveness, no insalling markers or insrumenaion in advance []. Specifically, PDR is divided ino sride and heading sysem (SHS) and inerial navigaion sysem (INS) []. Pedesrian inerial navigaion sysem which is based on MEMS inerial sensors has gradually become an indoor navigaion soluion due o is independence, porabiliy, and low cos, and can be used in D navigaion environmen. Pedesrian inerial navigaion sysem widely adops a sysem framework ha is characerized by exended Kalman filering and srapping of MEMS inerial sensors on inseps, which is proposed by Foxlin []. he main problem of he sysem is error accumulaion caused by inerial sensor drif error [], [], []. A considerable amoun of lieraure has been published on his problem. Researchers found ha wo fee ouch he ground alernaely and say for a shor ime for approximaely. s- s in a normal walking cycle. he shor inerval is called he zero velociy inervals []. As he real velociy of foo in zero velociy inervals is zero, if a zero velociy sae is deeced, he velociy error can be eliminaed. his algorihm is called zero velociy updaes algorihm. herefore, zero velociy deecion is an exremely imporan par of he pedesrian navigaion sysem. I provides he required informaion o rese he velociy error. Zero velociy deecion algorihms can be divided ino wo kinds. One opion is based on he hypohesis ha he measured acceleraion is consan and equal o graviy and he measured angular velociy is zero in zero velociy inervals. A range of zero velociy deecion algorihms have been proposed based on inerial sensor oupus, where he main difference is he disposal mehod of sensor oupus and he compound modes of inerial sensors. In reference [], he zero velociy inervals are deermined based on inerial sensors (acceleromeers and gyroscopes) oupu norms. If he oupus are smaller han some hresholds for a predeermined ime, hen he zero velociy is decided. In reference [9], he zero velociy is deermined based on Z-axis acceleromeer and Y-axis gyroscope oupus. In reference [], he zero velociy is deermined based on gyroscope oupu norms. In reference [], [], he zero velociy inervals are deermined based on he variance of acceleromeer values. In reference [], he zero velociy is deermined based on norms of acceleromeers and gyroscopes along wih variance of acceleraions. In reference [], he zero-velociy deecion problem is absraced as a hypohesis-esing problem. However, he proposed algorihms need corresponding hreshold values, if he chosen hreshold values are oo small or large, he real zero velociy inervals will be missed and he wrong inerval will be deeced. he oher opion is using he walking characerisics o deec he zero velociy inervals. ypically, he walking moion is modeled as a repeaing sequence of push off, swing, heel srike and sance. If each sae is deeced accuraely, hen zero velociy inervals can be deermined reliably. Compared wih he former algorihm which needs corresponding hreshold values, he laer algorihm has a hreefold advanage. o begin wih, he laer algorihm is more reliable, because he hreshold is consan, i may be suiable in some cases, bu may be oo small or large in some oher cases. Secondly, he laer algorihm is based on he walking characerisics, no only he zero velociy inervals, bu also he oher sage can be deermined, and he oher sage is useful o verify he zero velociy inervals and inspire poenial applicaions. Finally, he laer algorihm is a new research field which relaes o paern recogniion, ec., herefore, he pedesrian navigaion sysem will be exended wih laer algorihm. Download Dae // : PM
