A Robust Dead Reckoning Algorithm Based on Wi-Fi FTM and Multiple Sensors

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 first, systematic bias determination and removal; second, Clustering-based Trilateration (CbT) supported by Weighted Concentric Circle Generation (WCCG), namely CbT & WCCG; third, 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).

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.

As a result of the drastic increase in the number of mobile device users, there is considerable public debate about possible health hazards especially due to base stations and Wi-Fi access points. For this reason, in this study the effects of the number of users in a base station and wireless fidelity (Wi-Fi) access point on electric field strength (E) levels were investigated using real-time measurements. Two-stage E measurements were performed on Ondokuz Mayıs University's (OMU) Kurupelit Campus with a PMM-8053 and SRM-3006 electromagnetic field (EMF) meter. In the first stage, 24-h measurements were performed with PMM-8053 at the location where the maximum E was measured and from which the busiest times of the day were then determined. The relationship among band selective E values was assessed using the number of users per minute provided by three cellular system operators for the location. Upper and lower bounds of E according to the number of users were calculated, and then an empirical model that helps calculate the E of medium with 86% accuracy was proposed. In the second stage, the effect of the number of users in a Wi-Fi access point on E level was investigated. For this purpose, the measurement environment was set as including one Wi-Fi access point with needed measurement devices. The number of users was increased as per user per minute. The number of users was validated with the data obtained from OMU IT Department. Using the measurement result, a model with a 96% accuracy between the E value in the environment and the number of users accessing the Wi-Fi system is proposed. With the use of these models, E level in the medium can be determined without using any EMF meters, thus precautions can be taken to stay within regulatory limits.

Multi-sensor fusion based wheeled robot research on indoor positioning method

This paper focuses on how to optimize the deployment location of the anchor Wi-Fi access points (APs) in order to enhance the indoor positioning performance of the finetime measurement (FTM) protocol. Compared to conventional Wi-Fi positioning, FTM protocol is able to achieve relatively good indoor positioning performance since it estimates distance between a sender and its receiver with the flight duration of Wi-Fi signals. However, the FTM-based positioning accuracy in NLoS environments is still not high, which restricts its application in indoor scenarios. To this end, we formulate an optimization problem to maximize the total number of LoS paths by optimizing the locations of the anchor APs. To solve the problem, we propose a Deep Q-Network (DQN)-based AP deployment optimization method (DQ-WiAPDoM), which utilizes DQN to optimize the locations of APs such that the total number of LoS paths over all covered area is maximized. Experimental results show that DQ-WiAPDoM converges well and is able to reduce the average positioning error efficiently.

Sensors-based and radio frequency (RF)-based indoor localization technology is one of the keys in location-based services. The IEEE 802.11-2016 introduced the Wi-Fi fine timing measurement (FTM) protocol, which provides a new approach for Wi-Fi-based indoor localization. However, Wi-Fi signals are susceptible to complex indoor environments. To improve the positioning accuracy and stability, an enhanced particle filter (PF) with two different state update strategies, a new criterion for divergence monitoring and rapid re-initialization is proposed to integrate the advantages of pedestrian dead reckoning (PDR) and Wi-Fi FTM. In addition, an adaptive tilt compensation is proposed to improve real-time heading estimation of conventional PDR, and the Wi-Fi FTM outliers are detected by displacement estimation of the PDR. The experimental results show that the proposed PF has better localization performance than the single source positioning methods in a typical indoor scenario. The accuracy of final localization is within 1 m in 86.7% of the dynamic cases and the average calculation time is less than 0.5 s when the number of particles is 2000.

Indoor localization using a smartphone-based technique is the next generation of indoor navigation and tracking, leveraging the IEEE 802.11mc WiFi-based fine time measurement (FTM) capabilities. The technique enables GPS-like operation indoor by using smartphone devices embedded with WiFi round trip time (RTT) enabled chipset. FTM allows timestamping of send and receive events at the hardware level which in turn allows calculation of very accurate RTT to estimate distance. The technique is independent of infrastructure and is capable of networking and communicating through the formation of device clusters. Our indoor localization algorithms provide a localization error of less than 1.5m in 95% of the cases in typical university campus environments.

Optimizing AP and Beacon Placement in WiFi and BLE hybrid localization

Precise localization of Wi-Fi Access Point (AP) is becoming increasingly important with the rise of diverse location-based services based on smartphone. In this article, we propose a new method for precise Wi-Fi AP localization using GPS information of smartphone. The idea is that the possible area of Wi-Fi AP location, called AP_Area, is first determined by measuring GPS information and the Received Signal Strength (RSS) of smartphones from Wi-Fi AP. As the number of measurement points from users' smartphones increases, the AP_Area can be successively narrowed down to the actual AP location. Simulation results show that the proposed algorithm can detect the Wi-Fi AP localization within 5 m (probability over than 80%).

