In recent years, non-contact radar detection technology has been able to achieve long-term and long-range detection for the breathing and heartbeat signals. Compared with contact-based detection methods, it brings a more comfortable and a faster experience to the human body, and it has gradually received attention in the field of radar sensing. Therefore, this paper extends the application of millimeter-wave radar to the field of health care. The millimeter-wave radar first transmits the frequency-modulated continuous wave (FMCW) and collects the echo signals of the human body. Then, the phase information of the intermediate frequency (IF) signals including the breathing and heartbeat signals are extracted, and the Direct Current (DC) offset of the phase information is corrected using the circle center dynamic tracking algorithm. The extended differential and cross-multiply (DACM) is further applied for phase unwrapping. We propose two algorithms, namely the compressive sensing based on orthogonal matching pursuit (CS-OMP) algorithm and rigrsure adaptive soft threshold noise reduction based on discrete wavelet transform (RA-DWT) algorithm, to separate and reconstruct the breathing and heartbeat signals. Then, a frequency-domain fast Fourier transform and a time-domain autocorrelation estimation algorithm are proposed to calculate the respiratory and heartbeat rates. The proposed algorithms are compared with the contact-based detection ones. The results demonstrate that the proposed algorithms effectively suppress the noise and harmonic interference, and the accuracies of the proposed algorithms for both respiratory rate and heartbeat rate reach about 93%.

This paper uses an information-theoretic lens to view the error bound of Wireless Local Area Network (WLAN) localization, which is recognized as one of the superior candidate localization techniques in the GPS-denied environment. Interestingly, we analogize the process of WLAN localization into the one of information propagation in a parallel Gaussian noisy channel, and then derive the corresponding localization error bound from the channel capacity of the analogical information propagation system. Experimental results show that compared with the widely-known Cramer-Rao Lower Bound (CRLB), the proposed approach performs better in localization error bound estimation under most of the cases of different Access Point (AP) deployment and Reference Point (RP) calibration.

With the advancement of network and computer technologies, virtual cyberspace keeps evolving, and Metaverse is the main representative. As an irreplaceable technology that supports Metaverse, the sensing information transmission from the physical world to Metaverse is vital. Inspired by emerging semantic communication, in this paper, we propose a semantic transmission framework for transmitting sensing information from the physical world to Metaverse. Leveraging the in-depth understanding of sensing information, we define the semantic bases, through which the semantic encoding of sensing data is achieved for the first time. Consequently, the amount of sensing data that needs to be transmitted is dramatically reduced. Unlike conventional methods that undergo data degradation and require data recovery, our approach achieves the sensing goal without data recovery while maintaining performance. To further improve Metaverse service quality, we introduce contest theory to create an incentive mechanism that motivates users to upload data more frequently. Experimental results show that the average data amount after semantic encoding is reduced to about 27.87% of that before encoding, while ensuring the sensing performance. Additionally, the proposed contest theoretic based incentive mechanism increases the sum of data uploading frequency by 27.47% compared to the uniform award scheme.

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.

Indoor positioning in a multi-floor environment by using a smartphone is considered in this paper. The positioning accuracy and robustness of WiFi fingerprinting-based positioning are limited due to the unexpected variation of WiFi measurements between floors. On this basis, we propose a novel smartphone-based integrated WiFi/MEMS positioning algorithm based on the robust extended Kalman filter (EKF). The proposed algorithm first relies on the gait detection approach and quaternion algorithm to estimate the velocity and heading angles of the target. Second, the velocity and heading angles, together with the results of WiFi fingerprinting-based positioning, are considered as the input of the robust EKF for the sake of conducting two-dimensional (2D) positioning. Third, the proposed algorithm calculates the height of the target by using the real-time recorded barometer and geographic data. Finally, the experimental results show that the proposed algorithm achieves the positioning accuracy with root mean square errors (RMSEs) less than 1 m in an actual multi-floor environment.