Fuxi Wen

Bio: Fuxi Wen is an academic researcher from Chalmers University of Technology. The author has contributed to research in topics: Multipath propagation & Computer science. The author has an hindex of 13, co-authored 52 publications receiving 516 citations. Previous affiliations of Fuxi Wen include University of Sheffield & Nanyang Technological University.

Diffusion least-mean P-power algorithms for distributed estimation in alpha-stable noise environments

Abstract: A diffusion least-mean P-power (LMP) algorithm is proposed for distributed estimation in alpha-stable noise environments, which is one of the widely used models that appears in various environments. Compared with the diffusion least-mean squares algorithm, better performance is obtained for the diffusion LMP methods when the noise is with alpha-stable distribution.

An Impulse Radio Ultrawideband System for Contactless Noninvasive Respiratory Monitoring

Abstract: We design a impulse radio ultrawideband radar monitoring system to track the chest wall movement of a human subject during respiration. Multiple sensors are placed at different locations to ensure that the backscattered signal could be detected by at least one sensor no matter which direction the human subject faces. We design a hidden Markov model to infer the subject facing direction and his or her chest movement. We compare the performance of our proposed scheme on 15 human volunteers with the medical gold standard using respiratory inductive plethysmography (RIP) belts, and show that on average, our estimation is over 81% correlated with the measurements of a RIP belt system. Furthermore, in order to automatically differentiate between periods of normal and abnormal breathing patterns, we develop a change point detection algorithm based on perfect simulation techniques to detect changes in the subject's breathing. The feasibility of our proposed system is verified by both the simulation and experiment results.

5G Positioning and Mapping With Diffuse Multipath

Abstract: 5G mmWave communication is useful for positioning due to the geometric connection between the propagation channel and the propagation environment. Channel estimation methods can exploit the resulting sparsity to estimate parameters (delay and angles) of each propagation path, which in turn can be exploited for positioning and mapping. When paths exhibit significant spread in either angle or delay, these methods break down or lead to significant biases. We present a novel tensor-based method for channel estimation that allows estimation of mmWave channel parameters in a non-parametric form. The method is able to accurately estimate the channel, even in the absence of a specular component. This in turn enables positioning and mapping using only diffuse multipath. Simulation results are provided to demonstrate the efficacy of the proposed approach.

Fine-Grained Indoor Localization Using Single Access Point With Multiple Antennas

Abstract: We propose a single access point-based fine-grained indoor localization system in multipath environments using 80211ac Wi-Fi signals The physical layer of 80211ac supports orthogonal frequency-division multiplexing modulation (OFDM) scheme, more antennas, and multiuser multiple-input multiple-output Angle-of-arrival (AOA) of the paths can be obtained using the phase difference among all the antennas Similarly, time of arrival (TOA) information is estimated from the phase difference among all the subcarriers of the OFDM signal After obtaining the AOA and TOA information of the dominant paths, an auto paring approach is proposed to identify the line-of-sight (LOS) path Mobile device can be localized using the estimated AOA and TOA information of the LOS path Performance analysis of the proposed method is provided Numerical simulations are carried out to evaluate the performance Based on the simulation results, meter or submeter level accuracy is obtained for the proposed method

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

Abstract: 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.

Nonlinear System Identification

Abstract: This chapter contains sections titled: Historical Review Supervised Multilayer Networks Unsupervised Neural Networks: Kohonen Network Unsupervised Networks: Adaptive Resonance Theory Network Model Validation Summary References Recommended Exercises

A Survey on Model-Based Distributed Control and Filtering for Industrial Cyber-Physical Systems

Abstract: Industrial cyber-physical systems (CPSs) are large-scale, geographically dispersed, and life-critical systems, in which lots of sensors and actuators are embedded and networked together to facilitate real-time monitoring and closed-loop control. Their intrinsic features in geographic space and resources put forward to urgent requirements of reliability and scalability for designed filtering or control schemes. This paper presents a review of the state-of-the-art of distributed filtering and control of industrial CPSs described by differential dynamics models. Special attention is paid to sensor networks, manipulators, and power systems. For real-time monitoring, some typical Kalman-based distributed algorithms are summarized and their performances on calculation burden and communication burden, as well as scalability, are discussed in depth. Then, the characteristics of non-Kalman cases are further disclosed in light of constructed filter structures. Furthermore, the latest development is surveyed for distributed cooperative control of mobile manipulators and distributed model predictive control in industrial automation systems. By resorting to droop characteristics, representative distributed control strategies classified by controller structures are systematically summarized for power systems with the requirements of power sharing and voltage and frequency regulation. In addition, distributed security control of industrial CPSs is reviewed when cyber-attacks are taken into consideration. Finally, some challenges are raised to guide the future research.

Application of Linear-Frequency-Modulated Continuous-Wave (LFMCW) Radars for Tracking of Vital Signs

Abstract: This paper focuses on the exploitation of linear-frequency-modulated continuous-wave (LFMCW) radars for noncontact range tracking of vital signs, e.g., respiration. Such short-range system combines hardware simplicity and tracking precision, thus outperforming other remote-sensing approaches in the addressed biomedical scenario. A rigorous mathematical analysis of the operating principle of the LFMCW radar in the context of vital-sign monitoring, which includes the explanation of key aspects for the maintenance of coherence, is detailed. A precise phase-based range-tracking algorithm is also presented. Exhaustive simulations are carried out to confirm the suitability and robustness against clutter, noise, and multiple scatterers of the proposed radar architecture, which is subsequently implemented at the prototype level. Moreover, live data from real experiments associated to a metal plate and breathing subjects are obtained and studied.