FM-based Indoor Localization

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

3. 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

4. Introduction • WiFi - The most popular design • the high operating frequencymakes it susceptible to human presence • Optimized by frequency hoppingto 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.)

5. 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]

6. 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 WiFisignals • FM and WiFisignals are complementary • their localization errors are independent • Combine FM and WiFi

7. 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

8. Architecture

9. 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

10. Architecture

11. 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 WiFiAPs • Residential apartment • 5 different rooms • 117 WiFi APs

12. Experiment

13. 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 & MWiFi signals each location • (RSSI, SNR, MULTIPATH, FREQOFF) • (WiFi signal) • each fingerprint • 3 data set A1, A2, A3

14. 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%)

15. Result – FM-based Indoor localization • The localization errors in terms of physical distanceare lower in the case of WiFi

16. Result – FM-based Indoor localization • 3 squares correspond to the 3 floors profiled

17. Result – FM-based Indoor localization • Leverage additional information at the physical layer (SNR, MULTIPATH, FREQOFF) to generate more robust FM signatures

18. Result – FM-based Indoor localization • Combining all signal indicators into a single signature achieves higher accuracy than any individual signal indicator

19. Result – FM-based Indoor localization • distance matrix (c) appears to be significantly less noisy ?

20. Result – FM-based Indoor localization • Combining FM and Wi-Fi

21. Result – FM-based Indoor localization • FM localization errors are not correlated with the WiFierrors • Using more FM indicators removes many of the localization errors by FM RSSI

22. Result – FM-based Indoor localization

23. Result – FM-based Indoor localization • All the erroneously predicted rooms are on the same floor and nearby the true rooms

24. Result – FM-based Indoor localization • Sensitivity to number of FM stations • About 30 FM stations are required

25. Result – FM-based Indoor localization • Sensitivity to number of WiFi APs • About 50 WiFi APs are required

26. Result – FM-based Indoor localization • Combine WiFi & FM signals • 50 WiFi APs and 25 FM stations are required

27. Result – Temporal Variations • FM • Continuous Monitoring of FM Signals Over Ten Days

28. Result – Temporal Variations • Using ten days data as testing data • FM signals are stable

29. Result – Temporal Variations • WiFi • Collect four additional sets of fingerprints on the second floor on four different days

30. 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

31. Result – Different Buildings • Shopping Mall • 5 data set on three days (Weekends & Wed.)

32. 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 • WiFisignatures perform significantly better • more fingerprints in the database increases localization accuracy

33. 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

34. 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

35. Result – Fine-Grain Localization • Each location is identified as one of its two neighbors on the line in terms of FM • WiFiRSSI signatures exhibit larger errors

36. 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…

37. Result – Fine-Grain Localization • Temporal Variation • FM still outperforms WiFisignificantly • Device Variation • Data set 3 is collected by a different FM receiver • Localization error doesn’t increase significantly

38. Conclusion • Propose to exploit additional information at the physical layer to create more reliable fingerprinting of indoor spaces • Demonstrate that FM and WiFisignals 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~