MEASUREMEN SCIENCE REVIEW, Volume., No., he aim of his paper is o design a pedesrian inerial navigaion sysem using MEMS inerial sensors which is based on he hidden Markov model. Specifically, he zero velociy deecion problems are absraced as a hidden Markov model. he observaions exracion algorihm is developed o exrac observaions from sensor oupus, in addiion, a sample se and Baum-Welch algorihm are used o rain and opimize he model. Finally, he performance of he HMM-based pedesrian navigaion sysem is evaluaed using indoor and oudoor field ess, and he resuls show ha he proposed algorihm is reliable. he mos relevan resul is reference [], where a zero velociy deecion algorihm using a hidden Markov model is also used. he main difference is hreefold. One is ha saisical mehod is used in his paper o generae he iniial observaion probabiliies while hreshold values are used in reference []. he second difference is ha a new observaions exracion algorihm is developed. he daa volume is only % of sensor oupus. he hird difference is ha he Baum-Welch algorihm is used in his paper o rain and validae he hidden Markov model. he paper has been organized in he following way. Secion explores characerisics of pedesrians hrough experimens. Secion absracs he zero velociy deecion ino a hidden Markov model problem. Secion describes he pedesrian navigaion sysem framework. Secion evaluaes he performance of he HMM-based pedesrian navigaion sysem using indoor and oudoor field ess.. CHARACERISICS OF PEDESRIANS WALKING MOION Compared wih radiional carriers of inerial sysem, such as aircrafs, ships and guided missiles, pedesrians are characerized by low speed, lile mass, small inerial and periodic moion []. For he pedesrian walking moion, he movemen depends on wo fee. o be exac, wo fee swing forward alernaely, and he cener of graviy of a pedesrian is moving horizonally wih he rise and fall of a small scale. wo phases exis, one phase where he foo is firmly planed on he ground is called sance phase, and he foo is called supporive foo, providing a pivo poin over which o vaul. he oher phase where a foo lifs from behind he pedesrian and swings o ener is sance phase is called swing phase, and he foo is called swing foo which breaks he fall. herefore, he pedesrian walking moion can be characerized by alernae vauling of he body over a siffened leg, wih he fall being broken by he opposing leg []. In order o explore he moion characerisics of a pedesrian, inerial sensors are aached o he inseps o record inerial measuremens a samples per second. he Inerial Measuremen Uni used in his work is he Xsens Mx sensor (model AG) (Xsens), as shown in Fig.., which is he sandard model wih he acceleromeers wih a full scale of ± m/s and he gyroscopes wih a full scale of ± º/s. I has a size of mm, and a weigh of grams. herefore, i is small enough o be mouned on he insep of a pedesrian. he inerial sensors oupus are colleced by a lapop which is conneced o he experimener hrough serial por. he change rules of acceleraion and angular velociy of he walking moion are shown in Fig.. Acceleraion along X Axis (m/s ) Acceleraion along Y Axis (m/s ) Acceleraion along Z Axis (m/s ) - - - Fig.. he MEMS inerial sensors MX. - Sample number (Hz) - - a) Acceleraion along X axis Sample number (Hz) - b) Acceleraion along Y axis Sample number (Hz) c) Acceleraion along Z axis Download Dae // : PM
MEASUREMEN SCIENCE REVIEW, Volume., No., Angular velociy around X Axis (rad/s) Angular velociy around Y Axis (rad/s) Angular velociy around Z Axis (rad/s) - - - Sample number (Hz) - d) Angular velociy around X axis Sample number (Hz) - - e) Angular velociy around Y axis - Sample number (Hz) f) Angular velociy around Y axis Fig.. he acceleraion and angular velociy curves during walking moion. he daa described above indicae ha he acceleraion changes periodically, especially, he acceleraion along he X axis changes beween m/s and - m/s, and he acceleraion along he Z axis changes beween - m/s and m/s. Similarly, he angular velociy around he hree axes changes periodically. Compared wih X and Z axes, he angular rae around he Y axis changes significanly, specifically, beween - rad/s and 9 rad/s. Wha is more, he periodical characerisics are more obvious.. HE ZERO