本文選題：室內(nèi)定位與導(dǎo)航　切入點(diǎn)：MEMS傳感器　出處：《電子科技大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：室內(nèi)定位與導(dǎo)航潛藏著巨大的社會(huì)功能和經(jīng)濟(jì)效益,鑒于GNSS導(dǎo)航在室內(nèi)環(huán)境中存在嚴(yán)重的信號(hào)衰減和多徑問題,使用慣性測(cè)量單元進(jìn)行不依賴于外部環(huán)境的自主定位與導(dǎo)航技術(shù)成為該領(lǐng)域研究的熱點(diǎn)。本文即使用MEMS慣性測(cè)量單元裝配在行人腳部進(jìn)行行走動(dòng)作的測(cè)量,并作航位推算,然后結(jié)合行人所在室內(nèi)的拓?fù)涞貓D估計(jì)其在室內(nèi)的位置和航跡。具體而言,本文在研究和構(gòu)建完整的基于MEMS慣性測(cè)量單元的室內(nèi)定位與導(dǎo)航試驗(yàn)系統(tǒng)中完成如下工作:首先,本文針對(duì)室內(nèi)定位與導(dǎo)航領(lǐng)域?qū)�、�?shí)時(shí)性和成本等各方面的需求,對(duì)導(dǎo)航系統(tǒng)平臺(tái)的架構(gòu)和整體工作流程做了設(shè)計(jì),并針對(duì)其中所涉及的MEMS慣性測(cè)量傳感器精度級(jí)別較低的特點(diǎn)進(jìn)行了誤差分析,在對(duì)慣性測(cè)量傳感器的誤差分析中,將其測(cè)量誤差源分為確定性偏差與隨機(jī)噪聲兩類,提出了一個(gè)綜合性的校準(zhǔn)方案。其次,傳統(tǒng)的行人航位推算方法基于擴(kuò)展卡爾曼濾波器和對(duì)行人腳步站姿的檢測(cè)方法從而得到最優(yōu)估計(jì)的行人航跡。但這一方法并未考慮慣性測(cè)量傳感器的確定性偏差,使得推算航跡偏離實(shí)際航跡較大。本文所提出的最優(yōu)航跡推算方案加入了對(duì)傳感器確定性偏差的校準(zhǔn),通過建立一個(gè)綜合性的誤差模型,將計(jì)算得到的確定性校準(zhǔn)系數(shù)以及Allan方差作為參數(shù)用于卡爾曼濾波器中,同時(shí)對(duì)兩類誤差進(jìn)行補(bǔ)償,根據(jù)所使用的傳感器,以實(shí)際行走總距離為基準(zhǔn),新的方案使得最終航跡推算的位置精度相比傳統(tǒng)方法提高了30%左右。然后,本文根據(jù)行人航位推算結(jié)果使用基于粒子濾波的地圖匹配算法估計(jì)行人在室內(nèi)的位置與航跡。這一地圖匹配算法需要利用到室內(nèi)地圖中門、房間、走廊等結(jié)構(gòu)信息及其空間關(guān)系,本文采用一種容差性的方案提取所需建筑結(jié)構(gòu)并構(gòu)建室內(nèi)拓?fù)涞貓D。不同于已有室內(nèi)地圖拓?fù)浠桨钢袑⒆呃茸鳛槁窂?走廊中拐點(diǎn)作為節(jié)點(diǎn)的拓?fù)鋽?shù)據(jù)結(jié)構(gòu),本文在提取出室內(nèi)門、房間、走廊等空間結(jié)構(gòu)后,將可以通行的空間作為路徑以及將門作為節(jié)點(diǎn)進(jìn)行室內(nèi)拓?fù)涞貓D的構(gòu)建,一方面可以使用可通行空間的輪廓作為路徑約束,用于地圖匹配所使用粒子濾波算法的重要性采樣中,另一方面將房間也納入到可通行空間中,更加符合實(shí)際中行人常常在房間之間進(jìn)行活動(dòng)的特點(diǎn)。最后,采用手機(jī)客戶端實(shí)現(xiàn)演示系統(tǒng),對(duì)地圖匹配算法進(jìn)行了實(shí)驗(yàn)驗(yàn)證,結(jié)果顯示最終地圖匹配的位置精度可以達(dá)到±1.5m左右,行人可以根據(jù)匹配結(jié)果確定當(dāng)前所在室內(nèi)的位置,證明了該室內(nèi)導(dǎo)航方案的有效性。
[Abstract]:Indoor positioning and navigation have great social functions and economic benefits. In view of the serious signal attenuation and multipath problems in indoor environment, GNSS navigation has many problems. The use of inertial measurement units for autonomous positioning and navigation independent of the external environment has become a hot topic in this field. In this paper, the MEMS inertial measurement unit is assembled in the foot of the pedestrian to measure the walking motion, and the navigation calculation is made. Then, the location and track of the pedestrian are estimated by using the topological map of the indoor. Specifically, this paper studies and builds a complete indoor positioning and navigation test system based on the MEMS inertial measurement unit. The main work is as follows: first of all, Aiming at the requirement of precision, real-time and cost in the field of indoor positioning and navigation, this paper designs the architecture and overall workflow of the navigation system platform. The error analysis of the MEMS inertial measurement sensor is carried out according to its low precision level. In the error analysis of the inertial measurement sensor, the measurement error source is divided into two categories: deterministic deviation and random noise. A comprehensive calibration scheme is proposed. Secondly, The traditional footpath estimation method is based on the extended Kalman filter and the detection method of the footstep posture to obtain the best estimated pedestrian track, but the deterministic deviation of the inertial measurement sensor is not taken into account in this method. The optimal track calculation scheme proposed in this paper includes the calibration of the deterministic deviation of the sensor, and establishes a comprehensive error model. The calculated deterministic calibration coefficient and Allan variance are used as parameters in Kalman filter, and two kinds of errors are compensated. According to the sensor used, the actual total walking distance is taken as the reference. The new scheme improves the accuracy of the final track calculation by about 30% compared with the traditional method. Then, In this paper, we use particle filter based map matching algorithm to estimate the indoor position and track of pedestrians according to the results of pedestrian reckoning. This map matching algorithm needs to be used in the door and room of the indoor map. In this paper, a tolerance scheme is used to extract the required building structure and construct the indoor topological map, which is different from the corridor as the path in the existing topologies of indoor maps. The inflection point in the corridor is the topological data structure of the node. After extracting the indoor door, room, corridor and other spatial structures, the passable space is taken as the path and the door is used as the node to construct the indoor topological map. On the one hand, the contour of the passable space can be used as the path constraint to sample the importance of the particle filter algorithm used in map matching, on the other hand, the room can also be incorporated into the passable space. Finally, the mobile phone client is used to implement the demonstration system, and the map matching algorithm is verified experimentally. The results show that the position accuracy of the final map matching can reach 鹵1.5 m, and the pedestrian can determine the current indoor location according to the matching results, which proves the validity of the indoor navigation scheme.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP212;TN713
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本文編號(hào)：1604877