Indoor positioning with Wi-Fi Location: A survey of IEEE 802.11mc/az/bk fine timing measurement research

PARISROC is a complete read out chip, in a BiCMOS SiGe 0.35μm technology from AustriaMicroSystems, for photomultipliers array. It allows triggerless acquisition for next generation neutrino experiments and is part of a R&D program called PMm2. The ASIC integrates 16 independent and auto triggered channels with variable gain and provides charge and time measurement by a 10-bit Wilkinson ADC and a 24-bit counter. The time measurement is made of 2 complementary systems: a 24-bit gray counter (coarse time) with a step of 100 ns, and a double ramp TDC (fine time) with a 10-bit resolution and a measured precision of 425 ps RMS. Only the analog TDC will be explained in this paper by detailing the double ramp TDC architecture, the special cares and the first fine time measurements. One of the fine time TDC characteristics is the fact that the double ramp generator is common to all channels.

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.

The recent standardization by IEEE of Fine Timing Measurement (FTM), a time-of-flight based approach for ranging has the potential to be a turning point in bridging the gap between the rich literature on indoor localization and the so-far tepid market adoption. However, experiments with the first WiFi cards supporting FTM show that while it offers meter-level ranging in clear line-of-sight settings (LOS), its accuracy can collapse in non-line-of-sight (NLOS) scenarios.We present FUSIC, the first approach that extends FTM’s LOS accuracy to NLOS settings, without requiring any changes to the standard. To accomplish this, FUSIC leverages the results from FTM and MUSIC – both erroneous in NLOS – into solving the double challenge of 1) detecting when FTM returns an inaccurate value and 2) correcting the errors as necessary. Experiments in 4 different physical locations reveal that a) FUSIC extends FTM’s LOS ranging accuracy to NLOS settings – hence, achieving its stated goal; b) it significantly improves FTM’s capability to offer room-level indoor positioning.

Until the implementation of the IEEE 802.11az standard in common devices becomes a reality, the IEEE 802.11mc fine time measurement (FTM) procedure used for location purposes in indoor environments may be easily compromised by an adversary. Despite the scarce amount of work focusing on the security of the FTM procedure, in the first place, this paper provides an overview of the vulnerabilities that have been studied so far. Lack of encryption and authentication allows an attacker to eavesdrop on any FTM session and/or forge the frame exchange. But how critical can this be? We study the situation where an adversary is able to overhear the FTM frames of a legitimate user that is positioning itself. On the one hand, we show that the adversary is able to easily infer the position of the victim. Moreover, simulation results show that this calculated position can be obtained with a 99th percentile error of 1 m even under the presence of errors in the time measurements, raising significant concern about the security of the current implementation of the protocol.

Smartphone-based WiFi ranging using fine time measurement (FTM) is severely impacted by Non-line-of-sight (NLoS) environments, which causes significant positioning errors. To address this problem, we propose a novel WiFi FTM positioning (WFP) approach based on the geomagnetism and enhanced genetic algorithm (EGA), which can simultaneously execute WiFi localization and ranging compensation. Based on the distribution of the ranging error in NLoS environments, a semiparametric error model-based ranging compensation method is proposed. To construct the EGA searching model, geomagnetism-based positioning is adopted and fed to the EGA together with the measured WiFi ranging data and the ranging compensation method. During online localization, the EGA model dynamically compensates for the erroneous ranging data until it finds the optimal position. Experimental results show that the ranging and localization accuracy of this EGA-based WFP are 1.33 m and 1.64 m, being an improvement of 30.7% and 56.5% compared to the uncompensated ranging data and the trilateration algorithm using the weighted least square (WLS) method, respectively.

Academic and industry research has argued for supporting WiFi time-of-flight measurements to improve WiFi localization. The IEEE 802.11-2016 now includes a Fine Time Measurement (FTM) protocol for WiFi ranging, and several WiFi chipsets offer hardware support albeit without fully functional open software. This paper introduces an open platform for experimenting with fine time measurements and a general, repeatable, and accurate measurement framework for evaluating time-based ranging systems. We analyze the key factors and parameters that affect the ranging performance and revisit standard error correction techniques for WiFi time-based ranging system. The results confirm that meter-level ranging accuracy is possible as promised, but the measurements also show that this can only be consistently achieved in low-multipath environments such as open outdoor spaces or with denser access point deployments to enable ranging at or above 80 MHz bandwidth.

Recently released Wi-Fi adapters, such as Intel AX200 802.11ax NIC, support both Channel State Information (CSI) measurement and Fine Time Measurement (FTM). Angle of Arrival (AoA) estimation with CSI, using MUltiple SIgnal Classification (MUSIC), and FTM are both promising localization methods. But each suffers from practical constraints pertinent to the specific hardware and firmware used. The result can be rather inaccurate localization if AoA or FTM alone were used. We identify the issues/challenges specific to AX200, and as a remedy, we propose a localization approach that combines both CSI-based AoA and FTM. Our approach does not require any modification of the localization target device. This makes the solution readily available for localizing smartphones or any Wi-Fi devices with FTM functionality. Our experimental evaluation shows that our approach achieves a successful localization ratio of 80%, with localization error less than 1m; and less than 0.5m for 66% of the experiments.