VELOCIY DEECION ALGORIHM his secion sudies how o describe he zero velociy deecion problems wih a hidden Markov model, such as descripion of observaions, developmen of observaions exracion algorihm o exrac observaions from sensors oupus, iniial and rain sae ransfer probabiliy and observaion probabiliy. he hidden Markov model is a saisical Markov model on which he sysem being modeled is assumed o be a Markov process wih unobserved saes. he sae is no direcly visible, bu oupu, dependen on he sae, is visible. Each sae has a probabiliy disribuion over he possible oupu. For pedesrian walking moion zero velociy deecion, wo random processes exis. One is measured acceleraion and angular velociy wih inerial sensors, which is a visible process. he oher is ransfer process of four saes of walking moion, which is no visible. Hidden Markov model is a probabiliy model used o represen he saisic propery of he sochasic process and is characerized by model parameers. In order o define an HMM compleely, following elemens are needed []: () N, he number of saes of he model; () M, he number of mixures in each sae; () A= { a ij }, he sae ransiion probabiliy marix { } aij = P q+ = j q = i i, j N () Where q is he sae a ime and a ij is he ransiion probabiliy from sae i o saej; { j } =, he oupu probabiliy disribuion () B b ( O ) where bj( O ) is a finie mixure of Gaussian disribuions associaed wih saejof he form: M ( ) = ( µ,, ) b O c G O () j m= Where O is he -h observaion vecor, c is weighing coefficien for he m-h mixure in saej, and G is he Gaussian disribuion wih mean vecor µ and covariance marix for he m-h mixure componen in saej. () π = { π j }, he iniial sae disribuion ha is used o describe he probabiliy disribuion of he observaion symbol in he iniial momen when =. In order o absrac he zero velociy deecion ino a hidden Markov model, he saes and observaions are defined, and he sae ransiion probabiliy is iniialized, in addiion, he observaion probabiliy is iniialized using he saisical analysis mehod, finally, he esimaed sae ransiion and observaion probabiliies are opimized using he Baum-Welch algorihm... Saes and observaions Four saes alernae in urn in a walking cycle, as is shown in Fig.. he push off sae is defined as sae A, swing sae is defined as sae B, heel srike sae is sae C and sance sae is sae D. As angular velociy around Y axis changes significanly (see Fig..e)), so i is chosen as observaions. Fig.. shows angular velociy changes during one walking cycle, and i is divided ino four pars according o four saes. As menioned above, Y axis angular velociy is used Download Dae // : PM
MEASUREMEN SCIENCE REVIEW, Volume., No., o consruc observaions. Each sae will have a differen value in differen cycle due o he randomness of walking moion. In order o describe each sae, angular velociy is subdivided ino grades (see Fig..). So, saes (A, B, C and D) and observaions (,,,,,,,, 9,,,,, and ) are defined. Angular velociy around Y axis (rad/s) Fig.. he sae ransiion during a walking moion cycle. - 9 - - - - - - D A B Sample number (Hz) Fig.. he definiion of saes and observaions of HMM... Sae ransiion probabiliy In pedesrian walking moion, he ideal sae ransiion is A B C D A, so he ideal sae ransiion probabiliy is A. A= However, considering randomness moion and exernal facors, sae ransiion is no he same as he ideal one. herefore, parameers in A should be adjused o cope wih special sae ransiions. And A is consruced o be he iniial sae ransiion probabiliy, which is deermined using he rial and error process......... A =........ In he following seps, he iniial sae ransiion probabiliy will be opimized o ge he final sae ransiion probabiliy. C D.. Observaion probabiliy Observaion probabiliy indicaes he probabiliy of each sae generaing each observaion. In order o ge iniial observaion probabiliy, he saisical analysis mehod is used o process he observaions in he sample se. he sample se is raw angular velociy measured by MEMS inerial gyroscopes. In addiion, he observaions exracion algorihm is developed o exrac observaions from sample se. he flow char is shown in Fig.. Angular velociy around Y Axis (rad/s) Angular velociy around Y Axis (rad/s) Angular velociy around Y axis (rad/s) Angular velociy around Y axis (rad/s) - - - - - - Sample number (Hz) a) Raw angular velociy around Y axis. - - - - - - - Sample number (Hz) b) Smoohed angular velociy around Y axis - - - - - - - Exreme values c) Exreme poins of angular velociy around Y axis - - - - - - - Exreme values d) Removed redundan exreme in sae B Download Dae // : PM
MEASUREMEN SCIENCE REVIEW, Volume., No., Emissions Emissions 9 Exreme values e) Observaions of angular velociy around Y axis 9 Exreme values f) Removed redundan exreme in sae D. Fig.. he flow char of observaions exracion algorihm. able. Saisical analysis resuls of observaion. Probabiliy (%) Observaion Sae A Sae B Sae C Sae D.. 9.....9.........9. 9.9.......... he algorihm is inroduced in deail as follows: a firs, raw angular velociy around he Y axis is sored in a vecor (see Fig..a)); and hen in order o eliminae small noise values, he vecor is smoohed using moving average mehod (see Fig..b)) afer ha, he maximum and minimum values from he vecor are used o describe each sae (see Fig..c)). From experimenal resuls, we find ha consecuive exreme values exis wihin sae B and D. Because consecuive exreme values sand for he same sae, so redundan exreme values are deleed (see Fig..d) and f)); hen, he observaion value is used o presen he processed Y axis angular velociy according o grades (see Fig.. and Fig..e)). As menioned above, observaions from raw angular velociy are acquired and he daa volume is only % of raw daa volume. he resul obained hrough he analysis of observaion vecor is shown in able... Esimaed probabiliy opimizaion As menioned above, sae ransiion probabiliy and observaion probabiliy have been iniialized. In order o opimize he model parameer, he Baum-Welch algorihm and he sample se were used o rain he model parameer. he adoped sample se (daa volume is 9) was colleced in an experimen. he Baum-Welch algorihm is a paricular case of a generalized expecaion-maximizaion algorihm. I can compue he maximum likelihood esimae and poserior mode esimaes for he parameers (ransiion and observaion probabiliies) of an HMM. he algorihm process is as follows []: U a ij ( ) ( ) ( ) = = α i αijbj O β j α ( i) β ( i) = c µ π ( i) ( i) N α ( j) α β () i = () j= = γ = M = m= = γ = = γ ( j, γ ( j, (, ) ( j, j m O = γ ( j, ( O µ )( O µ ) = = γ( j, Whereπ i, a ij, c, uand U are he model parameers, (, ) γ j m is he probabiliy of beginning in saeja ime wih m h mixure componen accouning for O of he form γ ( j, ( i) ( i) ( j) ( i) (, µ, ) (,, ) α β c G O U = N M i= α β k= cjkg O µ jk Ujk Opimized sae ransiion probabiliy and observaion probabiliy are as follows, and he sae ransiion diagram is shown in Fig.. he opimized observaion probabiliy is shown in able..9.....9. A=....9.9.. () () () 9 Download Dae // : PM
MEASUREMEN SCIENCE REVIEW, Volume., No.,...9.9 when he IMU is deeced in saionary period beween each foosep. During he normal walking, zero-velociy occurs during he sance phase, when one foo is carrying he full weigh of he body, which has made he soluion based on foo-mouned IMU a popular choice for he PIN sysem. In our sysem, a novel zero velociy deecion algorihm is used, and he framework is shown in Fig.. As menioned above, he angular velociy around he Y axis is used o deec zero velociy. he navigaion sofware based on his framework is developed and will be inroduced in he nex secion. Fig.. he sae ransiion diagram. able. Opimized observaion probabiliy. Probabiliy (%) Observaion Sae A Sae B Sae C Sae D.9.9..9......9 9..9....9. I is imporan o noe ha opimized sae ransiion probabiliy and observaion