SpotFi: Decimeter Level Localization Using WiFi
17 Aug 2015-Vol. 45, Iss: 4, pp 269-282
TL;DR: SpotFi only uses information that is already exposed by WiFi chips and does not require any hardware or firmware changes, yet achieves the same accuracy as state-of-the-art localization systems.
Abstract: This paper presents the design and implementation of SpotFi, an accurate indoor localization system that can be deployed on commodity WiFi infrastructure. SpotFi only uses information that is already exposed by WiFi chips and does not require any hardware or firmware changes, yet achieves the same accuracy as state-of-the-art localization systems. SpotFi makes two key technical contributions. First, SpotFi incorporates super-resolution algorithms that can accurately compute the angle of arrival (AoA) of multipath components even when the access point (AP) has only three antennas. Second, it incorporates novel filtering and estimation techniques to identify AoA of direct path between the localization target and AP by assigning values for each path depending on how likely the particular path is the direct path. Our experiments in a multipath rich indoor environment show that SpotFi achieves a median accuracy of 40 cm and is robust to indoor hindrances such as obstacles and multipath.
Citations
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TL;DR: This paper aims to provide a detailed survey of different indoor localization techniques, such as angle of arrival (AoA), time of flight (ToF), return time ofFlight (RTOF), and received signal strength (RSS) based on technologies that have been proposed in the literature.
Abstract: Indoor localization has recently witnessed an increase in interest, due to the potential wide range of services it can provide by leveraging Internet of Things (IoT), and ubiquitous connectivity. Different techniques, wireless technologies and mechanisms have been proposed in the literature to provide indoor localization services in order to improve the services provided to the users. However, there is a lack of an up-to-date survey paper that incorporates some of the recently proposed accurate and reliable localization systems. In this paper, we aim to provide a detailed survey of different indoor localization techniques, such as angle of arrival (AoA), time of flight (ToF), return time of flight (RTOF), and received signal strength (RSS); based on technologies, such as WiFi, radio frequency identification device (RFID), ultra wideband (UWB), Bluetooth, and systems that have been proposed in the literature. This paper primarily discusses localization and positioning of human users and their devices. We highlight the strengths of the existing systems proposed in the literature. In contrast with the existing surveys, we also evaluate different systems from the perspective of energy efficiency, availability, cost, reception range, latency, scalability, and tracking accuracy. Rather than comparing the technologies or techniques, we compare the localization systems and summarize their working principle. We also discuss remaining challenges to accurate indoor localization.
1,447 citations
Cites background or methods from "SpotFi: Decimeter Level Localizatio..."
...[23] propose SpotFi that uses CSI and RSSI to obtain an accurate estimate of AoA and ToF, which are used to obtain user location....
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...most widely studied localization technologies in the literature [20]–[23], [37], [45], [46], [47]–[54], [55], [56]....
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...Recent WiFi based localization systems [20], [21], [23], details of which are given in Section VI, have achieved median localization accuracy as high as 23cm [22]....
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Proceedings Article•
16 Mar 2016TL;DR: Chronos, a system that enables a single WiFi access point to localize clients to within tens of centimeters, demonstrates that Chronos's accuracy is comparable to state-of-the-art localization systems, which use four or five access points.
Abstract: We present Chronos, a system that enables a single WiFi access point to localize clients to within tens of centimeters. Such a system can bring indoor positioning to homes and small businesses which typically have a single access point.
The key enabler underlying Chronos is a novel algorithm that can compute sub-nanosecond time-of-flight using commodity WiFi cards. By multiplying the time-of-flight with the speed of light, a MIMO access point computes the distance between each of its antennas and the client, hence localizing it. Our implementation on commodity WiFi cards demonstrates that Chronos's accuracy is comparable to state-of-the-art localization systems, which use four or five access points.
669 citations
Cites background or result from "SpotFi: Decimeter Level Localizatio..."
...…Point Presented By: Bashima Islam Indoor Localization Smart Home Occupancy Geo Fencing Device to Device Location 10/3/17 2 Previous Work 10 cm Accuracy Commodity Chipset & Sensors Multiple Access Point Goal Single WiFi Access Point Commodity Chipset Only (No Sensors) Decimeter Level…...
