九軸運動感測器融合及應用｜國立中央大學博碩士論文系統

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研究生：	楊郁莉 Yu-Li Yang
論文名稱：	九軸運動感測器融合及應用 9-Axis Motion Sensor Fusion and Its Applications
指導教授：	陳慶瀚 Ching-han Chen
口試委員:
學位類別：	碩士 Master
系所名稱：	資訊電機學院 - 資訊工程學系 Department of Computer Science & Information Engineering
畢業學年度：	100
語文別：	中文
論文頁數：	73
中文關鍵詞：	感測器、運動感測、九軸
外文關鍵詞：	9-axis, detective motion, sensor
相關次數：	點閱：12 下載：0
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典型的運動感測器包括加速度計、陀螺儀和磁力儀，傳統應用常使用單一類型感測器，然而近年來大量結合多感測器的電子產品快速普及，使得感測器融合成為重要的研究課題。
本論文提出一個智慧型融合方法，根據感測器使用情境和運動特性，利用機率類神經網路(PNN)估算九軸運動感測融合的調節參數，再將九軸資訊融合應用一起，互相彌補感測器不足的部分，如陀螺儀累積誤差的即時校正。我們以三個應用實例來驗證本論文提出的智慧型融合系統和方法的可行性，透過感測器融合系統輸出更穩定且精確的運
動資訊。所以，透過九軸運動感測器的智慧型融合系統，使運動量測和辨識結果的精度、解析度、穩定度及回應時間都得到了提升，同時也降低了應用端開發運動感測應用系統的複雜度。

Typical motion sensors include accelerometer, gyroscope and magnetometer. In past
application we usually use single type sensor. But in recent years, a lot of electronic products
are developed rapidly and usually combined with many sensors. Therefore the sensor fusion
issue becomes more and more important.
In this thesis we proposed the intelligent sensor fusion method regarding the different
situations and motion properties. We use PNN to estimate and adjust the parameter of 9-axis
motion sensor fusion. And then we can use these information for complementary of other
sensor to improve the insufficient part ( ex: real-time calibrating the gyroscope bias drift). In the experiments we can get more stable and accurate motion data by intelligent fusion system.
In conclusion, intelligent fusion system of 9-axis motion sensor can increase the system performance, including accuracy, resolution, stability and response time. In the same time it reduces the complexity of developing application system in detecting motion.

摘要	i
Abstract	ii
致謝	iii
目錄	iv
圖目錄	vii
表目錄	ix
第一章	緒論	1
1	研究動機	1
2	研究目標	2
3	論文架構	3
第二章	運動感測器原理	4
1	微機電技術	4
2	加速度計	5
3	磁力計	6
4	陀螺儀	8
5	九軸運動感測LSM303DLH, L3G4200D	10
6	感測器資料傳輸介面	12
6.1	I2C規範	12
6.2	I2C資料傳輸與接收	14
第三章	資料前處理	15
1	運動資料前處理	15
1.1	感測器校正	15
1.2	加速度與重力的抽離	17
1.3	雜訊濾除	19
2	運動特徵擷取	23
2.1	運動慣量	23
2.2	三軸傾角	24
2.3	訊號強度向量(SVM)	30
2.4	訊號區域強度(SMA)	30
第四章	九軸感測器融合	31
1	尤拉角融合	31
1.1	使用陀螺儀計算尤拉角	33
1.2	加速度計與磁力計融合	34
2	智慧型九軸感測融合系統	35
3	機率類神經網路(PNN)	37
4	PNN適應性九軸感測融合系統	41
第五章	系統整合與驗證	45
1	實驗設備與環境	45
1.1	實驗設備	45
1.2	SIOC	47
1.3	LSM303DLH	49
1.4	L3G4200D	50
1.5	EGBT-045MS	51
2	運動偵測實驗	53
2.1	移動	54
2.2	震動	55
2.3	衝擊	56
2.4	旋轉及傾斜	57
3	步態辨識實驗	60
3.1	運動步態的決策樹辨識方法	61
3.2	實驗結果	62
4	室內導航實驗	65
4.1	室內定位原理	65
4.2	實驗結果	67
第六章	結論與未來方向	69
1	結論	69
2	未來方向	70
參考文獻	71

                                

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