Indoor positioning technologies are of great use in GPS-denied areas. They can be partitioned into two types of systems—infrastructure-free based and infrastructure-dependent based. WiFi based indoor positioning system is somewhere between the infrastructure-free and infrastructure-dependent systems. The reason is that in WiFi based systems, Access Points (APs) as pre-installed infrastructures are necessary. However, the APs do not need to be specially installed, because WiFi APs are already widely deployed in many indoor areas, for example, offices, malls and airports. This feature makes WiFi based indoor positioning suitable for many practical applications. In this paper, a seq2seq model based, deep learning method is proposed for WiFi based fingerprinting. The model can learn from different length of training sequences, and thus can exploit the context information for positioning. The context information denotes the information contained in the sequence, which can help finding the correspondences between RSS fingerprints and the coordinate positions. A simple example piece of context information is human walking routine (such as no sharp turns). The proposed method shows an improvement with an open source dataset, when compared against deep learning based counterpart methods.

Research on position correction method for AUV large depth navigation based on ranging positioning

Smartphone-based WiFi ranging positioning based on fine time measurement (FTM) always collapses in real-life scenarios. In this work, a novel map-aided particle filter (PF)-based WiFi FTM fingerprinting approach is proposed to address the poor performance of the WiFi FTM ranging positioning. Different from manually collecting fingerprints, this approach utilizes the theoretical received signal strength and geometric distances between the access points and reference points as the fingerprints, which means less labour-intensive work. For accurate WiFi position estimation, a map-aided PF is designed to find the optimal position. Extensive experiments are carried out in the non-line-of-sight (NLoS) and mixed line-of-sight/non-line-of-sight (LoS/NLoS) environments, and the testing results show that the accuracy and stability of FTM fingerprinting are improved by using the mixed RSS and ranging data fingerprints. The minimal mean location errors (MEs) of the PF-based WiFi FTM fingerprinting in NLoS and mixed LoSINLoS conditions are 1.70 m and 1.85 m, respectively. Compared to the classic weighted least square method, the MEs are reduced by 54.91 % and 45.43 %, respectively. The testing results demonstrate that the PF-based FTM fingerprinting is an effective approach that provides satisfactory localization results in real-life indoor environments.

The paper answers a fundamental question: can the accuracy of Wi-Fi based localization be significantly increased by fusing information from alternate sources like LTE signals and magnetometers, collected through software defined radios and smart-devices? Further, it aims to eliminate dependency of well established localization techniques on only Wi-Fi access point (AP) positions, and instead, proposes a diversity-leveraging architecture called as the Wireless Locator (Wi-LO) framework. Wi-LO is a client-server paradigm for indoor localization that achieves precision by pattern-matching the collected signal samples with a priori references for each transmitter type, thus giving a stand-alone decision for these diverse sensing modes. The novelty of the paper is fusing these decision outcomes and resolving mis-matches (for e.g., Wi-Fi and LTE suggest different locations) in a seamless manner, and also identifying the best source to use at each location based on its spatial resolution. Wi-Lo is rigorously evaluated on a test-bed, with the proposed scheme of combining Wi-Fi, LTE and magnetometer performing better localization than the classical Wi-Fi-only approach in both urban (+8%) and rural (+25%) scenarios.

Indoor localization has recently and significantly attracted the interest of the research community mainly due to the fact that Global Navigation Satellite Systems (GNSSs) typically fail in indoor environments. In the last couple of decades, there have been several works reported in the literature that attempt to tackle the indoor localization problem. However, most of this work is focused solely on two-dimensional (2D) localization, while very few papers consider three dimensions (3D). There is also a noticeable lack of survey papers focusing on 3D indoor localization; hence, in this paper, we aim to carry out a survey and provide a detailed critical review of the current state of the art concerning 3D indoor localization including geometric approaches such as angle of arrival (AoA), time of arrival (ToA), time difference of arrival (TDoA), fingerprinting approaches based on Received Signal Strength (RSS), Channel State Information (CSI), Magnetic Field (MF) and Fine Time Measurement (FTM), as well as fusion-based and hybrid-positioning techniques. We provide a variety of technologies, with a focus on wireless technologies that may be utilized for 3D indoor localization such as WiFi, Bluetooth, UWB, mmWave, visible light and sound-based technologies. We critically analyze the advantages and disadvantages of each approach/technology in 3D localization.

As the number of smartphone users exploded, the demand for Location-Based Service (LBS) has increased. It is important for the LBS to specify the user location by utilizing the sensor built in the smartphone. Unlike outdoor localization, which can employ GPS, there are many challenging issues in indoor localization including non-line-of-sight (NLOS) and multipath effect. In our paper, we focus on Wi-Fi Fine Timing Measurement (FTM) which is a new function of the Android Pie Operating System (OS). We propose line-of-sight (LOS) identification algorithms applicable to Wi-Fi FTM and apply these algorithms to indoor localization based on multilateration methods. We utilize a hypothesis test framework and Support Vector Machine (SVM) to identify LOS signals. We divide LOS/NLOS signals as low and high-quality signals according to the degree of multipath error. We achieve high-quality signals identification rate of 92.4% on average in the sample size 99 and of 78.3% on average in the sample size 29. Therefore, we obtain a 24.4% localization performance improvement compared to the perfect LOS detector by using only high-quality signals to localization.