本文選題：慣性導(dǎo)航 + 零速度修正��； 參考：《廈門大學(xué)》2014年碩士論文

【摘要】：隨著科技的發(fā)展及城市化的快速推進,高林密布的大樓和大而密封的室內(nèi)環(huán)境越發(fā)增多。在傳統(tǒng)的GPS導(dǎo)航技術(shù)中,由于其信號在穿透建筑物后會被嚴重削弱,所以很難應(yīng)用于室內(nèi)導(dǎo)航與定位。然而,微機電系統(tǒng)(MEMS)的快速發(fā)展為這一問題帶來了解決方案,基于MEMS的慣性器件以其自身體積小、成本低、功耗低等特點在室內(nèi)捷聯(lián)式慣性導(dǎo)航應(yīng)用方面有了廣泛研究。但是,由于MEMS慣性器件本身存在漂移、噪聲等誤差,所以在將其應(yīng)用于室內(nèi)導(dǎo)航的過程時,如何消除誤差是較為重要的難題。 本文將基于鞋綁式的捷聯(lián)式慣性導(dǎo)航系統(tǒng),擬研究行人在不同步態(tài)(正常步行、跑步)下,針對慣性器件自身的漂移誤差和運動過程中引入的噪聲誤差進行零速度修正研究。其原理是當行人以不同步態(tài)活動時,腳步與地面接觸過程中會存在一段理論上速度為零的時刻,而實際上并非為零,因此,通過檢測到腳步處于零速度時刻,就可預(yù)測到誤差值,然后進行剔除。零速度時刻檢測部分將分別依據(jù)慣性傳感器數(shù)據(jù)(加速度值與角速度值)和固定在正腳背上的超聲波模塊,測量其與地面距離的數(shù)據(jù)。其中依據(jù)慣性傳感器數(shù)據(jù)的零速度檢測算法,采用一種基于加速度平方和、加速度平方和的方差和角速度值的多條件方法,而依據(jù)超聲波數(shù)據(jù)的零速度檢測算法是根據(jù)對雙足運動步態(tài)模型的分析,推出超聲波數(shù)據(jù)模型,再根據(jù)模型總結(jié)出檢測算法,從而找到零速度時刻。考慮到超聲數(shù)據(jù)的穩(wěn)定性,使用局部加權(quán)回歸散點平滑法處理超聲波數(shù)據(jù),使其整體能夠更加具體的顯示出運動模型的規(guī)律和趨勢,從而方便檢測。通過兩種零速度檢測方法檢測到零速度時刻后,再觸發(fā)卡爾曼濾波進行誤差預(yù)測并更新速度、位置和姿態(tài)信息。 通過實驗,其結(jié)果驗證了零速度檢測算法的可行性,以及在不同步態(tài)下,基于慣性數(shù)據(jù)和基于超聲波數(shù)據(jù)進行零速度修正后得到的步數(shù)、單步步長、整體距離與實際情況相符。在步數(shù)方面,兩者均能100%檢測到；而單步步長與實際設(shè)定值的誤差也不大；在整體運動距離方面,兩者修正后在正常步行步態(tài)下得到了1%以內(nèi)的距離誤差,而跑步則達到了2%以內(nèi)的距離誤差。但其中,基于超聲波的零速度修正得到的結(jié)果要好于基于慣性傳感器數(shù)據(jù)的結(jié)果。 本文對于零速度修正技術(shù)中零速度檢測算法進行了不同方面的分析并驗證,為MEMS慣性傳感器應(yīng)用到行人室內(nèi)導(dǎo)航實際場景中提供了一種簡單而高效的方法。
[Abstract]:With the development of science and technology and the rapid development of urbanization, there are more and more high-forest buildings and large and sealed indoor environment. In the traditional GPS navigation technology, it is difficult to be used in indoor navigation and positioning because its signal will be seriously weakened after penetrating the building. However, the rapid development of MEMS brings a solution to this problem. The inertial devices based on MEMS have been widely studied in the field of indoor strapdown inertial navigation because of their small size, low cost and low power consumption. However, there are drift and noise errors in MEMS inertial devices, so how to eliminate the errors is a more important problem in the process of indoor navigation. In this paper, a strapdown inertial navigation system based on shoe binding is proposed to study the zero velocity correction of drift error and noise error of inertial devices under different gait (normal walking, running). The principle is that when a pedestrian moves at a different gait, there is a time when the pace is in contact with the ground at a time when the velocity is theoretically zero, but not zero in practice, so by detecting that the pace is at zero, The error can be predicted and then eliminated. The zero-velocity detection part will measure the distance between the inertial sensor and the ground according to the data of the inertial sensor (acceleration and angular velocity) and the ultrasonic module fixed on the back of the positive foot. According to the zero velocity detection algorithm of inertial sensor data, a multi-condition method based on variance and angular velocity value of acceleration square sum, acceleration square sum is adopted. The zero-velocity detection algorithm based on ultrasonic data is based on the analysis of bipedal gait model, and then summarizes the detection algorithm according to the model to find the zero velocity time. Considering the stability of ultrasonic data, the local weighted regression scatter point smoothing method is used to process the ultrasonic data, so that the whole ultrasonic data can show the law and trend of the motion model more concretely, so it is convenient to detect. After the zero velocity time is detected by two zero velocity detection methods, the Kalman filter is triggered to predict the error and update the velocity, position and attitude information. The experimental results verify the feasibility of the zero velocity detection algorithm and the steps of zero velocity correction based on inertial data and ultrasonic data under different gait conditions. The step length is long and the whole distance is consistent with the actual situation. In terms of the number of steps, both can be detected in 100%, and the error between the single step length and the actual set value is small. In terms of the overall motion distance, the error of distance within 1% of the normal walking gait is obtained after correction. Running, on the other hand, reaches a distance error of less than 2%. But the result of zero velocity correction based on ultrasonic wave is better than that based on inertial sensor data. This paper analyzes and verifies the zero velocity detection algorithm in the zero speed correction technology, which provides a simple and efficient method for the application of MEMS inertial sensor in the pedestrian indoor navigation scene.
【學(xué)位授予單位】：廈門大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN966

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