本文選題：個人導航　切入點：活動識別　出處：《廈門大學》2014年碩士論文　論文類型：學位論文

【摘要】：導航技術作為眾多信息技術的代表,正悄然進入人類生活的細枝末節(jié)。如何適應復雜環(huán)境、融合多傳感器信息實現(xiàn)更加精確的定位成為導航技術的關鍵所在,貫性導航系統(tǒng)避免了對于信號源的依賴,使用更加靈活,正逐漸成為個人導航技術研究的重要課題。然而慣性導航系統(tǒng)存在誤差累積、容錯能力差等特點,本文意在設計一種基于慣性傳感器的個人導航系統(tǒng),并結合機器學習支持向量機方法和粒子濾波,實現(xiàn)對于定位結果的優(yōu)化。 本文通過提取基于卡爾曼濾波的慣性導航系統(tǒng)所解算步長、航向變化角度等信息,建立基于步長、航向變化角度的航位推算運動模型,通過粒子濾波算法對運動軌跡進行優(yōu)化。優(yōu)化模塊包括平面地圖信息融合和活動識別糾正點融合兩方面：首先,平面地圖信息為航位推算正確性提供了重要的判斷依據(jù),本文假設在室內平面地圖已知的情況下,利用平面地圖信息,判斷粒子濾波推算的正確性,即對每一步中每一個粒子分別進行推算,剔除錯誤粒子,對粒子權重進行二次優(yōu)化,保證運動軌跡符合客觀事實,從而實現(xiàn)糾正；另外,對慣性傳感器以及氣壓傳感器數(shù)據(jù)進行預處理,包括坐標變換、高通濾波、計算氣壓差值等過程,抽象出訓練集進行訓練,通過兩層的支持向量機對人的活動進行識別,主要識別靜止、走路、上下樓梯、上下電梯等活動,針對其中包含了地理信息的活動提取糾正點,并將其提供給粒子濾波模塊,在粒子濾波推算的過程中糾正定位解算結果。 通過實驗可以發(fā)現(xiàn),融合平面地圖信息使系統(tǒng)修正了穿越墻壁的錯誤解算結果；加入活動識別糾正模塊后,在基于卡爾曼濾波的導航系統(tǒng)解算結果誤差較大情況下,累積誤差控制在2%以內。 本文的研究證明：通過融合平面地圖信息,影響粒子權重的更新與傳遞,有效的剔除了錯誤粒子；同時借助支持向量機,對慣性傳感器數(shù)據(jù)進行活動識別,識別準確率較高,通過二次優(yōu)化識別結果,將帶有地理信息的活動作為糾正點,粒子濾波融合糾正點信息完成修正,提高了系統(tǒng)整體精度；算法具有一定的可行性。
[Abstract]:Navigation technology, as the representative of many information technologies, is quietly entering the details of human life. How to adapt to the complex environment and integrate multi-sensor information to achieve more accurate positioning become the key of navigation technology. The penetration navigation system avoids the dependence on the signal source and becomes more flexible. It is gradually becoming an important subject of personal navigation technology. However, the inertial navigation system has the characteristics of error accumulation and poor fault tolerance. The purpose of this paper is to design a personal navigation system based on inertial sensors and combine machine learning support vector machine method with particle filter to optimize the localization results. In this paper, by extracting the information of step size and course change angle of inertial navigation system based on Kalman filter, the motion model of dead-reckoning based on step size and changing angle of heading is established. Particle filter algorithm is used to optimize the motion trajectory. The optimization module includes two aspects: the fusion of plane map information and the fusion of activity recognition correction points. Firstly, the plane map information provides an important basis for the correctness of the dead reckoning. This paper assumes that if the indoor plane map is known, the accuracy of particle filter calculation is judged by using the plane map information, that is, each particle in each step is calculated separately, the wrong particle is eliminated, and the particle weight is optimized twice. In addition, the data of inertial sensor and pressure sensor are preprocessed, including coordinate transformation, high-pass filtering, calculation of air pressure difference and so on, and the training set is abstracted for training. Through the two-layer support vector machine to identify the human activity, mainly to identify the activities such as static, walking, up and down stairs, up and down elevators and so on, to extract correction points for the activities containing geographic information, and to provide them to particle filter module. In the process of particle filter calculation, the result of location calculation is corrected. Through experiments, it can be found that the system can correct the result of error calculation through the wall by the fusion of plane map information, and after adding the activity recognition and correction module, the error of the result of navigation system based on Kalman filter is large. The cumulative error is controlled within 2%. The research in this paper proves that by merging the plane map information, which affects the updating and transmission of particle weight, it can effectively eliminate the wrong particles, and at the same time, with the support vector machine, the inertial sensor data can be recognized with high recognition accuracy. Through the quadratic optimization recognition result, the activity with geographical information is taken as the correction point, and the information of correction point is corrected by particle filter fusion, which improves the overall accuracy of the system, and the algorithm is feasible.
【學位授予單位】：廈門大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN966

【參考文獻】

相關期刊論文 前7條

1 李華軍,Sau-Lon JAMES HU;Multiple-Step Predictive Control for Offshore Structures[J];China Ocean Engineering;1999年03期

2 朱雷鳴;吳曉平;李建偉;吳星;;直角坐標系的歐拉旋轉變換及動力學方程[J];海洋測繪;2010年03期

3 胡士強,敬忠良;粒子濾波算法綜述[J];控制與決策;2005年04期

4 龍鳳;薛冬林;陳桂明;楊慶;;基于粒子濾波與線性自回歸的故障預測算法[J];計算機技術與發(fā)展;2011年11期

5 盧恒惠;劉興川;張超;林孝康;;基于三角形與位置指紋識別算法的WiFi定位比較[J];移動通信;2010年10期

6 楊常松;徐曉蘇;汪麗云;李瑤;;信息融合技術在INS／GPS／DVL組合導航中的應用研究[J];中國慣性技術學報;2006年05期

7 謝波;江一夫;嚴恭敏;任宏科;;個人導航融合建筑平面信息的粒子濾波方法[J];中國慣性技術學報;2013年01期

相關博士學位論文 前1條

1 肖延國;智能傳感器偵察網(wǎng)絡中的目標跟蹤算法研究[D];中國科學技術大學;2007年

，

本文編號：1641735