Radio frequency ranging protocols enable a device to estimate the distance to another device, based on signal propagation time. In theory, ranging protocols are promising for indoor localization as one can obtain the position of the pedestrian directly from the ranging results if the access point positions are known. However, in practice, indoor scenarios still pose a challenging problem as the observed distances vary greatly due to non-line-of-sight signal paths, delayed signal propagation, and general hardware inaccuracies. The IEEE 802.11-2016 (formerly IEEE 802.11mc) standard defines a RF ranging protocol for Wi-Fi, namely Fine Timing Measurement (FTM). In order to improve the position estimate, a novel sensor model for FTM is derived from observed data. It is shown that the FTM error varies with the actual distance to the access point. Within this work, different parameter sets are estimated from the observed data for skew normal distributions, depending on the actual distance. For these parameters, low-order polynomials are then fitted to obtain the distribution parameters as functions of the actual distance. Furthermore, a particle filter is described and evaluated in an industrial scenario using cheap Espressif ESP32-S2 IoT FTM access points in the 2.4 GHz band. The filter combines map information, Pedestrian Dead Reckoning, and our novel FTM sensor model to estimate the pedestrian's position in the building. Finally, the localization result of the particle filter is compared to another promising radio frequency ranging method: ultra-wideband.

A medição de distâncias usando o protocolo Fine Time Measurement (FTM), que é parte das especificações do Wi-Fi, apresenta limitações significativas devido ao multicaminhamento. Este trabalho propõe um filtro adaptativo assimétrico para melhoria das medições FTM, levando em conta características intrínsecas deste protocolo. O algoritmo desenvolvido implementa um filtro com parâmetros ajustados considerando a velocidade do alvo e a tendência do FTM em superestimar distâncias. Os experimentos demonstraram que o método reduz o erro médio das medições em até 50% quando comparado ao FTM puro. Sua baixa complexidade computacional o torna especialmente adequado para implementação em sistemas embarcados. Os resultados indicam que a solução é efetiva para aplicações de medição de distância em tempo real utilizando o Wi-Fi, inclusive em hardware de baixo custo.

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.

In this paper, we present ViTag to associate user identities across multimodal data, particularly those obtained from cameras and smartphones. ViTag associates a sequence of vision tracker generated bounding boxes with Inertial Mea-surement Unit (IMU) data and Wi-Fi Fine Time Measurements (FTM) from smartphones. We formulate the problem as association by sequence to sequence (seq2seq) translation. In this two-step process, our system first performs cross-modal translation using a multimodal LSTM encoder-decoder network (X-Translator) that translates one modality to another, e.g. recon-structing IMU and FTM readings purely from camera bounding boxes. Second, an association module finds identity matches between camera and phone domains, where the translated modality is then matched with the observed data from the same modality. In contrast to existing works, our proposed approach can associate identities in multi-person scenarios where all users may be performing the same activity. Extensive experiments in real-world indoor and outdoor environments demonstrate that online association on camera and phone data (IMU and FTM) achieves an average Identity Precision Accuracy (IDP) of 88.39% on a 1 to 3 seconds window, outperforming the state-of-the-art Vi-Fi (82.93%). Further study on modalities within the phone domain shows the FTM can improve association performance by 12.56% on average. Finally, results from our sensitivity experiments demonstrate the robustness of ViTag under different noise and environment variations.

Despite the large literature on localization, there is no solution yet to localize a commercial off-the-shelf smartphone device using radio measurements from a single WiFi AP. We present SPRING, Smartphone Positioning with Radio measurements from a sINGle wifi access point. SPRING exploits Fine Time Measurements (FTM) and Angle of Arrival (AOA) extracted from commercial chipsets exploiting the specifications of the recent 802.11-2016 and the 802.11ac amendment to combine distance and direction from the AP to the client for positioning. Our system has the potential to bring indoor positioning to homes and small businesses which typically have a single access point. We exploit physical layer (PHY) information to detect the number of paths and their directions. We use this information to derive a new method for filtering ranging measurements obtained with the FTM protocol. We achieve sub-meter distance estimation accuracy eliminating the adverse effect of multipath in FTM using calibrated inputs from Channel State Information (CSI). Our evaluation in indoor scenarios in multipath rich environments demonstrates that the combination of AOA estimation and the proposed FTM refinement approach can locate a Google Pixel 3 smartphone with a median positioning error of 0.9-2.15 m through an area comparable to typical flat sizes.

Toward the simulation of WiFi Fine Time measurements in NS3 network simulator

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.

Accurate indoor positioning using IEEE 802.11mc round trip time