發(fā)布時(shí)間：2018-05-07 17:50

  本文選題：MEMS慣性器件 + 步行者導(dǎo)航�。� 參考：《電子科技大學(xué)》2014年碩士論文

【摘要】：便攜式移動(dòng)電子設(shè)備的快速發(fā)展,給人們的生活帶來(lái)了很大的改變,其中智能手機(jī)和平板電腦已經(jīng)成為日常生活中必不可少的工具。為了有更加新穎的應(yīng)用和更絢麗的視覺效果,智能手機(jī)和平板電腦中集成了多種微傳感器,如三軸MEMS加速度計(jì)、三軸MEMS陀螺儀和MEMS磁力計(jì)等。本文依據(jù)UESTC-NOKIA國(guó)際合作項(xiàng)目,旨在研究一種基于MEMS慣性傳感器的適用于移動(dòng)電子設(shè)備的新型而有趣的步行者導(dǎo)航方式。因?yàn)槌杀究刂?便攜式移動(dòng)電子設(shè)備中使用的MEMS慣性傳感器有精度偏低、隨機(jī)誤差較大等缺點(diǎn)。針對(duì)這些缺點(diǎn),本文對(duì)使用的LIS344ALH加速度傳感器和Ex3500A4962A陀螺儀建立了誤差模型;使用“六位置法”標(biāo)定了加速度傳感器和陀螺儀的靜態(tài)誤差;針對(duì)隨機(jī)誤差設(shè)計(jì)了低通濾波器和狀態(tài)擴(kuò)增卡爾曼濾波器,有效地抑制了傳感器信號(hào)的漂移,提高了航位推算的精度。慣性測(cè)量單元被捆綁于步行者的腰部,其中三軸MEMS加速度計(jì)用于測(cè)量的步行者的加速度,MEMS陀螺儀用于測(cè)量步行者轉(zhuǎn)向時(shí)的角速率。通過(guò)對(duì)步行者一次邁步周期內(nèi)的動(dòng)力學(xué)模型的分析,提取特征量判別步行者的邁步步態(tài)信息:邁步起至點(diǎn)、站立階段、邁步階段,進(jìn)而估計(jì)步行者的步頻、步數(shù)和步長(zhǎng);利用加速度計(jì)信號(hào)通過(guò)姿態(tài)算法可間接推算步行者的姿態(tài)角。通過(guò)角速率可直接計(jì)算步行者的姿態(tài)矩陣和姿態(tài)角。采用互補(bǔ)濾波算法融合兩種算法得到的姿態(tài)角,提高了姿態(tài)角的計(jì)算精度,有效地降低了計(jì)算姿態(tài)角所產(chǎn)生的累積誤差對(duì)運(yùn)動(dòng)軌跡的影響,使之更適用于低精度慣性傳感器。最后通過(guò)航位推算原理,合成步行者的相對(duì)運(yùn)動(dòng)軌跡。最后本文對(duì)整個(gè)系統(tǒng)在Linux系統(tǒng)中進(jìn)行了算法編程。為驗(yàn)證步行者航位推算系統(tǒng)的精度和對(duì)個(gè)體差異的適應(yīng)性,在室內(nèi)環(huán)境中以普通人的正常行走速度進(jìn)行了現(xiàn)場(chǎng)試驗(yàn)。根據(jù)試驗(yàn)結(jié)果對(duì)步長(zhǎng)估計(jì)算法和姿態(tài)更新算法的精度做了對(duì)比分析和評(píng)估;并將試驗(yàn)結(jié)果與實(shí)際參考數(shù)據(jù)軌跡作對(duì)比,分析了步行者航位推算算法的準(zhǔn)確性和可行性,且該系統(tǒng)可以滿足消費(fèi)電子領(lǐng)域?qū)?shí)時(shí)性的需求。并根據(jù)實(shí)驗(yàn)所得的結(jié)果和數(shù)據(jù)分析,本文還提出了一些改進(jìn)意見。
[Abstract]:The rapid development of portable mobile electronic devices has brought great changes to people's lives, in which smart phones and tablets have become an indispensable tool in daily life. For more novel applications and more beautiful visual effects, a variety of microsensors, such as three-axis MEMS accelerometers, three-axis MEMS gyroscopes and MEMS magnetometers, have been integrated into smartphones and tablets. This paper aims to study a new and interesting walker navigation method based on MEMS inertial sensors for mobile electronic devices according to the UESTC-NOKIA international cooperation project. Because of cost control, the MEMS inertial sensor used in portable mobile electronic equipment has some disadvantages, such as low precision and large random error. Aiming at these shortcomings, the error model of LIS344ALH accelerometer and Ex3500A4962A gyroscope is established, and the static error of accelerometer and gyroscope is calibrated by "six-position method". A low-pass filter and a state-amplified Kalman filter are designed for random error, which can effectively suppress the drift of sensor signal and improve the accuracy of dead-reckoning. The inertial measurement unit is tied to the walker's waist and the three-axis MEMS accelerometer is used to measure the walker's acceleration and the angular rate of the walker's turn. Based on the analysis of the dynamic model of the walker in one step cycle, the characteristic quantity is extracted to judge the gait information of the walker: the starting point, standing stage, step stage, and then estimating the walker's frequency, number and length; The attitude angle of the walker can be calculated indirectly by using the accelerometer signal through the attitude algorithm. The attitude matrix and attitude angle of the walker can be calculated directly by angular rate. The attitude angle obtained by the two algorithms is fused by the complementary filtering algorithm, which improves the accuracy of the attitude angle calculation, effectively reduces the influence of the accumulated error caused by the attitude angle calculation on the motion trajectory, and makes it more suitable for the low-precision inertial sensor. Finally, the relative trajectory of the walker is synthesized by the principle of dead-reckoning. Finally, the algorithm programming of the whole system is carried out in Linux system. In order to verify the accuracy and adaptability to individual differences of the pedestrian level reckoning system, a field test was carried out in the indoor environment with the normal walking speed of ordinary people. According to the experimental results, the accuracy of step size estimation algorithm and attitude updating algorithm are compared and evaluated, and the accuracy and feasibility of the algorithm are analyzed by comparing the experimental results with the actual reference data trajectory. The system can meet the real-time demand in consumer electronics field. According to the experimental results and data analysis, this paper also puts forward some suggestions for improvement.
【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】：2014
【分類號(hào)】：TN96;TP212.9

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本文編號(hào)：1857878