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LOCATING IN FINGERPRINT SPACE:WIRELESS INDOOR LOCALIZATION WITH LITTLE HUMAN INTERVENTION
Zheng Yang, Chenshu Wu, and Yunhao Liu
MobiCom 2012
- Sowhat 2012.08.20
MOTIVATION
RSSI fingerprinting-based localization
Site survey Time-consuming Labor-intensive Vulnerable to environmental dynamics Inevitable
MULTIDIMENSIONAL SCALING (MDS)
Information visualization for exploring similarities/dissimilarities in data
STRESS-FREE FLOOR PLAN
MDS
Geographical distance ≠ Walking distance,Ground-truth floor plan –conflict with measured distance
Sample grids in a floor plan (grid length l = 2m)
Distance matrix D = [dij],dij = walking distance between point i and j
Stress-free floor plan – 2D & 3D
FINGERPRINT SPACE – FINGERPRINT & DISTANCE MEASUREMENT
Fingerprints and distance collection Record while walking Footsteps every consecutive steps by accelerometer Set of fingerprints, F = {fi, i = 1~n}
Distance(footsteps) matrix, D’=[d’ij]
Pre-processing Merge similar fingerprints (δij<ε)
Accelerometer readingTwice integration Distance: NoiceLocal variance threshold method Step count
Stride lengths vary? MDS tolerate measurement errors
FINGERPRINT SPACE – FINGERPRINT SPACE CONSTRUCTION
Adequate fingerprints & distance1. 10x sample locations in stress-free floor plan2. First several days for training
d’ij unavailable d’ij = d’ik + d’kj
Shortest path update D’ all-pairs of fingerprints Floyd-Warshall algorithm
MDS Fingerprint space 2D & 3D
MAPPING –CORRIDOR & ROOM RECOGNITION
Corridor recognition (Fc) Higher prob. on a randomly chosen shortest path Minimum spanning tree Betweenness Watershed
1. Size(corridor) / Size(all)2. Large gap of betweenness values
Room recognition (FRi) k-means algorithm
(k = number of rooms)
Classify fingerprints into the corridor or rooms
Fingerprints collected near “doors”
PD = {p1, p2, …, pk}, stress-free floor planFD , fingerprint space
distance matrix D and D’ l = (lp1, lp2, …, lp k-1)l’ = (lf1, l’f2, …, l’f k-1)
cosine similarity
MAPPING –REFERENCE POINT
Near-door fingerprints, FD,labeled with real locations
1. Map near-door fingerprintsto real locations (FD → PD)
2. Map rooms to rooms
Floor-level transformation Stress-free floor plan ≠ Fingerprint space
∵ translation, rotation, reflection Transform matrix,
xi = coordinate of fi ∈ FD
yi = coordinate of pi ∈ PD
For fingerprint with coordinate xreal location = sample location closest to Ax + B
Room-level transformation Room by room Doors and room corners as reference point Transformation matrix
MAPPING –SPACE TRANSFORMATION
HARDWARE AND ENVIRONMENT
2 Google Nexus S phones Typical office building covering 1600m2
16 rooms,5 large – 142m2, 7 small, 4 inaccessible
26 Aps, 15 are with known location 2m x 2m grids, 292 sample locations
EXPERIMENT DESIGN
5 hours with 4 volunteers Fingerprints recording – every 4~5 steps
(2~3m) Accelerometer –
work in different frequency based on detecting movement
600 user traces, with 16498 fingerprints Corridor, >500 paths
Small rooms, >5 pathsLarge rooms, >10 paths
Half of data used for training,half …………………... in operating phase
STEP COUNT 5 ~ 200 footsteps
Error rate = 2% in number of detected steps
Accumulative error of long path Unobvious performance drop ∵ only use inter-fingerprint step counts
CORRIDOR RECOGNITION
Refining Perform MST iteratively Sift low betweenness Until MST forms a single line
LOCALIZATION ERROR Emulate 8249 queries using real data on LiFS Location error
Average,LiFS = 5.88mRADAR = 3.42m
Percentile of LiFS80 < 9m / 60 < 6m
Caused bysymmetric structure
Fairly reasonable!
Room error = 10.91%
DISCUSSION
Global reference point Last reported GPS location
Locations of APsSimilar surrounding sound signature…
Could be added in LiFS for more robust mapping Key for symmetric floor plans / multi-floor fuildings
Large open environment
CONCLUSION
LiFS Spatial relation of RSSI fingerprints + Floor plan Low human cost
Comments Clear architecture Not specific descriptions in evaluation
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