probabiliy are based on raw angular velociy measured by MEMS inerial gyroscopes, so i relaes o specific experimenal parameers, such as he walking characerisics of experimeners and differen experimenal environmen. herefore, he opimized sae ransiion probabiliy and observaion probabiliy (obained afer applying he Baum-Welch algorih are no used for all he daases, and he opimized sae ransiion probabiliy and observaion probabiliy should be opimized wih differen daabases, jus as he rain process.. PEDESRIAN NAVIGAION SYSEM FRAMEWORK oday, pedesrian navigaion sysem widely adops a sysem framework ha is characerized by exended Kalman filering and srapping MEMS inerial sensors on inseps, which is proposed by Eric Foxlin, whereby he filer racks he errors in he sysem sae raher han he sysem sae direcly, named an error-sae, or complemenary filer. In addiion o he values of sae errors, he filer also esimaes heir error covariance and cross-covariance, which enables he filer o correc he posiion (no only he velociy) during a ZUP. As a pseudo-measuremen, zero velociy provides he required informaion o rese he sae errors Fig.. he pedesrian navigaion sysem framework diagram.. PEDESRIAN NAVIGAION FIELD ES In his secion, he navigaion sofware based on MALAB plaform is described; secondly, he field es is inroduced; finally, a general descripion of he experimens is given... Navigaion sofware he experimens were run using cusom sofware developed based on he MALAB plaform, which processes he daa measured by inerial sensors in off-line way. he sysem is buil on he exended Kalman filering framework menioned above. In addiion, he relevan parameers, such as disance ravelled, and displacemen in D space, are compued accuraely. Finally, he rajecory of a pedesrian can be ploed in verical view, side view, and sereo view. I is worh menioning ha he animaion is used o perform he movemen process. he user inerface of he pedesrian navigaion sofware is shown in Fig.. Fig.. he User inerface of navigaion sofware. Download Dae // : PM
MEASUREMEN SCIENCE REVIEW, Volume., No.,.. Field ess In order o verify he validiy of our navigaion sysem, several field ess have been conduced. Generally, he field es can be divided ino wo kinds according o he navigaion environmens, one is oudoor es, and he oher is indoor es. A square is seleced as oudoor field, and a lab building wih complex inerior srucure is seleced as indoor field. he lab building has five floors, as a general building, each floor is conneced by sairs, and an inerlayer exiss beween wo floors. he experimener is a male who is. m all and weighs kg. In addiion, he walking speed is. m/s on average. As menioned above, he inerial sensor used is Mx (Mx-AG) from Xsens echnologies B.V., which is aached wih shoelaces on he insep of he pedesrian.... Oudoor es he oudoor es was conduced on a square, and he planned pah was along he sidelines of a square, which was a 9 m circular area and he radius was m, as shown in Fig.9. More specifically, wo ess have been conduced on his square, one is recangle walking moion which is ploed in red, and he oher is circular walking moion which is ploed in blue. y ( - - - - - - x ( Sar End Fig.. he walking rajecory of recangle walking moion. able. Navigaion error of recangle walking moion. - Direcion Displacemen [m] X Axis. Y Axis. Sar End - - y ( - - - - - - - x ( Fig.9. he saellie phoo of he es square. o begin wih, he recangle walking moion is analyzed. he perimeer is. m measured by he experimener, and he pah lengh is.9 m compued by he navigaion sysem. he rajecory is ploed in verical view as shown in Fig.. As he roue is closed, he sar poin and he end poin are overlapping in heory, bu he realiy is differen, as shown in Fig.. and in able. he displacemen error is. m,. % of oal pah lengh. he second es is a circular walking es, and he planned pah is ploed in blue as shown in Fig.9. he riangle symbol is he saring