Indoor Locationing and Tracking
4. Bandwidth Concatenation
4.6 Discussion
Effective bandwidth
100 200 300 400 500 600 700 800 900 0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
100% true-positive rate, 0% false-positive rate
At least 95% true-positive rate, at most 5% false-positive rate
Figure 4.12 Thresholdunder differentWeto achieve (i)PTP=100% andPFP=0% and (ii) PTP≥95% andPFP≤5%.
Figure 4.12also implies that we can achieve a perfect 5 cm localization ifis chosen appropriately.
Based on the experiment results, we conclude that a largeWe is imperative for the robustness, stability, and performance of the presented IPS. By formulating the location fingerprint that concatenates multiple channels, the presented IPS achieves a perfect centimeter localization accuracy in an NLOS environment with one pair of single- antenna Wi-Fi devices.
4.6 Discussion 85
x-axis (mm)
0 10 20 30 40 50
5 10 15 20 25 30 35 40 45 50
y-axis (mm)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Figure 4.13 TRRS near the intended location with a measurement resolution of 0.5cm.
space with a centimeter-level granularity. In this case, the complexity of CFR measure- ment can be too high to be practical, especially for a large indoor space.
The burden of measurement can be significantly reduced because we only need to obtain the fingerprints of a limited number of areas that are more critical than the others.
For instance, in an office, the main entrance and exit of the office as well as the entrance to some office rooms are of higher importance than the other areas, while in a museum, areas closer to the paintings could be more important. Fine-grained CFR measurements can be confined to these areas-of-interest. On the other hand, the efficiency of measure- ment can be boosted by automation techniques such as robotics.
4.6.3 Scalability
We notice that most of the calculations in the offline phase and online phase can be interpreted as linear operations. Thus, the computational complexity of the presented IPS scales linearly with the number of location fingerprints stored in the database. As the offline phase can in general tolerate a large delay, the increase in the computational complexity of the offline phase is less significant. On the other hand, the increase of the complexity imposes a challenge to the online phase because the online phase is much more time-sensitive than the offline phase. This issue becomes more severe when a huge number of fingerprints are stored in the database.
To deal with this problem, other information such as the sensory information or the RSSI values can be retrieved to supplement the presented IPS with a coarse posi- tion estimation. Then, the presented IPS can choose a subset of the fingerprints from
the database that are collected nearby the estimated location to formulate a refined estimation.
4.6.4 Fingerprint Degradation with Time
In an indoor space, movement of small and large objects such as chairs and desks should be expected. These movements slightly change the environment and thus introduce deviations into the CFRs collected in the offline phase. According to our most recent work in [36], we find that a large effective bandwidth can reduce the sensitivity of the fingerprints to the environmental dynamics, which can be achieved by concatenating a large enough number of channels using frequency hopping.
4.6.5 CFR Acquisition on Commercial Wi-Fi Devices
USRPs are used as the prototype to acquire CFRs due to the fact that CFRs are unavail- able on most commercial Wi-Fi devices. More recently, the CFRs can be obtained on the off-the-shelf 802.11n device, Intel Wi-Fi Wireless Link 5300, after modification of the firmware and the wireless driver [37]. Currently, we are investigating the IPS per- formance using the off-the-shelf Wi-Fi devices as well as implementing the frequency hopping mechanism.
4.7 Summary
In this chapter, we presented a Wi-Fi-based IPS that exploits the frequency diversity to achieve centimeter accuracy for indoor localization. The presented IPS fully har- nesses the frequency diversity by CFR measurements on multiple channels via fre- quency hopping. Impacts of synchronization errors and interference are mitigated by CFR sanitization, sifting, and averaging. The averaged CFRs of different channels are then concatenated together into location fingerprints to augment the effective bandwidth.
The location fingerprints are stored into a database in the offline phase and are used to calculate the TRRS in the online phase. Finally, the presented IPS determines the location based on the TRRS. Extensive experiment results of measurements on a 1 GHz frequency band demonstrate the centimeter localization accuracy of the presented IPS in a typical office environment with a large effective bandwidth. For related references, interested readers can refer to [38].
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With the development of the Internet of Things technology, indoor tracking has become a popular application nowadays, but most existing solutions can only work in line-of- sight scenarios or require regular recalibration. In this chapter, we present WiBall, an accurate and calibration-free indoor tracking system that can work well in non-line-of- sight based on radio signals. WiBall leverages a stationary and location-independent property of the time-reversal focusing effect of radio signals for highly accurate moving distance estimation. Together with the direction estimation based on inertial measure- ment unit and location correction using the constraints from the floorplan, WiBall is shown to be able to track a moving object with decimeter-level accuracy in different environments. Because WiBall can accommodate a large number of users with only a single pair of devices, it is low cost and easily scalable and can be a promising candidate for future indoor tracking applications.