20130107

Report
1
FM-BASED INDOOR
LOCALIZATION
20130107 TsungYun
2
Outline
• Introduction
• Architecture
• Experiment
• Result
• FM-based Indoor localization
• Temporal Variations
• Different Buildings
• Fine-Grain Localization
• Conclusion
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Introduction
• The major challenge for fingerprint-based approach is the
design of robust and discriminative signatures
• Existing approaches exhibit several limitations
• This paper study the feasibility of leveraging FM
broadcast radio signals for fingerprinting indoor
environments
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Introduction
• WiFi - The most popular design
• the high operating frequency makes it susceptible to human
presence
• Optimized by frequency hopping to improve network’s throughput
(RSSI values change across WiFi channels)
• WiFi RSSI values exhibit high variation over time
• the area of coverage of a WiFi access point is significantly reduced
due to the presence of walls and metallic objects, easily creating
blind spots (i.e. basement, parking lots, corners in a building, etc.)
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Introduction
• FM broadcast radio
• No need for extra deployment
• Lower frequency
• Stronger signal strength
• Lower power consumption
• Outdoor localization
• Zip code level [10]
• Tens of meters [8]
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Introduction
• FM-Based indoor localization
• internal structure of the building can significantly affect the
propagation of FM radio signals
• achieve similar room-level accuracy in indoor environments when
compared to WiFi signals
• FM and WiFi signals are complementary
• their localization errors are independent
• Combine FM and WiFi
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Architecture
• Training stage
• Fingerprint database
• Site survey artificially
• Crowd-sourced from freely services (e.g. Google)
• Positioning stage (Testing)
• Find the closest fingerprint (1-NN)
• Use Euclidean and Manhattan distance
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Architecture
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Architecture
• Augment the WiFi wireless fingerprint to include the RSSI
information obtained by FM radio signals
• Extract more detailed information at the physical layer for
FM radio signals
• SNR (signal to noise): 0~128 db
• Multipath: 0~100
• Frequency offset: -10~10
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Architecture
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Experiment
• Three different buildings
• Office building
• 3 different floors
• Totally 119 small rooms (9 ft x 9 ft)
• 434 WiFi APs
• Shopping mall
• 13 large rooms of varying size and shape
• 379 WiFi APs
• Residential apartment
• 5 different rooms
• 117 WiFi APs
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Experiment
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Experiment
• Hardware
• WiFi Link 5300 from Intel
• SI-4735 FM radio receiver from Silicon Lab
• Data collection (the official building)
• 3 random point each rooms
• collect 32 FM & M WiFi signals each location
•
•
(RSSI, SNR, MULTIPATH, FREQOFF)
(WiFi signal)
• each fingerprint
• 3 data set A1, A2, A3
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Result – FM-based Indoor localization
• Focus on RSSI value only
• Use 2 dataset as database, the other as testing data (the office
building)
• Average accuracy across 3 combinations
• FM and WiFi RSSI values achieve similarly high room-level
accuracies (close to 90%)
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Result – FM-based Indoor localization
• The localization errors in terms of physical distance are
lower in the case of WiFi
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Result – FM-based Indoor localization
• 3 squares correspond to the 3 floors profiled
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Result – FM-based Indoor localization
• Leverage additional information at the physical layer
(SNR, MULTIPATH, FREQOFF) to generate more robust
FM signatures
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Result – FM-based Indoor localization
• Combining all signal indicators into a single signature
achieves higher accuracy than any individual signal
indicator
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Result – FM-based Indoor localization
• distance matrix (c) appears to be significantly less noisy
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Result – FM-based Indoor localization
• Combining FM and Wi-Fi
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Result – FM-based Indoor localization
• FM localization errors are not correlated with the WiFi
errors
• Using more FM indicators removes many of the
localization errors by FM RSSI
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Result – FM-based Indoor localization
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Result – FM-based Indoor localization
• All the erroneously predicted rooms are on the same floor
and nearby the true rooms
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Result – FM-based Indoor localization
• Sensitivity to number of FM stations
• About 30 FM stations are required
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Result – FM-based Indoor localization
• Sensitivity to number of WiFi APs
• About 50 WiFi APs are required
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Result – FM-based Indoor localization
• Combine WiFi & FM signals
• 50 WiFi APs and 25 FM stations are required
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Result – Temporal Variations
• FM
• Continuous Monitoring of FM Signals Over Ten Days
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Result – Temporal Variations
• Using ten days data as testing data
• FM signals are stable
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Result – Temporal Variations
• WiFi
• Collect four additional sets of fingerprints on the second floor on
four different days
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Result – Temporal Variations
• Temporal variations lead to noticeable degradation of
accuracy in WiFi case
• FM signatures seem to be less susceptible
• Adding more datasets into the database can lead to
notable gains in the localization accuracy
• A bigger fingerprint database can better cope with temporal
variations
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Result – Different Buildings
• Shopping Mall
• 5 data set on three days (Weekends & Wed.)
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Result – Different Buildings
• Shopping Mall - 5 data set on three days (Weekends & Wed.)
• The ceilings are taller and the rooms are sparser and bigger => like
outdoor environment
• FM signatures perform slightly worse compared to the office
building
• WiFi signatures perform significantly better
• more fingerprints in the database increases localization accuracy
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Result – Different Buildings
• Residential Building
• 2 data sets on two days, different FM stations
• localization accuracies are independent of the building
type
• FM based indoor localization approach is applicable to
other geographic regions with different FM broadcast
infrastructure
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Result – Fine-Grain Localization
• More data collection (2-nd floor of the official B.)
• 100 locations along the hallway
• Distance between two adjacent locations is one foot
• 3 data sets in 3 different days
• Leave one out evaluation
• use one and only one location at a time from the dataset as the
testing fingerprint
• Use the other 99 signatures as database
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Result – Fine-Grain Localization
• Each location is identified as one of its two neighbors on
the line in terms of FM
• WiFi RSSI signatures exhibit larger errors
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Result – Fine-Grain Localization
•
• FM RSSI signatures have the necessary spatial resolution
For more accurate fingerprinting, even better than WiFi
signature
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Result – Fine-Grain Localization
• Temporal Variation
• FM still outperforms WiFi significantly
• Device Variation
• Data set 3 is collected by a different FM receiver
• Localization error doesn’t increase significantly
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Conclusion
• Propose to exploit additional information at the physical
layer to create more reliable fingerprinting of indoor
spaces
• Demonstrate that FM and WiFi signals are
complementary in the sense that their localization errors
are independent
• Study in detail the effect of wireless signal temporal
variation

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