fm-based indoor localization
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FM-based Indoor Localization. 20130107 TsungYun. Outline. Introduction Architecture Experiment Result FM-based Indoor localization Temporal Variations Different Buildings Fine-Grain Localization Conclusion. Introduction. - PowerPoint PPT PresentationTRANSCRIPT
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FM-BASED INDOOR LOCALIZATION
20130107 TsungYun
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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 也太強了吧…
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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
~Thanks for your listening~