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FM-BASED INDOOR LOCALIZATION 20130107 TsungYun 1.

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Presentation on theme: "FM-BASED INDOOR LOCALIZATION 20130107 TsungYun 1."— Presentation transcript:

1 FM-BASED INDOOR LOCALIZATION TsungYun 1

2 Outline Introduction Architecture Experiment Result FM-based Indoor localization Temporal Variations Different Buildings Fine-Grain Localization Conclusion 2

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 3

4 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.) 4

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] 5

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 WiFi signals FM and WiFi signals are complementary their localization errors are independent Combine FM and WiFi 6

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 7

8 Architecture 8

9 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 9

10 Architecture 10

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 WiFi APs Residential apartment 5 different rooms 117 WiFi APs 11

12 Experiment 12

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 & M WiFi signals each location (RSSI, SNR, MULTIPATH, FREQOFF) (WiFi signal) each fingerprint 3 data set A 1, A 2, A 3 13

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%) 14

15 Result – FM-based Indoor localization The localization errors in terms of physical distance are lower in the case of WiFi 15

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

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

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

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

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

21 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 21

22 Result – FM-based Indoor localization 22

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

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

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

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

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

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

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

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 30

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

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 WiFi signatures perform significantly better more fingerprints in the database increases localization accuracy 32

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 33

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 34

35 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 35

36 Result – Fine-Grain Localization FM RSSI signatures have the necessary spatial resolution For more accurate fingerprinting, even better than WiFi signature 36

37 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 37

38 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 38


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