Presentation is loading. Please wait.

Presentation is loading. Please wait.

Copyright ©2013 by SJTU, IWCT. Dongchuan Road #800, Minhang, Shanghai,200240 All rights reserved. Indoor Localization with a Crowdsourcing based Fingerprints.

Published byLiliana Powers Modified over 9 years ago

Similar presentations

Presentation on theme: "Copyright ©2013 by SJTU, IWCT. Dongchuan Road #800, Minhang, Shanghai,200240 All rights reserved. Indoor Localization with a Crowdsourcing based Fingerprints."— Presentation transcript:

1 Copyright ©2013 by SJTU, IWCT. Dongchuan Road #800, Minhang, Shanghai,200240 All rights reserved. Indoor Localization with a Crowdsourcing based Fingerprints Collecting

2 Copyright ©2013 by SJTU, IWCT. Dongchuan Road #800, Minhang, Shanghai,200240 All rights reserved. System Architecture

3 Copyright ©2013 by SJTU, IWCT. Dongchuan Road #800, Minhang, Shanghai,200240 All rights reserved. Crowdsourcing based fingerprint extraction methods Localization Algorithms based on clustering theory Key Technology

4 Copyright ©2013 by SJTU, IWCT. Dongchuan Road #800, Minhang, Shanghai,200240 All rights reserved. In crowdsourcing model, multiple users will upload fingerprints via diverse devices Our method extract fingerprint value based on RSS probability estimation, choose the optimum value from upload samples Kernel density estimation eliminates device diversity than Gaussian probability estimation Fingerprints Extraction

5 Copyright ©2013 by SJTU, IWCT. Dongchuan Road #800, Minhang, Shanghai,200240 All rights reserved. Comparison of Gaussian and Kernel density estimation: Fingerprints Extraction

6 Copyright ©2013 by SJTU, IWCT. Dongchuan Road #800, Minhang, Shanghai,200240 All rights reserved. Based on kernel density estimation, choose optimum value from multiple upload RSS samples by multiple users by diverse devices. Fingerprints Extraction

7 Copyright ©2013 by SJTU, IWCT. Dongchuan Road #800, Minhang, Shanghai,200240 All rights reserved. MMC-KNN algorithm: find M most matched clusters, then apply KNN principle to choose out matched fingerprint Use affinity propagation to process clustering: Localization Algorithm: MMC-KNN

8 Copyright ©2013 by SJTU, IWCT. Dongchuan Road #800, Minhang, Shanghai,200240 All rights reserved. How to find out the M most matched cluster? –Consider uploaded observation’s connections and similarities with all exemplars –Apply affinity propagation again and get responsibility vector: –choose the M most matched cluster by sort this responsibility vector Localization Algorithm: MMC-KNN

9 Copyright ©2013 by SJTU, IWCT. Dongchuan Road #800, Minhang, Shanghai,200240 All rights reserved. Assign a weight factor to each cluster’s fingerprints Apply a grid window filter to filter a region which has the maximum weight, with the purpose to restrict KNN applied to a bursting region Localization Algorithm: MMC-KNN

10 Copyright ©2013 by SJTU, IWCT. Dongchuan Road #800, Minhang, Shanghai,200240 All rights reserved. Average error distance with different matched cluster number and grid window size for Nexus-S Real-time experimental testbed

11 Copyright ©2013 by SJTU, IWCT. Dongchuan Road #800, Minhang, Shanghai,200240 All rights reserved. 220 observation’s error distance statistic with best performance parameters for Nexus-S Real-time experimental testbed

12 Copyright ©2013 by SJTU, IWCT. Dongchuan Road #800, Minhang, Shanghai,200240 All rights reserved. CDF of location error distance for different algorithms Real-time experimental testbed

13 Copyright ©2013 by SJTU, IWCT. Dongchuan Road #800, Minhang, Shanghai,200240 All rights reserved. Comparison of different types devices’ location performance under diverse fingerprint databases Real-time experimental testbed

Download ppt "Copyright ©2013 by SJTU, IWCT. Dongchuan Road #800, Minhang, Shanghai,200240 All rights reserved. Indoor Localization with a Crowdsourcing based Fingerprints."

Similar presentations

Measure Projection Analysis

Measure Projection Analysis
Document Clustering Carl Staelin. Lecture 7Information Retrieval and Digital LibrariesPage 2 Motivation It is hard to rapidly understand a big bucket.

