Fine Time Measurement Research Articles

A Two-Step Fusion Method of Wi-Fi FTM for Indoor Positioning.

The Wi-Fi fine time measurement (FTM) protocol specified in the IEEE 802.11-2016 standard provides a new two-way ranging approach to enhance positioning capability. Similar to other wireless signals, the accuracy of the real-time range measurement of FTM is influenced by various errors. In this work, the characteristics of the ranging errors is analyzed and an abstract ranging model is introduced. From the perspective of making full use of the range measurements from FTM, this paper designs two positioning steps and proposes a fusion method to refine the performance of indoor positioning. The first step is named single-point positioning, locating the position with the real-time range measurements based on the geometric principle. The second step is named the improved matching positioning, which constructs a distance database by utilizing the existing scene information and uses the modified matching algorithm to obtain the position. In view of the different positioning accuracies and error distributions from the results of the aforementioned two steps, a fusion method using the indirect adjustment principle is proposed to adjust the positioning results, and the advantages of the matching scene information and the range measurements are served simultaneously. Finally, a number of tests are conducted to assess the performance of the proposed method. The experimental results demonstrate that the precision and stability of indoor positioning are improved by the proposed fusion method.

Geomagnetic Positioning-Aided Wi-Fi FTM Localization Algorithm for NLOS Environments

WiFi-based indoor localization using the fine time measurement (FTM) protocol has become a popular technique. However, in harsh Non-Line-of-Sight (NLoS) environments, WiFi FTM positioning (WFP) suffers from poor performance. In this letter, a novel WiFi FTM localization method with the assistance of geomagnetic positioning (GP) is proposed. To ensure the accuracy of GP, an enhanced mind evolutionary algorithm (EMEA) is designed. A fine-grained WiFi position estimation method using the overlapping searching area (OSA) and the coincident points selection strategy is proposed. Experimental results show that the EMEA-based GP improves the localization performance of WFP in NLOS environments, the mean localization error (ME) and root-mean-square error (RMSE) of the GP-aided WFP are 1.82 m and 2.08 m, respectively. Compared with the classic WFP using the weighted least square (WLS) method, the ME and RMSE are reduced by 51.7% and 52.4%, respectively.

Indoor Localization Using Uncooperative Wi-Fi Access Points.

Indoor localization using fine time measurement (FTM) round-trip time (RTT) with respect to cooperating Wi-Fi access points (APs) has been shown to work well and provide 1–2 m accuracy in both 2D and 3D applications. This approach depends on APs implementing the IEEE 802.11-2016 (also known as IEEE 802.11mc) Wi-Fi standard (“two-sided” RTT). Unfortunately, the penetration of this Wi-Fi protocol has been slower than anticipated, perhaps because APs tend not to be upgraded as often as other kinds of electronics, in particular in large institutions—where they would be most useful. Recently, Google released Android 12, which also supports an alternative “one-sided” RTT method that will work with legacy APs as well. This method cannot subtract out the “turn-around” time of the signal, and so, produces distance estimates that have much larger offsets than those seen with two-sided RTT—and the results are somewhat less accurate. At the same time, this method makes possible distance measurements for many APs that previously could not be used. This increased accessibility can compensate for the decreased accuracy of individual measurements. We demonstrate here indoor localization using one-sided RTT with respect to legacy APs that do not support IEEE 802.11-2016. The accuracy achieved is 3–4 m in cluttered environments with few line-of-sight readings (and using only 20 MHz bandwidths). This is not as good as for two-sided RTT, where 1–2 m accuracy has been achieved (using 80 MHz bandwidths), but adequate for many applications A wider Wi-Fi channel bandwidth would increase the accuracy further. As before, Bayesian grid update is the preferred method for determining position and positional accuracy, but the observation model now is different from that for two-sided RTT. As with two-sided RTT, the probability of an RTT measurement below the true distance is very low, but, in the other direction, the range of measurements for a given distance can be much wider (up to well over twice the actual distance). We describe methods for formulating useful observation models. As with two-sided RTT, the offset or bias in distance measurements has to be subtracted from the reported measurements. One difference is that here, the offsets are large (typically in the 2400–2700 m range) because of the “turn-around time” of roughly 16 μs (i.e., about two orders of magnitude larger than the time of flight one is attempting to measure). We describe methods for estimating these offsets and for minimizing the effort required to do so when setting up an installation with many APs.