poin, walking in he couner-clockwise direcion unil reaching he circular symbol, which is he end poin. As menioned above, he acual measured disance of walking pah is m, as m disance exiss beween saring and end poin. he pah lengh is m compued by he navigaion sysem. he rajecory is ploed in verical view as shown in Fig.. he displacemen error is m, % of oal pah lengh. Fig.. he walking rajecory of circular walking moion. able. Navigaion error of circular walking moion. Direcion Displacemen [m] X Axis. Y Axis. As menioned above, he wo oudoor es resuls show ha accuracy of pedesrian navigaion sysem is reliable, abou ~ % of oal pah lengh. Neverheless, he oudoor ess are conduced on he ground, in order o research he navigaion accuracy in buildings; he indoor ess are conduced, which will be discussed in he nex secion.... Indoor es he indoor ess are conduced in a lab building wih complex inerior srucure. he lab building which has five floors, as a general building, each floor is conneced by sairs, and an inerlayer exiss beween wo floors. Download Dae // : PM
MEASUREMEN SCIENCE REVIEW, Volume., No., o begin wih, he firs es is analyzed, he walking planned pah is a closed roue from a poin on NO. floor, hen going upsairs o NO. floor, hrough he corridors, and going downsairs back o NO. floor, finally going back o he saring poin. As he roue is closed, he saring poin and he end poin are overlapping in heory, bu he realiy is differen, as shown in Fig.., which is a D view of he walking rajecory. In addiion, he navigaion error is shown in able. And he heigh walking rajecory is shown in Fig.. he displacemen error is.9 m,. % of oal pah lengh. Z( Sar End - Y( - - - X( Z( Sar End Fig.. he D walking rajecory. he compued D walking rajecory is shown in Fig.., and he heigh walking rajecory is shown in Fig.. he navigaion error is shown in able. he displacemen error is. m, % of oal pah lengh. - - Y( - - - - - - X( NO. Floor. Fig.. he D walking rajecory. Heigh ( NO. Floor Heigh (... NO. Floor Inerlayer - Disance ( Fig.. he heigh walking rajecory. NO. Floor NO. Floor -. Disance ( Fig.. he heigh walking rajecory. able. Navigaion error of firs indoor es. Direcion Displacemen [m] X Axis. Y Axis. Z Axis. Secondly, anoher indoor es in he lab building was conduced, specifically, he planned walking pah covered hree floors, and he saring poin was on NO. floor, climbing up o NO. floor and a recangular pah was followed on his floor, hen climbing down o NO. floor hrough anoher fligh of sairs. he end poin deviaes from he sar poin in he Y axis abou. m. able. Navigaion error of second indoor es. Direcion Displacemen [m] X Axis. Y Axis. Z Axis. As menioned above, wo oudoor navigaion ess and wo indoor navigaion ess were conduced, which were processed wih he pedesrian navigaion sofware developed on MALAB. he compued displacemen in hree axes was analyzed, he navigaion error of oudoor and indoor navigaion field ess was less han % of oal disance ravelled. he walking rajecories were ploed in differen view. he experimenal resuls indicae ha he pedesrian navigaion sysem proposed in his paper can eliminae error caused by inerial sensors and oher facors effecively, and he navigaion algorihm is suiable for horizonal navigaion and sereo navigaion, and he navigaion accuracy is accepable. Download Dae // : PM
MEASUREMEN SCIENCE REVIEW, Volume., No.,. CONCLUSIONS Zero velociy deecion is an essenial par of pedesrian inerial sysem, i provides he required informaion o rese velociy esimaed error, oherwise he velociy esimaed error would increase linearly wih ime, and he esimaed posiion error would increase a leas quadraically. In his paper, in order o deec zero velociy sae accuraely, a zero velociy deecion algorihm based on he hidden Markov model is proposed. he core idea is describing zero velociy deecion wih he hidden Markov model, and four saes are used o describe he walking moion. herefore, zero