
 
 
 
 
 Waandishi wa: (1) Liqiang Zhang, Shule ya Microelectronics, Chuo Kikuu cha Tianjin, China; (2) Kai Guo, Shule ya Microelectronics, Chuo Kikuu cha Tianjin, China; (3) Yu Liu, Shule ya Microelectronics, Chuo Kikuu cha Tianjin Tianjin, China. Authors: (1) Liqiang Zhang, Shule ya Microelectronics, Chuo Kikuu cha Tianjin, China; (2) Kai Guo, Shule ya Microelectronics, Chuo Kikuu cha Tianjin, China; (3) Yu Liu, Shule ya Microelectronics, Chuo Kikuu cha Tianjin Tianjin, China. Orodha ya kulia Ufafanuzi na I. Ufuatiliaji II. Mkakati wa III. Matokeo ya majaribio IV. Mwisho na Kazi ya baadaye, na Maelezo —Kalman filter-based Inertial Navigation System (INS) is a reliable and efficient method to estimate the position of a pedestrian indoors. Classical INS-based methodology which is called IEZ (INS-EKF-ZUPT) makes use of an Extended Kalman Filter (EKF), a Zero velocity UPdaTing (ZUPT) to calculate the position and attitude of a person. However, heading error which is a key factor of the whole Pedestrian Dead Reckoning (PDR) system is unobservable for IEZ-based PDR system. To minimize the error, Electronic Compass (EC) algorithm becomes a valid method. But magnetic disturbance may have a big negative effect on it. In this paper, the Quasi-static Magnetic field Detection (QMD) method is proposed to detect the pure magnetic field and then selects EC algorithm or Heuristic heading Drift Reduction algorithm (HDR) according to the detection result, which implements the complementation of the two methods. Meanwhile, the QMD, EC, and HDR algorithms are integrated into IEZ framework to form a new PDR solution which is named as Advanced IEZ (AIEZ). Abstract Abstract ya I. Maelezo ya Global Navigation Satellite System (GNSS) inatumika sana kama mbinu muhimu ya uendeshaji kwa ajili ya usafiri wa miguu. Lakini katika uwanja, kama vile kanyoni ya jiji au mazingira ya ndani, GNSS inakuwa isiyo ya kuaminika kutokana na kupungua kwa ishara, kuingilia na shadow. Ili kupata matokeo ya uendeshaji ya kuendelea na ya kuaminika, mfumo wa uendeshaji wa mitaani (LPS) unafanywa uchunguzi kwa kutumia aina mbalimbali za mbinu, kama vile inertial unit-based (IMU), wireless-based au vision-based teknolojia [1], nk Miongoni mwao, mbinu za PDR zilizoundwa na IMUs zinatolewa kwa sababu ya gharama ya chini, kuaminika zaidi, na hakuna miundombinu ya nje inahitajika. Vituo tofauti vya kuunganisha vilifanywa utafiti ikiwa ni pamoja na kuunganishwa kwa mkono [2], kuunganishwa kwa midomo [3] au kuunganishwa kwa miguu [4-8], nk. Kwa bahati mbaya, IMU ya bei ya chini inatoa utulivu wa muda mfupi na makosa ya utaratibu yanaongezeka kutokana na asili yake ya ushirikiano. Ili kuboresha utendaji wa mfumo, mfumo wa PDR uliotumika kwa kiasi kikubwa na Kalman IEZ [4] alitumia algorithm ya ZUPT ili kuhesabu makosa ya kasi wakati wa awamu ya mstari. Kwa utendaji wa mfumo wa PDR, kichwa ni moja ya sababu muhimu ambazo zina athari juu ya usahihi wake. Hata hivyo, makosa ya kichwa haijulikani kwa IEZ. Matumizi ya kichwa cha magnetic ni njia yenye ufanisi ya kuunganisha makosa ya kichwa cha kushuka. Lakini usumbufu wa magnetic ikiwa ni pamoja na usumbufu mkali na mkali [9, 10] ni stochastic na kubadilisha ndani, ambayo haiwezekani kutabiriwa. Kwa hiyo, algorithm ya Quasi-static Magnetic Field Detection (QMD) ilipendekezwa kuchagua shida ya shida ya magnetic katika mazingira ya usumbufu wa magnetic na kisha kuunganisha data kutoka gyroscope na magnetometer [11]. Njia inapatikana kugundua shida ya magnetic ambayo inabadilika kidogo na ina Katika makala hii, algorithm ya QMD iliundwa upya ili kutatua tatizo kwamba usumbufu mkali wa magnetic ni vigumu kugunduliwa kwa algorithm ya QMD ya kisasa katika [11]. Wakati usumbufu ni kugunduliwa na QMD, algorithm ya HDR ambayo inategemea tu data kutoka gyroscope ni kuchaguliwa ili kupunguza makosa ya kupiga kichwa. Kwa kinyume chake, algorithm ya Electronic Compass (EC) ni chaguo bora ya kubadilisha makosa ya kichwa. Kuhusu [4], kufuatia kazi ya Foxlin, kuondokana na mfumo mpya wa PDR, algorithm ya QMD, HDR na EC ni kuunganishwa katika IEZ kuunda mfumo mpya unaoitwa AIEZ (Advanced IEZ). II. Utaratibu wa Sehemu hii inaelezea utaratibu wa mfumo wa PDR AIEZ. Hatua za usindikaji zinaonyeshwa kama ifuatavyo. 
 