Enhanced Wi-Fi RTT Ranging: A Sensor-Aided Learning Approach

The fine timing measurement (FTM) protocol is designed to determine precise ranging between Wi-Fi devices using round-trip time (RTT) measurements. However, the multipath propagation of radio waves generates inaccurate timing information, degrading the ranging performance. In this study, we use a neural network (NN) to adaptively learn the unique measurement patterns observed at different indoor environments and produce enhanced ranging outputs from raw FTM measurements. Moreover, the NN is trained based on an unsupervised learning framework, using the naturally accumulated sensor data acquired from users accessing location services. Therefore, the effort involved in collecting training data is significantly minimized. The experimental results verified that the collection of unlabeled data for a short duration is sufficient to learn the pattern in raw FTM measurements and produce improved ranging results. The proposed method reduced the errors in raw distance measurements and well-calibrated ranging results requiring the collection of ground truth data by 47–50% and 17–29%, respectively. Consequently, positioning errors were reduced by 17–30% compared to the well-calibrated ranging scenario.

Precise 3D Indoor Localization and Trajectory Optimization Based on Sparse Wi-Fi FTM Anchors and Built-In Sensors

Indoor location-based services have become more and more important due to their potential applications in a wide range of personalized services in recent years. The accuracy of smartphone based 3D indoor localization is subjected to the poor performance of low-cost sensors and limited coverage of location sources. In order to solve these problems, this paper proposes a precise 3D indoor localization and trajectory optimization framework that uses the combination of sparse Wi-Fi Fine Time Measurement (FTM) anchors and built-in sensors (3D-LOWS). The inertial navigation system (INS) mechanization, multi-level constraints and observed values are integrated by the adaptive unscented Kalman filter to eliminate effects of cumulative error, indoor magnetic interference, and diversity of handheld modes. The Wi-Fi based ranging and landmark detection information is used to provide an accurate absolute reference to the built-in sensors based method. In addition, this paper proposes and evaluates two different trajectory optimization algorithms and compares the improved localization performance. The comprehensive experiments indicate that the proposed 3D-LOWS is proved to achieve accurate and stable 3D indoor positioning and trajectory optimization performance under complex indoor environments using sparse wireless stations.

Privacy-Preserving Positioning in Wi-Fi Fine Timing Measurement

AbstractWith the standardization of Wi-Fi Fine Timing Measurement (Wi-Fi FTM; IEEE 802.11mc), the IEEE introduced indoor positioning for Wi-Fi networks. To date, Wi-Fi FTM is the most widely supported Wi-Fi distance measurement and positioning system. In this paper, we perform the first privacy analysis of Wi-Fi FTM and evaluate devices from a wide variety of vendors. We find the protocol inherently leaks location-sensitive information. Most notably, we present techniques that allow any client to be localized and tracked by a solely passive adversary. We identify flaws inWi-Fi FTM MAC address randomization and present techniques to fingerprint stations with firmware-specific granularity further leaking client identity. We address these shortcomings and present a privacy-preserving passive positioning system that leverages existing Wi-Fi FTM infrastructure and requires no hardware changes. Due to the absence of any client-side transmission, our design hides the very existence of a client and as a side-effect improves overall scalability without compromising on accuracy. Finally, we present privacy-enhancing recommendations for the current and next-generation protocols such as Wi-Fi Next Generation Positioning (Wi-Fi NGP; IEEE 802.11az).

