Integrated sensing and communication (ISAC) utilizes existing communication devices for sensing and is emerging as a key technology in wireless networks, particularly for mobile applications such as vehicular networks. Most systems rely on path parameters, such as direction of arrival (DoA), for accurate sensing. However, commercial communication devices often adopt wider antenna spacings to enhance communication performance, which can lead to grating lobes and reduce DoA accuracy in mobile environments. To address this issue, we investigate the variation of grating lobes across OFDM subcarrier frequencies and propose a differential frequency array (DFA) model to suppress grating lobes through subcarrier cooperation. Furthermore, we develop an off-grid DoA estimation algorithm based on sparse Bayesian learning, tailored to the DFA structure. Simulation results show that the proposed method effectively suppresses grating lobes and significantly improves DoA estimation accuracy. Prototype experiments based on 5G picocells further confirm its feasibility in practical mobile ISAC scenarios.

Precise distance measurement using wireless signals is essential for applications such as structural health monitoring, autonomous navigation, and asset tracking. Traditional high-accuracy ranging techniques typically rely on large signal bandwidths, often several hundred megahertz, to resolve carrier-phase ambiguities and achieve centimeter-level precision. However, this results in increased system complexity and high spectral resource consumption. To address these challenges, this paper proposes an optimized dual-differential carrier phase measurement method that achieves centimeter-level accuracy using only a few of discrete frequency bands, each occupying a bandwidth of approximately 20 MHz. Carrier-phase measurements at the selected frequencies are first obtained and wrapped within the (−π,+π) range. Dual-differential operations are then applied between adjacent positions to eliminate systematic phase biases and improve estimation robustness. A weighted least-squares estimator provides initial float ambiguity estimates, which are subsequently resolved using the Modified Least-squares Ambiguity decorrelation adjustment (MLAMBDA) algorithm. Compared with conventional ranging methods, the proposed approach significantly reduces bandwidth requirements while maintaining sub-5 cm relative positioning accuracy. It is particularly well-suited for deployment in bandwidth-constrained Integrated Sensing and Communication (ISAC) applications.

This paper proposes a WiFi-based self-constructing heatmap technique aimed at addressing the inefficiency and high cost of traditional signal strength measurement methods. The study employs a single-base station algorithm that utilizes Channel State Information (CSI) to extract angle and time-related parameters, enabling accurate positioning of User Equipment (UE) and eliminating the need for manual position measurements. We employ a Multi-Layer Perceptron (MLP) to learn the signal power distribution in a simulated environment. To enable effective transfer from simulated data to real-world data, we incorporate a Variational Auto-Encoder (VAE). Ultimately, the trained model is used to generate complete and high-precision heatmaps. Experimental results show that the proposed method can generate high-accuracy heatmaps using a relatively small dataset in typical indoor environments, with prediction errors below 4dB in 90% of the area. It outperforms traditional methods such as Kriging and Multivariate Gaussian Process Regression (MGPR), providing reliable support for network optimization and localization applications.

Sensing-assisted communication plays a critical role in integrated sensing and communication (ISAC) systems, where environmental awareness can significantly enhance communication performance. In this paper, we propose a novel indoor environment reconstruction method for Sub-6GHz systems. The approach first leverages multiband channel state information (CSI) to enable accurate localization of user equipment (UE). Based on the estimated UE positions and multiband CSI, we further estimate the distances of multipath components (MPCs). These distances, combined with UE positions, are used to infer the positions of virtual-access points (VAPs), which serve as key intermediaries for identifying environmental reflectors. By aggregating information across multiple UEs, we compute a reflector point cloud that supports high-fidelity reconstruction of the indoor environment. Our proposed algorithm is validated through simulations, achieving a corner point localization error of 0.03 meters and an intersection over union (IoU) of 0.98. Experimental results demonstrate the effectiveness of the method in reconstructing typical rectangular indoor layouts using only Sub-6GHz CSI.

