1 / 80

Chapter 19 Wireless Network-Assisted Localization

Chapter 19 Wireless Network-Assisted Localization. Yu-Chee Tseng cs.nctu.edu.tw. Outline. Model of Wireless Location Systems Classification of WLS RADAR BikeNet: An Application Some Research Works on WLS 3 technical papers Pedestrian Localization. Model. (week 1).

michaelj
Download Presentation

Chapter 19 Wireless Network-Assisted Localization

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Chapter 19Wireless Network-Assisted Localization Yu-Chee Tseng cs.nctu.edu.tw

  2. Outline • Model of Wireless Location Systems • Classification of WLS • RADAR • BikeNet: An Application • Some Research Works on WLS • 3 technical papers • Pedestrian Localization

  3. Model

  4. (week 1) Location: Mobile Computing’s Future • LBS = location-based services • Examples: • where you are • how best to get to a destination • whether friends are nearby • local weather forecast • where businesses of interests in this area are located • companies to track packages, vehicles, buese, etc. • US 9-1-1 emergency localization by 2012

  5. Inside LBS • GPS devices: Garmin, Magellan, and TomTom all sale dedicated GPS devices • Hardware: chip makers (such as Qualcomm’s gpsOne chipset) • Software: Google’s navigation service

  6. LBS服務分類 • 封閉系統: • 不具有無線資料傳輸能力，以GPS為代表 • 但是如果有臨時的道路封閉或臨時的車禍路況，就無法即時反映在系統上 • 行動位置服務（Mobile Location Based Service, MLBS）系統: • 以GSM、3G或WiFi為資料、語音甚至多媒體的通訊管道、並同時具有定位功能 • 如手機的緊急求救可同時回傳位置資訊，爭取救援時效

  7. Augmenting GPS with Cellular and WiFi • cell-based or triangulation-based • examples: • Skyhook has LBS software to determine location via triangulation of cellular and WiFi signals • XPS: hybrid positioning system • using 50 million WiFi APs to enhance accuracy • Google’s My Location: • using database of cell tower positions • WiFi triangulation • Microsoft may include LBS capabilities in its upcoming Windows 7.

  8. GPS + WiFi • GPS導航定位功能與日常生活越來越密切，除了車載導航，許多手機都開始內建GPS或A-GPS功能. • 在個人行動應用的領域，當使用者進入騎樓、巷弄或室內時，無法清楚收到GPS衛星訊號，定位功能無法發揮作用，必須尋求其他的輔助方案。 • Wi-Fi定位技術有兩種型態 • 一為運用已佈建完成之無線網路環境 • 另一種是無線網路隨同定位系統一起佈建之環境 (more costly) • 龍頭大廠Broadcom推出升級版的行動定位服務（LBS）架構, 將Wi-Fi定位功能增至LBS組合中 • 藉由偵測Wi-Fi無線存取點並與一個己知的地點位置資料庫比對產生精確的定位資訊

  9. Classification

  10. Cell-Identification • 是最基本的行動定位技術，利用行動終端連線時所處之基地台位置來確認用戶端位置 • 定位準確度取決於基地台涵蓋面積及密度。 • 在鄉村地區，基地台稀少覆蓋範圍大，所以定位準確度很差； • 而在都市地區，基地台覆蓋範圍較小，且密度較高，定位準確度相對提高許多 • 平均大約為200公尺至2公里。

  11. Tri-angulation (三角定位 ) • 利用訊號蜂巢交叉點的定位技術，當行動終端收到基地台訊號，利用其強度計算行動終端與基地台距離，並以此距離為半徑畫出一個覆蓋圓弧, 畫出3個覆蓋圓弧，其交接點處即為行動終端位置

  12. TOA（Time of Arrival） • 基於訊號傳輸時間的定位技術 • 需調整基地台設置，讓其時間可以同步，使相鄰基地台能夠同時監控同一行動終端的信號，隨著基地台個數增加其準確度也會提升。 • TOA基於測量信號從行動終端發送出去並到達訊號測量基地台（3個或更多）的時間，並將此時間轉換成距離，畫出各基地台的覆蓋圓弧，取其焦點即為行動終端位置。 • 為了使時間誤差不會對定位效果造成影響，基地台之間的訊號傳輸同步顯得相對重要，即使是1微秒的時間誤差，也會導致兩三百公尺的誤差。