Fusion-Based Smartphone Positioning Using Unsupervised Calibration of Crowdsourced Wi-Fi FTM

This paper presents a multi-source fusion smartphone localization solution using Wi-Fi Fine Time Measurement (FTM) and Pedestrian Dead Reckoning (PDR), calibrated via multi-source and unsupervised crowdsourcing. In crowdsourcing phase, user movement within the site utilizes PDR to infer their location, and this location is used to calibrate the FTM data. The multi-layer perceptron (MLP) of the ranging model is suitable for non-line-of-sight (NLOS) reception, and the ranging accuracy is improved by more than 24%. In the positioning phase, the 90 percentile error of the ranging model trained using only crowdsourced data is less than 1.37m, which is 32% smaller than the traditional weighted least squares (WLS) localization error.

LiFi Positioning for Industry 4.0

Precise position information is considered as the main enabler for the implementation of smart manufacturing systems in Industry 4.0. In this article, a time-of-flight based indoor positioning system for LiFi is presented based on the ITU - T recommendation G.9991. Our objective is to realize positioning by reusing already existing functions of the LiFi communication protocol which has been adopted by several vendors. Our positioning algorithm is based on a coarse timing measurement using the frame synchronization preamble, similar to the ranging, and a fine timing measurement using the channel estimation preamble. This approach works in various environments and it requires neither knowledge about the beam characteristics of transmitters and receivers nor the use of fingerprinting. The new algorithm is validated through both, simulations and experiments. Results in an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{1}\;{\rm{m}} \times \text{1}\;{\rm{m}} \times \text{2}\;{\rm{m}}$</tex-math></inline-formula> area indicate that G.9991-based positioning can reach an average distance error of a few centimeters in three dimension. Considering the common use of lighting in indoor environments and the availability of a mature optical wireless communication system using G.9991, the proposed LiFi positioning is a promising new feature that can be added to the existing protocols and enhance the capabilities of smart lighting systems further for the benefit of Industry 4.0.

WiFi FTM, UWB and Cellular-Based Radio Fusion for Indoor Positioning.

High-precision indoor localisation is becoming a necessity with novel location-based services that are emerging around 5G. The deployment of high-precision indoor location technologies is usually costly due to the high density of reference points. In this work, we propose the opportunistic fusion of several different technologies, such as ultra-wide band (UWB) and WiFi fine-time measurement (FTM), in order to improve the performance of location. We also propose the use of fusion with cellular networks, such as LTE, to complement these technologies where the number of reference points is under-determined, increasing the availability of the location service. Maximum likelihood estimation (MLE) is presented to weight the different reference points to eliminate outliers, and several searching methods are presented and evaluated for the localisation algorithm. An experimental setup is used to validate the presented system, using UWB and WiFi FTM due to their incorporation in the latest flagship smartphones. It is shown that the use of multi-technology fusion in trilateration algorithm remarkably optimises the precise coverage area. In addition, it reduces the positioning error by over-determining the positioning problem. This technique reduces the costs of any network deployment oriented to location services, since a reduced number of reference points from each technology is required.

Two-Way Ranging Algorithms for Clock Error Compensation

Two-Way Ranging (TWR) is a widely used method for distance estimation in asynchronous wireless networks, but the deviation between the clocks can lead to errors in distance estimation. The ultra-wideband (UWB) technology provides the best accuracy in distance estimation, but total emission time is drastically limited by harmonized standards. Localization accuracy, latency and network scalability are key performance metrics in wireless location characterization. This article advances a new sequencing of the localization process, called Double-Beacon Two-Way Ranging (DB-TWR) algorithm, and presents the related time-of-flight (ToF) estimation formula, which is able to compensate for clock error and to reduce the localization period. The Fine Time Measurement (FTM) protocol, specified by the Wireless LAN (WLAN) standard, uses the TWR method and as a result the clock deviation leads to systematic errors in ToF estimation. The paper proposes two additional timestamps captures and a new ToF computation formula which compensates for clock deviation. The article provides a theoretical analysis and extensive simulations of errors propagation in the ranging process, both as a mean and as an uncertainty, and proves that the designed algorithms cancel the clock offsets entirely.

