Accurate Device-Free Tracking Using Inexpensive RFIDs
Abstract
:1. Introduction
- It presents a device-free tracking scheme that exploits Doppler shifts as a feature of the target’s motion. As a result, the design delivers fine-grained resolution in different environments with different multipath conditions.
- It is the first to demonstrate the capability of the DWT technique to detect the beginning and the ending of the influenced time for each tag caused by the target motion, and we successfully use it to track the target. While our scheme has been verified in the context of RFIDs, the basic idea can be extended to other tracking systems.
- It presents a less complex trajectory estimation model, which has a high tracking accuracy of 32 cm.
- It demonstrates a prototype system and evaluates it in real-world deployments.
2. Background and Analysis
2.1. Backscatter Communication
- There is no carrier frequency offset between the reader and the tag, because the tag does not generate its own RF signal, but rather reflects the reader’s signal [19]. Hence, the reader can use coherent detection to recover the complex channel values of the tag. These channels are available to the tracking systems and are used by many proposals [11,12,21,22], as well as ours.
- The passive RFID system’s range is limited by the reader’s sensitivity to the reflected signal and by the amount of energy that reaches the tag from the reader. The actual return signal is tiny as a result of two attenuations, including the first attenuation occurring as EM waves [19] radiating from the reader to the tag and the second attenuation occurring when reflected waves come back from the tag to the reader. Therefore, the returning energy is , where r is the distance between the tag and the reader. Providing accurate tracking information while keeping the range large will be the valuable and necessary features of RFID systems.
2.2. Signal Characteristics
2.2.1. RF Phase
2.2.2. Doppler Frequency Shift
3. System Overview
4. Data Extraction
4.1. Revision on the Random Hopping
4.2. Noise Reduction Using DWT
4.2.1. Decomposition
4.2.2. Threshold Denoising
4.2.3. Reconstruction
4.3. Calculate Doppler Frequency Shift Using Phase Profile
5. Trajectory Mapping
5.1. Motion Detection and Segmentation
5.2. Trajectory Estimate
5.3. Trajectory Smoothing
5.3.1. Data Normalization
5.3.2. Decision Tree Regression Based on AdaBoost
5.3.3. Trajectory Estimating Model Based on HMM
5.3.4. Trajectory Detailing and Smoothing
6. Implementation and Evaluation
- (1)
- The robustness of RDTrack in PIU recognition.
- (2)
- The effectiveness of RDTrack with different parameters.
6.1. Implementation
6.1.1. Hardware
6.1.2. Default Deployment Set
6.1.3. Reference Profiles Setup
6.2. Effectiveness Validation
- A small short corridor between staircases, which has a length and width of 2 m × 3.5 m, as shown in Figure 11a.
- The reading room of the library on our campus, which has a length and width of 1 0 m × 12 m, as shown in Figure 11b.
- The first floor of our laboratory building, which has a length and width of 5 m × 7 m, as is shown in Figure 11c.
- A typical apartment consisting of a living room connected to the kitchen, bathroom and bedroom, which covers 8 m × 9 m, as shown in Figure 11d.
6.2.1. Segmentation Accuracy
6.2.2. Motion Recognition Accuracy
- (a)
- True Positive Rate (TPR): The accuracy of RDTrack. The proportion of the number of PIU that have been correctly identified to the total number of PIU.
- (b)
- False Positive Rate (FPR): The proportion of the number of other activities that were misjudged as Activity B to the total number of Activity B.
6.2.3. System Performance
6.3. Evaluation under Different Parameters
6.3.1. Impact of Human Diversity
6.3.2. Impact of the Motion Speed
6.3.3. Impact of the Numbers of Tags
6.3.4. Impact of Moving Direction
6.3.5. Impact of the Experimental Environment
6.4. Supplemental Experiment
6.4.1. Impact of the Number of Targets
6.4.2. Impact of Multiple Readers
7. Related Work
8. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
RFID | Radio Frequency Identification |
RF | Radio Frequency |
RSSI | Received Signal Strength Indicator |
CSI | Channel State Information |
FMCW | Frequency-Modulated Continuous Wave |
USRP | Universal Software Radio Peripheral |
DWT | Discrete Wavelet Transform |
PIU | Path Information Unit |
UHF | Ultra-High Frequency |
RSS | Received Signal Strength |
V | Velocity |
HMM | Hidden Markov Model |
IoT | Internet of Things |
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Abbreviations | Motion | Training Time |
---|---|---|
F | Going Straight | 10.23 s |
B | Going Backward | 10.43 s |
T | Turning | 2.47 s |
System | Time Delay | Tag Discovery Accuracy |
---|---|---|
RDTrack | ≤0.3 s | 100% |
Rollcaller | ≤0.5 s | 95% |
Twins | ≤1.1 s | 98.8% |
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Li, L.; Guo, C.; Liu, Y.; Zhang, L.; Qi, X.; Ren, Y.; Liu, B.; Chen, F. Accurate Device-Free Tracking Using Inexpensive RFIDs. Sensors 2018, 18, 2816. https://doi.org/10.3390/s18092816
Li L, Guo C, Liu Y, Zhang L, Qi X, Ren Y, Liu B, Chen F. Accurate Device-Free Tracking Using Inexpensive RFIDs. Sensors. 2018; 18(9):2816. https://doi.org/10.3390/s18092816
Chicago/Turabian StyleLi, Liyao, Chongzheng Guo, Yang Liu, Lichao Zhang, Xiaofei Qi, Yuhui Ren, Baoying Liu, and Feng Chen. 2018. "Accurate Device-Free Tracking Using Inexpensive RFIDs" Sensors 18, no. 9: 2816. https://doi.org/10.3390/s18092816
APA StyleLi, L., Guo, C., Liu, Y., Zhang, L., Qi, X., Ren, Y., Liu, B., & Chen, F. (2018). Accurate Device-Free Tracking Using Inexpensive RFIDs. Sensors, 18(9), 2816. https://doi.org/10.3390/s18092816