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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 謝宏昀(Hung-Yun Hsieh) | |
dc.contributor.author | Jun-Shen Chen | en |
dc.contributor.author | 陳俊伸 | zh_TW |
dc.date.accessioned | 2021-06-17T09:11:36Z | - |
dc.date.available | 2022-08-26 | |
dc.date.copyright | 2019-08-26 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-08-22 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74970 | - |
dc.description.abstract | 車子定位是一件非常具有挑戰的議題,因為城市中存在許多障礙物 (例如 樹、行人、建築物等)阻隔了通訊信號,而這些障礙物有可能隨時都在改變其位 置,往往不可預測,因此我們必須要想出更多方法或技術去應付這個多變的環境以確保定位演算法的穩定性。藉由裝置間互相分享位置資訊,可以增進定位效能,然而由於不同的GPS (Global Positioning System) 裝置和測距工具會根據位置、設計而有不同的精準度,如果我們盲目地使用這些位置資訊並且結合測量距離的資料,合作式定位的效能將會大打折扣。在本論文中,我們針對V2X (Vehicle to everything) 的通訊環境設計一套合作式定位演算法,考慮當定位資訊如:里程表 (Odometer)、GPS和RSS (Received Signal Strength)等不精準時,我們仍可得到穩定的定位品質。為了達到這個目的,這篇論文定義了一個新的參數叫做信心因子 (Confidence factor)去評斷哪些裝置預期能夠提供更好的定位資訊。這個信心因子與周圍裝置的位置精準度和幾何分布有關係。我們把信心因子融入貝式濾波器 (Bayesian filter)中,以調整個別裝置對定位的影響程度,並因此得到更佳的效能。由實驗結果顯示我們的方法能有效地降低不精準的資訊更新,跟傳統方法比較起來,在城市車載模擬環境中可以降低約50.2%的平均錯誤以及提升73.0%的效能穩定性,因此我們提出的合作式定位技術不止能增進定位的準確度也能提升其精密度。 | zh_TW |
dc.description.abstract | Traditional vehicle localization in urban is a challenge because of the complicated environment (e.g. tree, pedestrian, building, etc.) which blocks communication signals. We should consider more techniques in this changeable situation to ensure the stability of positioning. By sharing positioning information, we could improve the localization performance. However, we know that different GPS (Global Positioning System) devices and raging models will suffer from different precision due to position or design. If this information is blindly used and directly positioned with the ranging data, the performance will be greatly reduced. In this thesis, we focus on designing a cooperative positioning algorithm in the V2X (Vehicle to everything) scenario with the fusion model of the odometer, GPS and RSS (Received Signal Strength) communication device. With this approach, we can get stable positioning performance even if with imprecise measurements. To achieve this goal, this thesis defines the “confidence factor” to judge which information contains a more positive response. This confidence factor is related to the precision of the surrounding device’s position and geometric position. We use this factor to change the influence of the positional update with current observed data and then combine with the Bayesian filter to obtain the estimated position. The results show that our proposed algorithm can effectively reduce imprecise measurement updates. Comparing to the traditional method, our approach can get 50.2% improvement of average error and 73.0% improvement of stability. Thus, we can conclude our proposed algorithm not only improves the accuracy but also increases the precision. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T09:11:36Z (GMT). No. of bitstreams: 1 ntu-108-R04942051-1.pdf: 3246160 bytes, checksum: 32b6f0d508b6b19e996693aa5c85d7ab (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | ABSTRACT ii
LIST OF TABLES vi LIST OF FIGURES vii LIST OF ALGORITHMS ix LIST OF SYMBOLS x CHAPTER 1 INTRODUCTION 1 CHAPTER 2 BACKGROUND AND RELATED WORK 4 2.1 Introduction 4 2.2 Background 6 2.2.1 Bayes filter 6 2.2.2 Kalman filter 7 2.2.3 Extended Kalman filter 9 2.2.4 Particle filter 10 2.3 Related work 11 2.3.1 Proposal distribution 12 2.3.2 Kalman gain modification 14 2.4 Summary 15 CHAPTER 3 SYSTEM MODEL 16 3.1 Overview 16 3.2 Motion model 17 3.3 GPS 19 3.4 V2X 23 3.4.1 V2V 23 3.4.2 V2I 24 3.5 Ranging technique 24 3.5.1 TOA 24 3.5.2 RSS 25 3.6 Summary 26 CHAPTER 4 SCENARIO AND PROBLEM FORMULATION 28 4.1 Scenario 28 4.2 Problem formulation 29 4.2.1 Maximum likelihood estimator 30 4.2.2 Maximum a posterior estimator 32 CHAPTER 5 PROPOSED ALGORITHM 35 5.1 Introduction 35 5.2 RSS ranging model analysis 36 5.3 Confidence factor 39 5.3.1 Dealing with neighbors uncertainty 39 5.3.2 Dealing with geometry problem 44 5.3.3 Designing confidence factor 48 5.4 Proposed algorithm 48 5.4.1 Proposed PF-based algorithm 49 5.4.2 Proposed KF-based algorithm 50 CHAPTER 6 SIMULATION 54 6.1 Introduction 54 6.1.1 MSE and RMSE 55 6.1.2 Visualization for covariance error ellipse 57 6.1.3 Dynamic CRLB 58 6.2 RSS measurement model 59 6.2.1 MLE test 59 6.2.2 Name of algorithms 64 6.2.3 Theoretical DCRLB analysis 65 6.2.4 Fixed measurement analysis 66 6.2.5 Mean and variance analysis with time elapsing 67 6.2.6 Information source analysis 68 6.2.7 Different topology 70 6.2.8 Changes in equivalent measurement covariance analysis 75 6.2.9 Without estimated covariance 76 6.2.10 Shared GPS level of neighbors analysis 78 6.3 Distance measurement model 79 6.3.1 Different topology 81 6.3.2 Four fixed anchors 82 6.3.3 All devices use proposed algorithm 84 CHAPTER 7 CONCLUSION AND FUTURE WORK 87 REFERENCES 88 | |
dc.language.iso | en | |
dc.title | 車載通訊下融合不精準資訊之合作式定位技術 | zh_TW |
dc.title | Handling Position Uncertainty for Cooperative Localization in V2X Communications Based on Bayesian Filtering | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 周俊廷(Chun-Ting Chou),高榮鴻(Rung-Hung Gau) | |
dc.subject.keyword | 車載通訊,貝氏濾波器,合作式定位,融合模型,克拉馬-羅下限, | zh_TW |
dc.subject.keyword | VANET,Bayesian filter,Cooperative localization,Fusion model,CRLB, | en |
dc.relation.page | 92 | |
dc.identifier.doi | 10.6342/NTU201903830 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-08-23 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
顯示於系所單位: | 電信工程學研究所 |
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