Accurate indoor positioning using IEEE 802.11mc round trip time

WiFi time of flight (ToF) measurement has been supported recently by the wireless LAN protocols to improve WiFi localization. Specifically, the IEEE 802.11-2016 standard has a fine-time measurement (FTM) protocol that can be used to measure the WiFi signal round trip time (RTT). In this paper, we present the design and implementation of WiNar, a WiFi RTT-based indoor location determination system that combines the advantages of both fingerprint and ranging-based techniques to overcome the different challenges of indoor environments. Using commercial-off-the-shelf access points and mobile phones, WiNar leverages both the propagation time and received signal strength of the wireless signal with a fingerprinting model to address the multipath, non-line-of-sight, signal attenuation, and interference challenges of the indoor environments. Moreover, when leveraging the round trip time measurements, WiNar does not require clock synchronization between the transmitter and the receiver. We discuss the different components of the system and its implementation on the Android operating system. Our results show that WiNar has a sub-meter localization accuracy with an average localization error of less than 0.77 meters for two different testbeds. This accuracy outperforms the performance of the traditional signal strength (RSS) fingerprinting technique by at least 50% and ranging-based multi-lateration technique by at least 193%. Finally, our system is also robust to heterogeneous devices.

A Robust Seamless Localization Framework Based on Wi-Fi FTM / GNSS and Built-In Sensors

Global Navigation Satellite System (GNSS) has been widely applied for outdoor localization while it is still challenging to realize seamless positioning due to the limited coverage of accurate indoor location sources. This letter proposes a robust seamless localization framework using the combination of GNSS, Wi-Fi Fine Time Measurement and built-in sensors (SL-GWBS). Pedestrian dead reckoning and magnetic detection are used to decrease the cumulative error of inertial navigation system for accurate heading and speed estimation, and an adaptive multi-model based extended Kalman filter is adopted to realize concrete localization towards different integration methods of location sources. The experimental results demonstrate the meter-level accuracy of the proposed SL-GWBS.

Exploiting User Mobility for WiFi RTT Positioning: A Geometric Approach

Recently, round-trip time (RTT) measured by a fine-timing measurement protocol has received great attention in the area of WiFi positioning. It provides an acceptable ranging accuracy in favorable environments when a line-of-sight (LOS) path exists. Otherwise, a signal is detoured along with non-LOS (NLOS) paths, making the resultant ranging results different from the ground truth, called an RTT bias, which is the main reason for poor positioning performance. To address it, we aim at leveraging the user mobility trajectory detected by a smartphone’s inertial measurement units, called pedestrian dead reckoning (PDR). Specifically, PDR provides the geographic relation among adjacent locations, guiding the resultant positioning estimates’ sequence not to deviate from the user trajectory. To this end, we describe their relations as multiple geometric equations, enabling us to render a novel positioning algorithm with acceptable accuracy. Depending on the mobility pattern being linear or arbitrary, we develop different algorithms divided into two phases. First, we can jointly estimate an RTT bias of each access point (AP) and the user’s step length by leveraging the geometric relation mentioned above. It enables us to construct a user’s relative trajectory defined on the concerned AP’s local coordinate system. Second, we align every AP’s relative trajectory into a single one, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">trajectory alignment</i> , equivalent to transformation to the global coordinate system. As a result, we can estimate the sequence of the user’s absolute locations from the aligned trajectory. Various field experiments extensively verify the proposed algorithm’s effectiveness that the average positioning error is approximately 0.369 (m) and 1.705 (m) in LOS and NLOS environments, respectively.

