Lingwen Zhang

Bio: Lingwen Zhang is an academic researcher from Beijing Jiaotong University. The author has contributed to research in topics: MIMO & Angle of arrival. The author has an hindex of 7, co-authored 11 publications receiving 168 citations.

Overview of received signal strength based fingerprinting localization in indoor wireless LAN environments

Abstract: In recent years, indoor localization technique is anticipated to play an increasingly important role in our society. In general, the indoor localization techniques include trilateral, triangulation and fingerprinting localization. As an important alternative, fingerprinting localization techniques are extensively studied and applied in the existing localization systems. This paper presents an overview of the fingerprinting localization system based on the received signal strength (RSS) in indoor wireless LAN (WLAN) environments. In particular, we describe the structure of localization system and then analyze the challenges that most existing research works try to deal with. Corresponding solutions and several pattern matching algorithms are also presented. Furthermore, we demonstrate a comparative study of existing fingerprinting localization systems and address the possible directions of future research in indoor localization techniques.

Hybrid TOA/AOA Cooperative Localization in Non-Line-of-Sight Environments

Abstract: The majority of the location estimation error in wireless communication systems comes from the effect of non-line-of-sight (NLOS) propagation. NLOS identification and correction are the main techniques of mitigating the NLOS impact on positioning accuracy. In this paper, we propose a cooperative localization algorithm that combines the hybrid time of arrival (TOA) / angle of arrival (AOA) measurements of all identified Line-of-Sight (LOS) base station (BS) - mobile station (MS) links with the TOA measurements of MS-MS links. Different cost functions are described according to the NLOS detection results based on existing identification methods. A NLOS correction model is also presented when the destination MS to be located is completely in NLOS propagation, whereas some BS - cooperative MS links are in LOS conditions. Simulation results demonstrate that the proposed algorithm outperforms other existing hybrid localization techniques, and its accuracy increases with the number of LOS BS-MS links, as well as the NLOS detection accuracy.

Fingerprinting localization based on affinity propagation clustering and artificial neural networks

Abstract: Fingerprinting localization techniques have been intensively studied in indoor WLAN environment. Artificial neural networks (ANN) based fingerprinting technique could potentially provide high accuracy and robust performance. However, it has the limitations of slow convergence, high complexity and large memory storage requirement, which are the bottlenecks of its wide application, especially in the case of a large-scale indoor environment and the terminal with limited computing capability and memory resources. In this paper, we firstly introduce affinity propagation (AP) clustering algorithm to reduce the computation cost and memory overhead, and then explore the properties of radio basis function (RBF) neural networks that may affect the accuracy of the proposed fingerprinting localization systems. We carry out various experiments in a real-world setup where multiple access points are present. The detailed comparison results reveal how the clustering algorithm and the neural networks affect the performance of the proposed algorithms.

A Survey of Indoor Localization Systems and Technologies

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.

Survey of Cellular Mobile Radio Localization Methods: From 1G to 5G

Abstract: Cellular systems evolved from a dedicated mobile communication system to an almost omnipresent system with unlimited coverage anywhere and anytime for any device. The growing ubiquity of the network stirred expectations to determine the location of the mobile devices themselves. Since the beginning of standardization, each cellular mobile radio generation has been designed for communication services, and satellite navigation systems, such as Global Positioning System (GPS), have provided precise localization as an add-on service to the mobile terminal. Self-contained localization services relying on the mobile network elements have offered only rough position estimates. Moreover, satellite-based technologies suffer a severe degradation of their localization performance in indoors and urban areas. Therefore, only in subsequent cellular standard releases, more accurate cellular-based location methods have been considered to accommodate more challenging localization services. This survey provides an overview of the evolution of the various localization methods that were standardized from the first to the fourth generation of cellular mobile radio, and looks over what can be expected with the new radio and network aspects for the upcoming generation of fifth generation.

Semisupervised Deep Reinforcement Learning in Support of IoT and Smart City Services

Abstract: Smart services are an important element of the smart cities and the Internet of Things (IoT) ecosystems where the intelligence behind the services is obtained and improved through the sensory data. Providing a large amount of training data is not always feasible; therefore, we need to consider alternative ways that incorporate unlabeled data as well. In recent years, deep reinforcement learning (DRL) has gained great success in several application domains. It is an applicable method for IoT and smart city scenarios where auto-generated data can be partially labeled by users’ feedback for training purposes. In this paper, we propose a semisupervised DRL model that fits smart city applications as it consumes both labeled and unlabeled data to improve the performance and accuracy of the learning agent. The model utilizes variational autoencoders as the inference engine for generalizing optimal policies. To the best of our knowledge, the proposed model is the first investigation that extends DRL to the semisupervised paradigm. As a case study of smart city applications, we focus on smart buildings and apply the proposed model to the problem of indoor localization based on Bluetooth low energy signal strength. Indoor localization is the main component of smart city services since people spend significant time in indoor environments. Our model learns the best action policies that lead to a close estimation of the target locations with an improvement of 23% in terms of distance to the target and at least 67% more received rewards compared to the supervised DRL model.

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Abstract: Location information for events, assets, and individuals, mostly focusing on two dimensions so far, has triggered a multitude of applications across different verticals, such as consumer, networking, industrial, health care, public safety, and emergency response use cases. To fully exploit the potential of location awareness and enable new advanced location-based services, localization algorithms need to be combined with complementary technologies including accurate height estimation, i.e., three dimensional location, reliable user mobility classification, and efficient indoor mapping solutions. This survey provides a comprehensive review of such enabling technologies. In particular, we present cellular localization systems including recent results on 5G localization, and solutions based on wireless local area networks, highlighting those that are capable of computing 3D location in multi-floor indoor environments. We overview range-free localization schemes, which have been traditionally explored in wireless sensor networks and are nowadays gaining attention for several envisioned Internet of Things applications. We also present user mobility estimation techniques, particularly those applicable in cellular networks, that can improve localization and tracking accuracy. Regarding the mapping of physical space inside buildings for aiding tracking and navigation applications, we study recent advances and focus on smartphone-based indoor simultaneous localization and mapping approaches. The survey concludes with service availability and system scalability considerations, as well as security and privacy concerns in location architectures, discusses the technology roadmap, and identifies future research directions.

ToneTrack: Leveraging Frequency-Agile Radios for Time-Based Indoor Wireless Localization

Abstract: Indoor localization of mobile devices and tags has received much attention recently, with encouraging fine-grained localization results available with enough line-of-sight coverage and hardware infrastructure. Some of the most promising techniques analyze the time-of-arrival of incoming signals, but the limited bandwidth available to most wireless transmissions fundamentally constrains their resolution. Frequency-agile wireless networks utilize bandwidths of varying sizes and locations in a wireless band to efficiently share the wireless medium between users. ToneTrack is an indoor location system that achieves sub-meter accuracy with minimal hardware and antennas, by leveraging frequency-agile wireless networks to increase the effective bandwidth. Our novel signal combination algorithm combines time-of-arrival data from different transmissions as a mobile device hops across different channels, approaching time resolutions previously not possible with a single narrowband channel. ToneTrack's novel channel combination and spectrum identification algorithms together with the triangle inequality scheme yield superior results even in non-line-of-sight scenarios with one to two walls separating client and APs and also in the case where the direct path from mobile client to an AP is completely blocked. We implement ToneTrack on the WARP hardware radio platform and use six of them served as APs to localize Wi-Fi clients in an indoor testbed over one floor of an office building. Experimental results show that ToneTrack can achieve a median 90 cm accuracy when 20 MHz bandwidth APs overhear three packets from adjacent channels.