  13. TDOA (Time Difference of Arrival) • TDOA也是基於訊號傳輸時間的定位技術 • 利用一個參考基地台與多個輔助基地台收到訊號到達的時間差，再將之轉換成距離 • 利用雙曲線的特性，即雙曲線上的點到兩焦點距離之差為定值，帶入雙曲線的方程式中，多組雙曲線方程式聯立求解，即得到行動終端位置。 • 容易實現，且行動終端與基地台間無需保持精確同步

  14. RSS定位 • RSS=Received Signal Strength • 訓練階段: 定位原理是行動終端利用無線網路來收集無線信號，並藉由電播傳播模型或內外差法，得到其它收集訊號的區域，得到無線電波圖(radio map) • 追蹤定位階段: 行動終端得到的訊號與電波圖比較，計算與模擬電波圖上相似機率，以機率最高處為行動終端位置。 • 優勢: 可用於室內, 大樓的各樓層

  15. (week 2) RADAR for Indoor Localization

  16. Introduction • Positioning techniques can be categorized into • Range-based: triangulation, trilateration, multi-lateration, pattern-matching localization • Range-free: use the information of hop counts, zone-based localization • Distance/angle/pattern measurement • Distance: time-of-arrival (ToA), time-difference-of-arrival (TDoA), signal strength attenuation • Angle: angle-of-arrival (AoA) • Pattern: signal strength

  17. <xn, yn> s i signal strength vector: [s1, s2, …, sm] <x2, y2> <xi, yi> <x1, y1> i 1 s real-time data Pattern-Matching Localization Overview access point (AP) Training Phase Positioning Phase avg. signal strength: [ i,1, i.2,…, i.m] training data training location <x1, y1>1 <x2, y2> 2 . . . <xn, yn>n Location Database Pattern-Matching Localization Algorithm <x, y>

  18. Challenges with Pattern-Matching Localization • Unstable signal strengths and unpredictable multipath effect • High computation cost: huge location database to match, especially in large-scale environments • Environment changes and training cost • Maintenance (movement/lost of beacons) • Publications • S.-P. Kuo, B.-J. Wu, W.-C. Peng, and Y.-C. Tseng, "Cluster-Enhanced Techniques for Pattern-Matching Localization Systems", IEEE Int'l Conf. on Mobile Ad-hoc and Sensor Systems (MASS), 2007 • S.-P. Kuo, Y.-C. Tseng, and C.-C. Shen, "Increasing Search Space for Pattern-Matching Localization Algorithms by Signal Scrambling ", IEEE Asia-Pacific Wireless Communications Symposium, 2007. • S.-P. Kuo, Y.-C. Tseng, and C.-C. Shen, "A Scrambling Method for Fingerprint Positioning Based on Temporal Diversity and Spatial Dependency", IEEE Trans. on Knowledge and Data Engineering, submitted. • S.-P. Kuo, H.-J. Kuo, Y.-C. Tseng, and Y.-F. Lee, "Detecting Movement of Beacons in Location-Tracking Wireless Sensor Networks", IEEE VTC, 2007-Fall.

  19. The BikeNet Mobile Sensing System for Cyclist Experience Mapping Shane B. Eisenman**, Emiliano Miluzzo*, Nicholas D. Lane* Ron A. Peterson*, Gahng-Seop Ahn** and Andrew T. Campbell* *Dartmouth College, **Columbia University Sensys 07

  20. BikeNet System Overview

  21. BikeNet Social Network Shared Data Air Quality Braking Noise Coasting Car Density Distance

  22. System Architecture • Hardware • Mobile Sensor Tier • Logical Bike Area Network (BAN) • Sensor Access Point Tier • Static v.s. Mobile • An unmodified Tmote Invent plugged into the USB port of an Aruba AP-70 IEEE 802.11a/b/g access point • Nokia N80 paired to a custom built Bluetooth/802.15.4 gateway (GSM/GPRS  Server Tier) • Server Tier • Backend server: Query and Visualization

  23. The Sensing System PhysicalBike Area Network (BAN) BikeNet niclane@cs.dartmouth.edu

  24. The Sensing System Logical Bike Area Network (BAN) BikeNet niclane@cs.dartmouth.edu

  25. The Sensing System Backend Services BikeNet niclane@cs.dartmouth.edu

  26. System Evaluation Health index = 1.0 − a1 ∗ CarDensity − a2 ∗ CO2Level − a3 ∗ SoundLevel. Performance index = b1 ∗ HillAngle + b2 ∗ WheelSpeed/PedalSpeed + b3 ∗ Distance.

