Pilot: Device-free Indoor Localization Using Channel State Information

Transcription

1 ICDCS 2013 Pilot: Device-free Indoor Localization Using Channel State Information Jiang Xiao, Kaishun Wu, Youwen Yi, Lu Wang, Lionel M. Ni Department of Computer Science and Engineering Hong Kong University of Science and Technology July. 10 th, 2013

2 Outline Introduction & Motivation Related Work Hypotheses Methodology Performance Evaluation Conclusion 2

3 Indoor LBSs Safety Precaution Area Isolation Pilferage Prevention Residence Hospital Warehouse Device-free techniques are in need! 3

4 Indoor Device-free Localization (DfL) High cost for large environment Ultrasonic Existing Approaches Environment constraints (e.g., dark, smoke) Sensor High cost for dense deployment Camera Infrared Line-of-sight Can t work well in large scale, complicate, typical indoor environment! 4

5 Motivation WLAN Advantages ü Low Cost ü Good Scalability WLAN-based Indoor Device-free Localization! 5

6 Challenges 1. When to trigger localization? à Motion detection is the prerequisite. 6

7 Challenges 2. How to realize localization? 7

8 Outline Introduction & Motivation Related Work Hypotheses and Measurements Methodology Performance Evaluation Conclusion 8

9 Existing WLAN-based DfL Most of researches rely on RSS measurement MLE Alg.[PerCom] Nuzzer[TMC] FIMD [ICPADS] Challenges [MobiCom] Survey [IEEE Invited Paper] RASID[PerCom] Pilot [ICDCS] 2013 Time M. Seifeldin M. Youssef Analyzed the challenges for DfL systems Proposed to use radio signal strength (RSS) Developed Nuzzer & RASDID DfL systems Surveyed the recent researches on DfL J. Wilson N. Patwari 9

10 Existing RSS-based DfL TX RX RSS Static Dynamic AP l1 l2 Static l3 l4 Dynamic DP Youssef et al. Nuzzer System [PerCom 11] 10

11 In static environment RSS Limitation 15dB High Variability Receiver Node ID single value RSS 2.4GHz RF band Coarse measuremen t 11

12 We want to find a reliable location feature for WLAN-based DfL. 12

13 Opportunity Data in OFDM Transmitter + Channel OFDM Receiver Data out In n OFDM system, the received signal over multiple subcarriers is CSI Channel gain Previously, CSI à amplitude phase [SIGCOMM 10, MobiCom 11] 13

14 Observations CSI Property 1: Frequency Diversity Receiver single value RSS multiple values CSIs 2.4GHz S/P FFT RF band Baseband CSI Property 2: Temporal Stability RSS (dbm) RSS: Variant CSI amplitude CSI: relatively stable Time Duration (s) Time Duration (s) 14

15 CSI RSS Temporal Stability Frequency Diversity We want to harness fine-grained CSI for device-free indoor localization. 15

16 Our Work Hypotheses Feasibility of CSI Methodology Design Pilot System Evaluation Accuracy (Motion Detection & Localization) 16

17 Outline Introduction & Motivation Related Work Hypotheses Methodology Performance Evaluation Conclusion 17

18 Hypothesis1 Hypotheses 1/2 Ø CSI can reveal the abnormal status caused by appearance of human. 18

19 Hypotheses 1/2 Experiment 1: Anomaly Detection by CSI Feature CSI feature can reveal normal/motion behavior 19

20 Hypothesis 2 Hypotheses 2/2 Ø CSI can be leveraged to distinguish human in different locations. 20

21 Hypotheses 2/2 Experiment 2: Location Distinction by CSI Feature 21

22 Outline Introduction & Motivation Related Work Hypotheses Methodology Performance Evaluation Conclusion 22

23 System Overview 3 Components 23

24 Pilot Architecture Process CSI Passive Radio Map Construction Collect CSI Channel Estimation OFDM Demodulator Anomaly Detection Fingerprint Mapping Data Fusion Passive Fingerprints DB (Normal/Abnormal) Position Estimation Pilot DP Pilot Server 24

