FM-based Indoor Localization

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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~