Document Clustering Carl Staelin. Lecture 7Information Retrieval and Digital LibrariesPage 2 Motivation It is hard to rapidly understand a big bucket.
Reasonable Resolution of Fingerprint Wi-Fi Radio Map for Dense Map Interpolation University of Seoul Wonsun Bong, Yong Cheol Kim Auckland, New Zealand.

Reasonable Resolution of Fingerprint Wi-Fi Radio Map for Dense Map Interpolation University of Seoul Wonsun Bong, Yong Cheol Kim Auckland, New Zealand.
Human Identity Recognition in Aerial Images Omar Oreifej Ramin Mehran Mubarak Shah CVPR 2010, June Computer Vision Lab of UCF.

Human Identity Recognition in Aerial Images Omar Oreifej Ramin Mehran Mubarak Shah CVPR 2010, June Computer Vision Lab of UCF.
Salvatore giorgi Ece 8110 machine learning 5/12/2014

Salvatore giorgi Ece 8110 machine learning 5/12/2014
K Means Clustering , Nearest Cluster and Gaussian Mixture

K Means Clustering , Nearest Cluster and Gaussian Mixture
Clustering (1) Clustering Similarity measure Hierarchical clustering Model-based clustering Figures from the book Data Clustering by Gan et al.

Clustering (1) Clustering Similarity measure Hierarchical clustering Model-based clustering Figures from the book Data Clustering by Gan et al.
Error Estimation for Indoor 802.11 Location Fingerprinting.

Error Estimation for Indoor Location Fingerprinting.
Uncertainty Representation. Gaussian Distribution variance Standard deviation.

Uncertainty Representation. Gaussian Distribution variance Standard deviation.
Density Surfaces Arturas Mazeika. Outline of the Presentation A very brief overview of the architecture of the 3DVDM system An intuition of the density.

Density Surfaces Arturas Mazeika. Outline of the Presentation A very brief overview of the architecture of the 3DVDM system An intuition of the density.
Object Recognition with Invariant Features n Definition: Identify objects or scenes and determine their pose and model parameters n Applications l Industrial.

Object Recognition with Invariant Features n Definition: Identify objects or scenes and determine their pose and model parameters n Applications l Industrial.
EE 290A: Generalized Principal Component Analysis Lecture 6: Iterative Methods for Mixture-Model Segmentation Sastry & Yang © Spring, 2011EE 290A, University.

EE 290A: Generalized Principal Component Analysis Lecture 6: Iterative Methods for Mixture-Model Segmentation Sastry & Yang © Spring, 2011EE 290A, University.
MACHINE LEARNING 9. Nonparametric Methods. Introduction Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1) 2 

MACHINE LEARNING 9. Nonparametric Methods. Introduction Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1) 2 
Mean-Shift Algorithm and Its Application Bohyung Han

Mean-Shift Algorithm and Its Application Bohyung Han
Relevance Feedback based on Parameter Estimation of Target Distribution K. C. Sia and Irwin King Department of Computer Science & Engineering The Chinese.

Relevance Feedback based on Parameter Estimation of Target Distribution K. C. Sia and Irwin King Department of Computer Science & Engineering The Chinese.
Texture Reading: Chapter 9 (skip 9.4) Key issue: How do we represent texture? Topics: –Texture segmentation –Texture-based matching –Texture synthesis.

Texture Reading: Chapter 9 (skip 9.4) Key issue: How do we represent texture? Topics: –Texture segmentation –Texture-based matching –Texture synthesis.
Region Filling and Object Removal by Exemplar-Based Image Inpainting

Region Filling and Object Removal by Exemplar-Based Image Inpainting
Collaborative Ordinal Regression Shipeng Yu Joint work with Kai Yu, Volker Tresp and Hans-Peter Kriegel University of Munich, Germany Siemens Corporate.

Collaborative Ordinal Regression Shipeng Yu Joint work with Kai Yu, Volker Tresp and Hans-Peter Kriegel University of Munich, Germany Siemens Corporate.
Semi-Supervised Learning D. Zhou, O Bousquet, T. Navin Lan, J. Weston, B. Schokopf J. Weston, B. Schokopf Presents: Tal Babaioff.

Semi-Supervised Learning D. Zhou, O Bousquet, T. Navin Lan, J. Weston, B. Schokopf J. Weston, B. Schokopf Presents: Tal Babaioff.
Pattern Recognition. Introduction. Definitions.. Recognition process. Recognition process relates input signal to the stored concepts about the object.

Pattern Recognition. Introduction. Definitions.. Recognition process. Recognition process relates input signal to the stored concepts about the object.

Similar presentations

Ads by Google