25 Outline Introduction & Motivation Related Work Hypotheses Methodology Passive Radio Map Construction Anomaly Detection Position Estimation Performance Evaluation Conclusion 25

26 1. Passive Radio Map Construction DP1 DP2 AP1 AP2 AP1 AP2 26

27 1. Passive Radio Map Construction To process the measurement data To generate the fingerprints database In normal state: -- aggregated correlation sum CSI of n packets In dynamic state: 27

28 1. Passive Radio Map Construction Sensing Zone & Dead Spot 28

29 Outline Introduction & Motivation Related Work Hypotheses and Measurements Methodology Passive Radio Map Construction Anomaly Detection Position Estimation Performance Evaluation Conclusion 29

30 Anomaly Detection Localization Trigger N Alarm? Y Localization 30

31 2. Anomaly Detection Main idea: to check the probability of each CSI feature to be in normal profile. To estimate the distribution of Kernel density estimator Correlation of (M+W-1) Packets Bandwidth Anomaly Detection Threshold Epanechnikov Quadratic Kernel Function CDF CDF of CSI Feature à β CSI Feature C β 31

32 Anomaly Detection N Alarm? Y Localization Position Estimation 32

33 Outline Introduction & Motivation Related Work Hypotheses and Measurements Methodology Passive Radio Map Construction Anomaly Detection Position Estimation Performance Evaluation Conclusion 33

34 3. Position Estimation To compare CSIs against the abnormal To select the best match (MAP Alg.) Link l Unknown Location Fuse Multiple RF Links 34

35 Outline Introduction & Motivation Related Work Hypotheses Methodology Performance Evaluation Conclusion 35

36 Experimental Setup Commercial Hardware Intel WiFi Link n Router 36

37 Testbeds 1/2 Lab in HKUST 77m 2 37

38 Testbeds 2/2 Lobby in HKUST 776m 2 38

39 Evaluation Metrics Receiver Operating Characteristic (ROC) curve: interprets the detection performance in the presence of false alarm. Condition: Motion Condition: Static Test outcome: Motion True Positive (TP) Rate False Positive (FP) Rate False Alarm Test outcome: Static False Negative (FN) Rate Ignore Miss detection 39

40 Detection Accuracy in Lab DR>90%, 30% Refer to [PerCom 12] Youssef et. al. RASID system 40

41 Detection Accuracy in Lobby DR > 90% 10% Refer to [PerCom 12] Youssef et. al. RASID system 41

42 Localization Accuracy in Lab Refer to [PerCom 11] Youssef et. al. Nuzzer system 42

43 Localization Accuracy in Lobby Refer to [PerCom 11] Youssef et. al. Nuzzer system 43

44 Localization Accuracy in Lab Refer to [PerCom 11] Youssef et. al. Nuzzer system 44

45 Localization Accuracy in Lobby Refer to [PerCom 11] Youssef et. al. Nuzzer system 45

46 Outline Introduction & Motivation Related Work Hypotheses Methodology Performance Evaluation Conclusion 46

47 Conclusion We explore the feasibility of using CSI for indoor DfL. We design the Pilot system, and implement it on commercial products. CSI is a better tool for DfL compared to RSS as shown in experiments. 47

48 Future Work To facilitate multiple objects localization. To quickly establish database. To optimize AP-DP deployment. 48

49 Thanks. Questions? HKUST

50 Backup Slides

51 51

52 1. Passive Radio Map Construction Sensing Zone & Dead Spot 52

53 Hypothesis 2 Hypotheses 2/2 Ø CSI can be leveraged to distinguish human in different locations. Direct light-of-sight (LOS) blocking: the entity is located exactly be tween the AP and DP such that blocks the direct LOS transmitting link; Indirect non-light-of-sight (NLOS) reflection: the position of entity lies beside the LOS link and influences the multipath propagation of RF signals. 53