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...Our results reveal the following: • Chronos computes the time-of-flight with a median error of 0.47 ns in line-of-sight and 0.69 ns in non-lineof-sight settings....
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Proceedings Article•
01 Jan 2007TL;DR: In this paper, the Gaussian Process Latent Variable Model (GPLVM) is used to reconstruct a topological connectivity graph from a signal strength sequence, which can be used to perform efficient WiFi SLAM.
Abstract: WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GPLVM) to determine the latent-space locations of unlabeled signal strength data. We show how GPLVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization.
488 citations
18 Jun 2018
TL;DR: A deep neural network approach that parses wireless signals in the WiFi frequencies to estimate 2D poses through walls despite never trained on such scenarios, and shows that it is almost as accurate as the vision-based system used to train it.
Abstract: This paper demonstrates accurate human pose estimation through walls and occlusions. We leverage the fact that wireless signals in the WiFi frequencies traverse walls and reflect off the human body. We introduce a deep neural network approach that parses such radio signals to estimate 2D poses. Since humans cannot annotate radio signals, we use state-of-the-art vision model to provide cross-modal supervision. Specifically, during training the system uses synchronized wireless and visual inputs, extracts pose information from the visual stream, and uses it to guide the training process. Once trained, the network uses only the wireless signal for pose estimation. We show that, when tested on visible scenes, the radio-based system is almost as accurate as the vision-based system used to train it. Yet, unlike vision-based pose estimation, the radio-based system can estimate 2D poses through walls despite never trained on such scenarios. Demo videos are available at our website.
481 citations
Cites background from "SpotFi: Decimeter Level Localizatio..."
...For example, one can track a person using the WiFi signal from their cellphone [44, 24, 40]....
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07 Sep 2015
TL;DR: Splicer, a software-based system that derives high-resolution power delay profiles by splicing the CSI measurements from multiple WiFi frequency bands is presented and a set of key techniques to separate the mixed hardware errors from the collected CSI measurements are proposed.
Abstract: Power delay profiles characterize multipath channel features, which are widely used in motion- or localization-based applications. Recent studies show that the power delay profile may be derived from the CSI traces collected from commodity WiFi devices, but the performance is limited by two dominating factors. The resolution of the derived power delay profile is determined by the channel bandwidth, which is however limited on commodity WiFi. The collected CSI reflects the signal distortions due to both the channel attenuation and the hardware imperfection. A direct derivation of power delay profiles using raw CSI measures, as has been done in the literature, results in significant inaccuracy. In this paper, we present Splicer, a software-based system that derives high-resolution power delay profiles by splicing the CSI measurements from multiple WiFi frequency bands. We propose a set of key techniques to separate the mixed hardware errors from the collected CSI measurements. Splicer adapts its computations within stringent channel coherence time and thus can perform well in presence of mobility. Our experiments with commodity WiFi NICs show that Splicer substantially improves the accuracy in profiling multipath characteristics, reducing the errors of multipath distance estimation to be less than $2m$. Splicer can immediately benefit upper-layer applications. Our case study with recent single-AP localization achieves a median localization error of $0.95m$.
454 citations
Cites background from "SpotFi: Decimeter Level Localizatio..."
...site survey, and no sophisticated AP hardware [1, 6, 9, 47, 48]....
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"SpotFi: Decimeter Level Localizatio..." refers background or methods in this paper
...RSSI based approaches: This class of systems measures the RSSI from the target at multiple APs, combines them via triangulation along with a propagation model to locate the target [3, 10, 4, 5, 11, 12, 13]....
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...RSSI based systems are deployable and universal, but are not accurate; their median accuracy ranges from 2–4 m [3, 4, 5]....
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...However the best known systems in this space tend to achieve a median accuracy of around 2–4 m [3, 4], and the 80 percentile error is often as high as 5 m due to insufficient modeling of RSSI which in practice depends not only on the location of the target but also on the changing environment....
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...The server assumes a standard widely used path loss model to relate RSSI to distance as described in prior work [3, 71]....
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