  27. Performance Index Distance Duration Path Slope Coasting Speed Per f . = b1 ∗HillAngle+b2 ∗WheelSpeed/PedalSpeed +b3 ∗Distance.

  28. Health Index Noise Traffic Density C02 Level Health = 1.0−a1 ∗CarDensity−a2 ∗CO2Level −a3 ∗ SoundLevel.

  29. BikeView: Present and Share

  30. Public Utility Sensing: CO2 Map ~ Hanover NH

  31. Conclusions • BikeNet represents the first comprehensive mobile sensing system quantifying the cyclist experience. • Performance/Fitness/Health • Personal sensing + Social sensing

  32. (week 4) Some Technical Papers Signal scrambling (IEEE TKDE) Data clustering (MASS 2007) Beacon movement (VTC 2007, IEEE TMC)

  33. Localization: Signal Scrambling A Scrambling Method for Pattern-Matching Positioning Based on Temporal Diversity and Spatial Dependency

  34. Difficulties • Multipath effect results in low accuracy for pattern-matchinglocalization. • Most of pattern-matching localization schemes adopt traditional classification, but ignore some unique features. • Ex. Continuous samples should have high similarity as well as diversity.

  35. Observations • A positioning error could be generated by a small portion of interfered signal strengths. • Counting on one single observation is unreliable. • We can enlarge the search space by multiple continuous observations. • Continuous observations may have some degrees of • Temporal diversity: For a sequence ofobservations on a beacon, diversified signal strengths may be seen. • Spatial dependency: For a serious of estimated locations, they should be close each other.

  36. new search space scrambled beacons Signal Scrambling Concept • Select a set of beacons (access points) for scrambling • Ex. top d =2 beacons with strongest signal strengths • Average the signal strengths of the other beacons • Generate wdscrambled samples from the past w=2 samples.

  37. Localization: Clustering of Location Database for pattern-matching localization in large-scale sensing field (such as a wireless city)

  38. Challenges • Scalability problem when the field is large. • High computation cost in the positioning phase • Long system response time (critical to real-time applications) • To reduce computation cost in the positioning phase: • apply clustering technique to fragment database into a number of sets. • examine only one cluster in the positioning phase

  39. <xn, yn> i s <x2, y2> <xi, yi> <x1, y1> i 1 s real-time data Cluster Scheme Overview access point (AP) Training Phase Positioning Phase avg. signal strength: [ i,1, i.2,…, i.m] signal strength vector: [s1, s2, …, sm] training data training location <x1, y1>1 <x2, y2> 2 . . . <xn, yn>n Location Database Pattern-Matching Localization Algorithm <x, y> C* Clustering

  40. appears at <x1,y1> Cluster C2 Real-time received signal strengths s 2 RSS of AP 2  1 C1 1 If s is in the shaded region 3  2 C2  3 C3 RSS of AP 1 Cluster C1 Cluster C3 Difficulty: False Cluster Selection Cluster C2 1,1: received signal of AP 1 at <x1,y1> 1,2: received signal of AP 2 at <x1,y1> <x1, y1>(1,1, 1,2) The region that the signal may fluctuate Cluster C3 The cluster that contains the true location C2 ≠ False cluster selection occurs!!! Considering 2 APs in the environment

  41. RSS of AP 2 average overlapping degree RSS of AP 1 Clustering Allowing Overlaps • Design new clustering techniques that allow a training location to join multiple clusters constructed by k-means. • overlapping degree: the number of clusters that a training location can join. • Complexity: Cluster C2 If C2 also contains <x1, y1>, the false cluster selection problem can be avoided Cluster C2 <x1, y1>(1,1, 1,2) Cluster C1 Cluster C3 Considering 2 APs in the environment without overlaps (k-means) with overlaps

  42. Localization: Beacon Movement Detection

  43. Beacon Movement Detection Problem • Maintenance issue: beacon movement/failure • Ex: What happens if some beacons are moved by accident? • Goal: • Automatically detect the beacon movement events • Remove the data of these unreliable beacons from the database to improve accuracy Result: More serious localization error!!

  44. b2 b1 b1 b2 b3 Challenges • Ambiguity • More Ambiguity: b2 is moved! b1 is moved! b1 = b2 b3 O = O’ Two different scenarios will induce equal observations!

  45. System Model Positioning Procedure BMD Procedure ( t =0 denotes the initial time)

More Related