velociy deecion is convered o deec a sae in he hidden Markov model using he Baum-Welch algorihm. In addiion, pedesrian navigaion sofware was developed based on he exended Kalman filering and srapping MEMS inerial sensors on inseps. Finally, employing he several indoor and oudoor navigaion ess in lab buildings and squares, we evaluaed he performance of pedesrian inerial navigaion based on hidden Markov model proposed in his paper. he es resuls show ha he posiion error was less han % of oal disance ravelled. In addiion, i works well in indoor and oudoor navigaion environmen. ACKNOWLEDGMEN his work was suppored by he Naional Naural Science Foundaion Projecs of China (NO., NO.9, NO.9); he Fundamenal Research Funds for he Cenral Universiies (NO., NO., NO. 9, NO., and NO.); he Applied Fundamenal Research Projec of Minisry of ranspor of China (NO.99); he Scienific Research Projec of Liaoning Educaion Deparmen (NO.L). REFERENCES [] Dedes, G., Dempser, A.G. (). Indoor GPS posiioning-challenges and opporuniies. In nd Vehicular echnology Conference (VC- -Fall), - Sepember. IEEE, -. [] Jim Nez, A.R., Seco, F., Zampella, F., Prieo, J.C., Guevara, J. (). PDR wih a foo-mouned IMU and ramp deecion. Sensors, (), 99-9. [] Ali, J. (9). Asronavigaion sysem as an auonomous enhancemen suie for a srapdown inerial navigaion sysem: An evaluaion. Measuremen Science Review, 9 (), -. [] Harle, R. (). A survey of indoor inerial posiioning sysems for pedesrians. IEEE Communicaions Surveys & uorials, (), -9. [] Foxlin, E. (), Pedesrian racking wih shoe-mouned inerial sensors. IEEE Compuer Graphics and Applicaions, (), -. [] Fischer, C., alkad Sukumar, P., Hazas, M. (), uorial: Implemening a pedesrian racker using inerial sensors. IEEE Pervasive Compuing, (), -. [] Wen, L., Yingjun, Z., Feixiang, Z. (). A gai recogniion algorihm using MEMS inerial sensor for pedesrian dead-reckoning. In h Sain Peersburg Inernaional Conference on Inegraed Navigaion Sysems, -9 May, -. [] Park, S.K., Suh, Y.S. (). A zero velociy deecion algorihm using inerial sensors for pedesrian navigaion sysems. Sensors, (), 9-9. [9] Yun, X., Bachmann, E.R., Moore, H., Calusdian, J. (). Self-conained posiion racking of human movemen using small inerial/magneic sensor modules. In IEEE Inernaional Conference on Roboics and Auomaion, - April. IEEE, -. [] Ojeda, L., Borensein, J. (). Non-GPS navigaion wih he personal dead-reckoning sysem. In Unmanned Sysems echnology IX, 9- April. SPIE, Vol.. [] Bousbia-Salah, M., Fezari, M. (). he developmen of a pedesrian navigaion aid for he blind. In IEEE GCC Conference, - March. IEEE, -. [] Godha, S., Lachapelle, G., Cannon, M.E. (). Inegraed GPS/INS sysem for pedesrian navigaion in a signal degraded environmen. In 9h Inernaional echnical Meeing of he Saellie Division (ION GNSS ), -9 Sepember. For Worh, X, -. [] Jimenez, A.R., Seco, F., Prieo, J.C., Guevara, J. (). Indoor pedesrian navigaion using an INS/EKF framework for yaw drif reducion and a foo-mouned IMU. In h Workshop on Posiioning Navigaion and Communicaion, - March. IEEE, -. [] Skog, I., Nilsson, J.O., Handel, P. (). Evaluaion of zero-velociy deecors for foo-mouned inerial navigaion sysems. In Inernaional Conference on Indoor Posiioning and Indoor Navigaion, - Sepember. IEEE, -. [] Wen, L., Yingjun, Z. (). Feaure analysis of pedesrian navigaion using energy-saving inerial sensors. Energy Educaion Science and echnology Par A: Energy Science and Research, (), -. [] Cheshomi, S., Rahai-Q, S., Akbarzadeh-, M.-R. (). Hybrid of Chaos Opimizaion and Baum-Welch algorihms for HMM raining in coninuous speech recogniion. In Inernaional Conference on Inelligen Conrol and Informaion Processing, - Augus. IEEE, -. [] Rabiner, L. (99). A uorial on hidden Markov models and seleced applicaions in speech recogniion. Proceedings of he IEEE, (), -. Received July,. Acceped February,. Download Dae // : PM