Target localization using a single receiver is highly needed due to the mobility of the target. In indoor environment, multipath signals are rich and usually relate the target location and structure of indoor environment through geometry parameters such as Angle of Arrival (AOA) and Time of Flight (TOF). Based on this, in this paper we propose a multipath-assisted indoor localization system using commodity WiFi signals which contain phase errors caused by imperfect hardware and non-synchronized clocks. The proposed system is different from conventional localization algorithms which treat multipath signals as enemies, and only uses a single receiver. To realize accurate localization without interferences of phase errors, we firstly construct a geometry model for jointly estimating the locations of the target and scatterers which can be regarded as objects such as furniture, by using TOF differences between reflection paths and direct path. Considering more constrains on target location, we select the direct path as the reference. Then, with the help of AOAs, we develop a location searching algorithm of the target and scatterers based on Particle Swarm Optimization (PSO). We have implemented the proposed system on the commodity WiFi devices, and the experiment results in actual indoor environment show that the median location error is around 1.5m by using only one receiver. What’s more, the intensive simulation results show that the proposed system is promising in the emerging networks such as 5G, where higher bandwidth of signal and more antennas can be used for providing accurate AOA and TOF.

With the wide deployment of Wi-Fi networks, Wi-Fi based indoor localization systems that are deployed without any special hardware have caught significant attention and have become a currently practical technology. At the same time, the Magnetic, Angular Rate, and Gravity (MARG) sensors installed in commercial mobile devices can achieve highly-accurate localization in short time. Based on this, we design a novel indoor localization system by using built-in MARG sensors and a Wi-Fi module. The innovative contributions of this paper include the enhanced Pedestrian Dead Reckoning (PDR) and Wi-Fi localization approaches, and an Extended Kalman Particle Filter (EKPF) based fusion algorithm. A new Wi-Fi/MARG indoor localization system, including an Android based mobile client, a Web page for remote control, and a location server, is developed for real-time indoor pedestrian localization. The extensive experimental results show that the proposed system is featured with better localization performance, with the average error 0.85 m, than the one achieved by using the Wi-Fi module or MARG sensors solely.

With the increasing demand for location-based services, indoor localization technology based on wireless sensor networks (WSNs) has recently received significant attention. Bluetooth low energy (BLE) has become a research hotspot among the numerous WSNs due to its ubiquitous deployment, low power consumption, and low cost. Many previous BLE-based indoor localization technologies can only use received signal strength indication (RSSI) for localization in the 2-dimensional (2D) plane and achieve coarse accuracy. However, high-accuracy location information is required in an increasing number of applications, e.g., elderly fall detection for medical applications, which urgently requires a new BLE-based localization technology in 3D space. To this end, we propose Spotlight, an angle-based fine-grained indoor localization system in 3D space using BLE sensors. The critical idea of the Spotlight is to measure the angle of arrival (AoA) and elevation of angle (EoA) of the signal from the target to the BLE receivers and utilize them to achieve accurate 3D localization. Specifically, we develop offset elimination algorithm and data stitching algorithm, utilizing the second-order cone (SoC) programming to obtain accurate angle estimates. Then the simulated annealing algorithm (SAA) is used to search for the target location in 3D space. Finally, we develop the prototype system with commercial off-the-shelf BLE sensors and conduct extensive experimental measurements in real-world scenarios. The results demonstrate that the proposed system can achieve a median localization error of 36 cm in 2D plane and 48 cm in 3D space.

The aim of this paper is to present a new indoor localization approach by employing the Angle-of-arrival (AOA) and Received Signal Strength (RSS) measurements in Wi-Fi network. To achieve this goal, we first collect the Channel State Information (CSI) by using the commodity Wi-Fi devices with our designed three antennas to estimate the AOA of Wi-Fi signal. Second, we propose a direct path identification algorithm to obtain the direct signal path for the sake of reducing the interference of multipath effect on the AOA estimation. Third, we construct a new objective function to solve the localization problem by integrating the AOA and RSS information. Although the localization problem is non-convex, we use the Second-order Cone Programming (SOCP) relaxation approach to transform it into a convex problem. Finally, the effectiveness of our approach is verified based on the prototype implementation by using the commodity Wi-Fi devices. The experimental results show that our approach can achieve the median error 0.7 m in the actual indoor environment.

The Non-Line-of-Sight (NLOS) propagation degrades the performances of mobile computing applications such as indoor localization when the Line-of-Sight (LOS) propagation between Access Point (AP) and client is blocked due to the mobility of users and building structure. Thus, the ability of sensing environment, i.e., awareness of LOS/NLOS propagation is significant important. Many previous systems relied on Channel State Information (CSI) statistic to construct LOS identification schemes by using the commodity WiFi network. However, the vulnerability of these systems is that the robustness is not considered carefully since the system configuration such as operating channel affects statistic results. Up to now, the estimation of spatial parameters of Multipath Components (MCPs) is not challenging due to the advanced Multiple-input-multipleoutput (MIMO) technique. In this paper, we propose an robust LOS/NLOS propagation identification system by using WiFi signal. We first resolve the Angle-of-arrival (AOA) and Timeof- flight (TOF) of MCPs, and then creatively introduce Hopkins statistic to measure the spatial randomness of LOS and NLOS propagation, respectively. We finally employ the non-parametric method to calculate the optimal threshold of Hopkins statistic to differ the two cases. The experimental results show that the proposed system achieves LOS and NLOS identification rates over 91% and 87% respectively under different system configuration. Furthermore, the upper-layer indoor localization applications can be benefited by cooperating with the proposed algorithm, and the case study of an indoor localization removes large localization errors effectively.

With the development of the Internet of Things (IoT), indoor localization technology plays a vital role in smart factories, smart cities, and other fields. When using the network infrastructures such as Wi-Fi, some conventional localization technologies usually need multiple access points (APs), which involve time and data synchronizations. As a result, those drawbacks hinder the deployment of those localization technologies. In this article, we propose TriLoc, a single AP-based indoor localization system with the assistance of microwave reflections, which has the minimum overheads compared to the existing schemes. The insight is that the reflection paths and the direct path experience the same phase errors, and thus, it can be used for locating the user device using only a single AP. In the system, we first estimate the angle of arrivals (AoA) and time of flight (ToF) of propagation paths and then use the multipath triangle that consists of one first-order reflection path and the direct path to locate the user device. Finally, we construct the prototype using software-defined radio and evaluate it in various indoor environments. The experimental results show that TriLoc can achieve submeter localization accuracy using only a single AP.