 
 
 
 
 
 
 The data collected from accelerometer and gyroscope are utilized to detect the stance phase. 
 
 The roll and pitch angle are estimated using the data from accelerometer during the stance phase. 
 
 Meanwhile, when the pure magnetic field is detected by QMD, EC is selected to calculate the optimal heading. On the contrary, HDR is the better option to correct the heading drift error. 
 
 The Extended Kalman Filter (EKF) is updated with velocity and attitude measurement by ZUPT and Attitude Correction (AC) during stance phase, respectively. 
 
 Eventually, we obtain the optimal attitude, velocity and position using inertial navigation mechanism. A. Mfumo wa Usafiri wa Inertial Tunatumia Mfumo wa Usafiri wa Inertial (INS) wa kiwango cha chini ili kuhesabu msimamo, kasi na msimamo wa mpendwa. IMU inayoitwa MTw Awinda na ukubwa wa 148×104×31.9 mm kutoka kampuni ya Xsens imewekwa kutuma data kwa 100 Hz, ambayo imewekwa kwenye pete kama ilivyoonyeshwa katika Fig.3. B. Algorithm ya uchunguzi wa hatua Ufuatiliaji wa awamu ya msimamo ni mchakato muhimu katika mfumo wa PDR AIEZ. Ufuatiliaji bora wa awamu ya msimamo husaidia kuchochea EKF kwa wakati ili kubadilisha makosa ya INS kwa utendaji bora. Algorithi nyingi za ufuatiliaji wa awamu ya msimamo hutegemea data kutoka kwa accelerometer [12] au gyroscope [13] pamoja na both accelerometer na gyroscope [6, 14]. Baadhi ya wanasayansi kutekeleza ufuatiliaji kwa kutumia ultrasound, RFID au magnet [15], nk Tunatumia detector bora ya ufuatiliaji wa awamu ya msimamo (SHOE) iliyopendekezwa katika [14] ili kugundua awamu ya msimamo. C. Algorithm ya kugundua shamba la magnetic Tunafafanua tatizo la kugundua shamba la magnetic kama tatizo la kupima hypothesis ya binary, ambapo detector inaweza kuchagua kati ya hypotheses mbili ambazo zinafafanuliwa kama ifuatavyo: Kama ilivyoaanishwa katika Sheria ya EWURA kipengere cha 6 (e, moja ya majukumu ya EWURA ni kutoa elimu kwa wadau wake kuhusu kazi na majukumu ya Mamlaka kwa sekta inazozidhibiti. D. Algorithm ya Urekebishaji Kutokana na ushawishi wa vipengele vya accelerometer na gyroscope, makosa ya kipengele cha Euler huhifadhiwa kwa muda. Kutumia data kutoka kwa accelerometer na gyroscope inaweza kuhesabu kipengele cha roll na pitch wakati miguu ni imara. shamba la magnetic safi hutoa kichwa halisi. Tunatumia data wakati wa hatua kubadilisha mtazamo uliofanywa na INS. 2) Utambulisho wa elektroniki 3) Heuristic kichwa Drift Reduction E. Kuongeza Filter ya Kalman 
 
 Makala hii inapatikana kwenye arkiv chini ya leseni ya ATTRIBUTION-NONCOMMERCIAL-NODERIVS 4.0 INTERNATIONAL. Makala hii ni chini ya leseni ya ATTRIBUTION-NONCOMMERCIAL-NODERIVS 4.0 INTERNATIONAL. available on arxiv Upatikanaji wa Archives

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