Kalman Filter-Based Data Fusion of Wi-Fi RTT and PDR for Indoor Localization

The Fine Time Measurement (FTM) protocol introduced by IEEE 802.11 includes a new ranging method, named Wi-Fi Round Trip Time (Wi-Fi RTT), which can be used for indoor localization. Pedestrian Dead Reckoning (PDR) can provide accurate pedestrian tracking through inertial sensors in a short time. Information fusion of PDR and existing wireless technology is widely used in indoor localization to ensure the robustness and stability. In this paper, we propose a fusion indoor localization method of Wi-Fi RTT and PDR. Firstly, an adaptive filtering system consisting of multiple Extended Kalman Filter (EKF) and a new outlier detection method is proposed to reduce the localization error of Wi-Fi RTT. Secondly, the fusion algorithm based on the Federated Filter (FF) and observability is designed to combine Wi-Fi RTT with PDR. Finally, to further improve the localization performance of the fusion algorithm, a real-time smoothing method with fixed interval is used. We evaluate the proposed method in four different scenarios. The results show that the proposed indoor localization method has better stability and robustness, and the average localization error decreased by 37.4-67.6% compared with the classic EKF-based method.

Wi-Fi RTT-Based Active Monopulse RADAR for Single Access Point Localization

The purpose of this work is to implement a novel active radar system for single access point two-dimensional localization system based on 802.11 wireless local-area networks (WLAN). The active radar employes the 802.11 Fine Time Measurement (FTM) Round Trip Time (RTT) as specified in the 2016 update of WIFI Standard to calculate the ranging distance and acquires two channels of Received Signal Strength Indicator (RSSI) to estimate the azimuthal angle. In this way, real-time two-dimensional localization of Wi-Fi RTT-compatible mobile Internet of Things devices is demonstrated using a single access point operating in the 2.4GHz band. The experimental results performed in anechoic chamber and in a real environment point up a mean positioning error below one meter within a Field of View (FoV) of 60° and distances up to 14 meters, with an acquisition time below 0.5 seconds.

Evaluation and correction of smartphone-based fine time range measurements

ABSTRACT Wi-Fi-based positioning technology has been recognised as a useful and important technology for location-based service (LBS) accompanied by the rapid development and application of smartphones since the beginning of the 21st century. However, no mature technology or method of Wi-Fi-based positioning had provided a satisfying output in the past 20 years, until recently, when the IEEE 802.11mc standard was released and hardware-supported in the market, in which a fine time measurement (FTM) protocol and multiple round-trip time (RTT) was used for more accurate and robust ranging without the received signal strength indicator (RSSI) involved. This paper provided an evaluation and ranging offset correction approach for Wi-Fi FTM based ranging. The characteristics of the ranging offset deviation errors are specifically examined through two well-designed evaluation tests. In addition, the offset deviation errors from a CompuLab WILD router and a Google access point (AP) are also compared. An average of 0.181 m accuracy was achieved after a typical offset correction process to the ranging estimates obtained from a complex surrounding environment with line-of-sight (LOS) condition. The research outcome will become a useful resource for implementing other algorithms such as machine learning and multi-lateration for our future research projects.

Comparison of 2.4 GHz WiFi FTM- and RSSI-Based Indoor Positioning Methods in Realistic Scenarios.

With the addition of the Fine Timing Measurement (FTM) protocol in IEEE 802.11-2016, a promising sensor for smartphone-based indoor positioning systems was introduced. FTM enables a Wi-Fi device to estimate the distance to a second device based on the propagation time of the signal. Recently, FTM has gotten more attention from the scientific community as more compatible devices become available. Due to the claimed robustness and accuracy, FTM is a promising addition to the often used Received Signal Strength Indication (RSSI). In this work, we evaluate FTM on the 2.4 GHz band with 20 MHz channel bandwidth in the context of realistic indoor positioning scenarios. For this purpose, we deploy a least-squares estimation method, a probabilistic positioning approach and a simplistic particle filter implementation. Each method is evaluated using FTM and RSSI separately to show the difference of the techniques. Our results show that, although FTM achieves smaller positioning errors compared to RSSI, its error behavior is similar to RSSI. Furthermore, we demonstrate that an empirically optimized correction value for FTM is required to account for the environment. This correction value can reduce the positioning error significantly.

