The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics. Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.

From RSSI to CSI: Indoor Localization via Channel Response, A survey on indoor localization using PHY-layer information

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.

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

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of Things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.

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Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Sensor-based and vision-based human activity recognition: A comprehensive survey

Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network.

A Survey of Localization in Wireless Sensor Network

Gaussian message passing-based cooperative localization on factor graph in wireless networks

Geometrical kinematic modeling on human motion using method of multi-sensor fusion

The channel state information (CSI) measurement, which characterizes the multipath channel between the transmitter and the receiver, can serve as a fingerprint for the receiver position. Recently, a series of deep learning-based indoor positioning fingerprinting (FP) methods using CSI have been proposed to enhance the localization performance. In this paper, we present a deep neural network (DNN)-based indoor positioning FP system using CSI, which is termed DNNFi. The proposed DNNFi, which maintains a single DNN instead of multiple deep autoencoders at different reference points, allows a faster computation for the online inference and a lower memory usage for the weights/biases. A stack of autoencoders is utilized to pre-train the weights layer-by-layer. The softmax function is utilized to decide the probabilities of the receiver position being on these reference points, which can be used to estimate the receiver position. Experimental results are presented to confirm that DNNFi can effectively reduce location error compared with the conventional CSI positioning FP approaches.

A Deep Neural Network-Based Indoor Positioning Method using Channel State Information

Location estimation and tracking for the mobile devices have attracted a significant amount of attention in recent years. The network-based location estimation schemes have been widely adopted based on the radio signals between the mobile device and the base stations. The location estimators associated with the Kalman filtering techniques are exploited to both acquire location estimation and trajectory tracking for the mobile devices. However, most of the existing schemes become inapplicable for location tracking due to the deficiency of signal sources. In this paper, two predictive location tracking algorithms are proposed to alleviate this problem. The predictive location tracking (PLT) scheme utilizes the predictive information obtained from the Kalman filter in order to provide the additional signal inputs for the location estimator. Furthermore, the geometric-assisted PLT (GPLT) scheme incorporates the geometric dilution of precision (GDOP) information into the algorithm design. Persistent accuracy for location tracking can be achieved by adopting the proposed GPLT scheme, especially with inadequate signal sources. Numerical results demonstrate that the GPLT algorithm can achieve better precision in comparison with other network-based location tracking schemes.

https://ir.nctu.edu.tw:443/bitstream/11536/6331/1/000270779500008.pdf

Wireless Location Tracking Algorithms for Environments with Insufficient Signal Sources

Position fingerprinting (FP), in which a common position signature is based on the received signal strength (RSS), is one of the most efficient indoor positioning methods. Another position signature, known as the channel impulse response (CIR), is regarded as a linear temporal filter, which characterizes the multipath channel of the operating environment. We implement a channel sounder based on an orthogonal frequency-division multiplexing system to collect off-line/online CIR measurements and develop a ray-tracing (RT) channel predictor to capture the main characteristics of the channel for the off-line predicted database. We are the first to utilize RT as a channel predictor to assist indoor FP using CIR measurements. We utilize coarse localization to classify the reference points (RPs) based on the access point with the strongest RSS. We propose an RT-assisted FP (RAFP) method, in which we estimate a position by fusing the measured and predicted signatures to find the RPs with the highest correlation values between the online measurement and the off-line measured and simulated CIR databases. Experimental results show that the RAFP—positioning with a hybrid of the predicted and measured CIR—reduces the FP localization error by 25%. By incorporating simulated CIRs, the RAFP has the advantages in reducing human labor for off-line measurement collection and in using less number of CIR measurements to maintain a satisfactory performance. The results encourage a further development to reduce the cost by replacing the sounding system with the wireless network interface cards for a scalable deployment.

Po-Hsuan Tseng

Papers

Gaussian message passing-based cooperative localization on factor graph in wireless networks

Geometrical kinematic modeling on human motion using method of multi-sensor fusion

A Deep Neural Network-Based Indoor Positioning Method using Channel State Information

Wireless Location Tracking Algorithms for Environments with Insufficient Signal Sources

Ray-Tracing-Assisted Fingerprinting Based on Channel Impulse Response Measurement for Indoor Positioning