ArrayTrack: a fine-grained indoor location system
NSDI, pp.71-84, (2013)
With myriad augmented reality, social networking, and retail shopping applications all on the horizon for the mobile handheld, a fast and accurate location technology will become key to a rich user experience. When roaming outdoors, users can usually count on a clear GPS signal for accurate location, but indoors, GPS often fades, and so u...更多
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- The proliferation of mobile computing devices continues, with handheld smartphones, tablets, and laptops a part of the everyday lives.
- Outdoors, these devices largely enjoy a robust and relatively accurate location service from Global Positioning System (GPS) satellite signals, but indoors where GPS signals don’t reach, providing an accurate location service is quite challenging.
- Location-aware smartphone applications on the horizon such as augmented reality-based building navigation, social networking, and retail shopping demand both a high accuracy and a low response time.
- A solution that offers a centimeter-accurate location service indoors would enable these and other exciting applications in mobile and pervasive computing
- The proliferation of mobile computing devices continues, with handheld smartphones, tablets, and laptops a part of our everyday lives
- First we present the accuracy level ArrayTrack achieves in the challenging indoor office environment and explore the effects of number of antennas and number of access points (APs) on the performance of ArrayTrack
- We demonstrate that ArrayTrack is robust against different transmitter/receiver heights and different antenna orientations between clients and APs
- We examine the latency introduced by ArrayTrack, which is a critical factor for a real-time system
- We have presented ArrayTrack, an indoor location system that uses angle-of-arrival techniques to locate wireless clients indoors to a level of accuracy previously only attainable with expensive dedicated hardware infrastructure
- ArrayTrack combines best of breed algorithms for AoA based direction estimation and spatial smoothing with novel algorithms for suppressing the non-line of sight reflections that occur frequently indoors and synthesizing information from many antennas at the AP
- The layout shows the basic structure of the office but does not include the numerous cubicle walls present.
- The authors place the 41 clients roughly uniformly over the floorplan, covering areas both near to, and far away from the AP.
- The authors put some clients near metal, wood, glass and plastic walls to make the experiments more comprehensive.
- The authors place some clients behind concrete pillars in the office so that the direct path between the AP and client is blocked, making the situation more challenging.
- To measure ground truth in the location experiments presented the authors used scaled architectural drawings of the building combined with measurements taken from a Fluke 416D laser distance measurement device, which has an accuracy of 1.5 mm over 60 m
- First the authors present the accuracy level ArrayTrack achieves in the challenging indoor office environment and explore the effects of number of antennas and number of APs on the performance of ArrayTrack.
- The authors demonstrate that ArrayTrack is robust against different transmitter/receiver heights and different antenna orientations between clients and APs. the authors examine the latency introduced by ArrayTrack, which is a critical factor for a real-time system
- The authors have presented ArrayTrack, an indoor location system that uses angle-of-arrival techniques to locate wireless clients indoors to a level of accuracy previously only attainable with expensive dedicated hardware infrastructure.
- ArrayTrack combines best of breed algorithms for AoA based direction estimation and spatial smoothing with novel algorithms for suppressing the non-line of sight reflections that occur frequently indoors and synthesizing information from many antennas at the AP.
- Suppose the AP is distance h above the client; the authors compute the resulting percentage error.
- AoA relies on the distance difference d1 − d2 between the client and the two AP antennas in a pair.
- This difference becomes: d1 − d2 = cods1φ − cods2φ (13).
- The percentage error is (d1−dd2)1−−(dd21−d2) =−1 − 1.
- Table1: Peak stability microbenchmark measuring the frequency of the direct and reflection-path peaks changing due to slight movement
- The present paper is based on the ideas sketched in a previous workshop paper , but contributes novel diversity synthesis (§2.2) and multipath suppression (§2.4) design techniques and algorithms, as well as providing the first full performance evaluation of our system.
ArrayTrack owes its research vision to early indoor location service systems that propose dedicated infrastructure Active Badge  equips mobiles with infrared transmitters and buildings with many infrared receivers. The Bat System  uses a matrix of RF-ultrasound receivers, each hard-coded with location, deployed on the ceiling indoors. Cricket  equips buildings with combined RF/ultrasound beacons while mobiles carry RF/ultrasound receivers.
Some recent work including CSITE  and PinLoc  has explored using the OFDM subcarrier channel measurements as unique signatures for security and localization. This requires a large amount of wardriving, and the accuracy is limited to around one meter, while ArrayTrack achieves finer accuracy and eliminates any calibration beforehand.
- While the few meters of accuracy GPS provides outdoors are more than sufficient for street-level navigation, small differences in location have more importance to people and applications indoors: a few meters of error in estimated location can place someone in a different office within a building, This material is based on work supported by the European Research Council under Grant No 279976
- Jie Xiong is supported by the Google European Doctoral Fellowship in Wireless Networking
In principle, one time domain packet sample would provide enough information for the AoA spectrum computation described in Section 2.3. However, to average out the effects of noise, we use 10 samples (we justify this choice in Section 4.3.3). Since a commodity WiFi AP samples at 40 Msamples/second, this implies that we need to process just 250 nanoseconds of a packet’s samples, under 1.5% of an WiFi preamble’s 16 μs duration
We can see that when the number of samples increased to 5, the AoA spectrum is already quite stable which demonstrate ArrayTrack has the potential to responds extremely fast. We employ 10 samples in our experiments and for a 100 ms refreshing interval, the overhead introduced by ArrayTrack traffic is as little as: (10 samples)(321b0i0ts/mssample)(8 radios) = 0.0256 Mbps. 4.3.4 Low signal to noise ratio (SNR)
Figure 21 summarizes the latency our system incurs, starting from the beginning of a frame’s preamble as it is received by the ArrayTrack APs. As discussed previously (§4.3.3), ArrayTrack only requires 10 samples from the preamble in order to function. We therefore have the opportunity to begin transferring and processing the AoA information while the remainder of the preamble and the body of the packet is still on the air, as shown in the figure
This yields. (10 samples)(32 bits/sample)(8 radios) 1 Mbit/s. 2.56 ms
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