Wi-Fi RTT Ranging Performance Characterization and Positioning System Design

The aim of this research is to implement a precise Wi-Fi indoor positioning system (IPS) or localization system based upon the IEEE 802.11mc fine-timing measurement (FTM) scheme also known as the Wi-Fi round trip time (RTT) ranging technique, where ranging refers to a sub-process of positioning that determines the distance between a transmitter and receiver. Our system and its algorithms were implemented using a COTS (Commercial-Off-The-Shelf) smartphone and Wi-Fi access points. Experiments were conducted in several real-life indoor environments. This paper presents the detailed Wi-Fi RTT ranging performance of these devices in different system configurations and characterizes the systematic biases and noise model to improve the ranging accuracy. A novel three-step-positioning method is proposed to overcome the issues of no or multiple intersect points in trilateration due to ranging errors to improve positioning accuracy. This consists of the following: 1) systematic bias determination and removal; 2) clustering-based trilateration (CbT) supported by weighted concentric circle generation (WCCG), namely CbT & WCCG; 3) positioning result and trajectory optimization using a Kalman filter. As a result, the evaluation experiments gave a position accuracy of ±1.2 m in 2D static positioning and ±1.3 m for dynamic motion tracking. Also, our CbT & WCCG method demonstrate good tolerance against ranging errors. Moreover, the computational cost and positioning accuracy of CbT & WCCG methods are compared with least square (LS) and recursive least square (RLS) methods and the accuracy standard deviation of our algorithm is the closest to the Cramer–Rao bound (CRB).

Passive Round-Trip-Time Positioning in Dense IEEE 802.11 Networks

The search for a unique and globally available location solution has attracted researchers for a long time. However, a solution for indoor scenarios, where high accuracy is needed, and Global Positioning System (GPS) is not available, has not been found yet. Despite the number of proposals in the literature, some require too long a calibration time for constructing the fingerprinting map, some rely on the periodic broadcast of positioning information that may downgrade the data communication channel, while others require specific hardware components that are not expected to be carried on commercial off-the-shelf (COTS) wireless devices. The scalability of the location solution is another key parameter for next-generation internet of things (IoT) and 5G scenarios. A passive solution for indoor positioning of WiFi devices is first introduced here, which merges a time-difference of arrival (TDOA) algorithm with the novel fine time measurements (FTM) introduced in IEEE 802.11mc. A proof of concept of the WiFi passive TDOA algorithm is detailed in this paper, together with a thorough discussion on the requirements of the proposed algorithm.

Precise 3-D Indoor Localization Based on Wi-Fi FTM and Built-In Sensors

More and more applications of location-based services lead to the development of indoor positioning technology. As a part of the Internet-of-Things ecosystem, most existing indoor positioning algorithms are applied to specific situations, e.g., pedestrian navigation and target detection. To meet the high-precision indoor localization requirement, IEEE 802.11 included the Wi-Fi fine-time measurement (FTM) protocol in 2016, which provides a novel approach for Wi-Fi ranging between the mobile terminal and Wi-Fi access point (AP). This article proposes a precise 3-D indoor localization algorithm based on Wi-Fi FTM and smartphone built-in sensors (3D-WFBS). The adaptive extended Kalman filter (AEKF) is used to estimate the pedestrian's real-time heading and walking speed, and the received signal strength indication and round-trip time collected from Wi-Fi APs are combined for proximity detection and providing more accurate ranging results. In addition, the unscented particle filter is applied to fuse the results of AEKF, proximity detection, and Wi-Fi ranging. The experimental results show that compared with the existing dead reckoning method and the other fusion methods, the proposed 3D-WFBS algorithm is proved to achieve meter-level indoor positioning accuracy in typical indoor scenes.