Integrated Sensing and Communication (ISAC) is expected to be one of the key technologies for next-generation communication systems, with extensive application prospects in fields such as low-altitude economy, security surveillance, and smart cities. As a significant application of ISAC, Inverse Synthetic Aperture Radar (ISAR) technology offers notable advantages in detecting and imaging moving objects. However, current ISAR techniques, which primarily rely on processing along dimensions such as time-Doppler, face challenges such as limited detection accuracy and insufficient extensibility. Inspired by ISAR imaging principles, based on the ISAC echo signal model, a far-field wavenumber domain ISAR imaging method based on minimum image entropy is proposed, which realizes multiframe imaging of non-cooperative moving targets. Moreover, an ISAC system is implemented based on the 5 G communication millimeter-wave platform, and experimental results are presented to validate the performance and feasibility of the proposed system.

With the emergence of the Internet of Things (IoT), indoor positioning services have garnered increasing attention. When it comes to target tracking, the uncertainty surrounding velocity poses a challenge in obtaining the initial state, and the setting of this initial state significantly impacts early tracking performance. To address this issue, this paper proposes an extended Kalman filter (EKF) algorithm based on a dual model, aiming to mitigate the influence of this problem. Although the tracking performance of the without velocity constrained (WVC)-EKF model falls short compared to that of the velocity constraint (VC)-EKF model, the WVC-EKF model eliminates the need to grapple with the difficulty of setting the initial velocity. Consequently, the tracking process benefits from the combined strengths of both models. Firstly, the early tracking is accomplished using the WVC-EKF model. Subsequently, the WVC-EKF model serves as an auxiliary component to assist the VC-EKF model in establishing the initial state. Finally, the VC-EKF model takes charge of the subsequent tracking. In this paper, ray-tracing software was employed for simulation purposes, and the obtained results demonstrate that the proposed algorithm enhances tracking accuracy.

With the proposal and development of integrated sensing and communication (ISAC), utilizing existing communications devices to realize the function of sensing is the current hot research. In existing research, the system is usually implemented based on path parameters, i.e., parameters such as direction of arrival (DoA). Obtaining accurate angle information requires the assumption that antennas are equally spaced and at a standard half wavelength. However, the antenna spacing of commercial-off-the-shelf (COTS) communication devices is often arranged in communication in a manner that is wider than half-wavelength, which may produce grating lobes that result in incorrect angle estimates. Therefore, to resolve this challenge, this paper first deeply analyzes the problem of incorrect angle estimates due to grating lobes. Then, we propose a phase projection-based ambiguity eliminate model and a power-based parameter estimate algorithm. Finally, simulation experiments are conducted to verify the proposed algorithm performance, and a system prototype is built for field trial. The experimental results show that using a 4 -antenna array, the proposed algorithm achieved median errors of$6.12^{\circ}, 4.18^{\circ}$, and 2.53° for antenna spacing of$0.6,0.8$, and 1 times the wavelength, respectively. In addition, we use localization as a case study to demonstrate the promising work done in this paper for ISAC.

Owing to the proliferation of Wi-Fi devices, the employment of Wi-Fi for indoor localization has emerged as the predominant trend within the realm of indoor localization technologies. In this paper, we harness multipath assistance for the purpose of localizing indoor terminals and generating maps. Precisely, Uniform Circular Array (UCA) is deployed at both the Access Point (AP) and the terminal to augment the power of reflection paths. By constructing a triangular geometric configuration with the Angle of Arrival (AoA), Angle of Departure (AoD), and Time of Flight (ToF) of the reflection path as well as the direct path, the localization of the terminal can be accomplished. Subsequently, the localization results are optimized through Extended Kalman Filtering (EKF) tracking. Moreover, by integrating the optimized positional information, the locations of reflectors can be estimated. The positions of these scattering points mirror diverse components of the indoor scene. As the terminal device undergoes movement, more scene information within the indoor environment is acquired. Ultimately, by obtaining a sufficient number of portions of the locations of all scenes and interconnecting them, the generation of an indoor map is achieved. We simulated the indoor environment using the Wireless Insite software and extracted simulation data to verify the proposed system. The outcomes demonstrate that the system has attained a situation where 90% of the localization errors are within 1 meter and the accuracy of the generated map has reached 94.6%.

Faced with the increasing demand for indoor localization, global navigation satellite systems perform significantly worse in indoor environments than in outdoor scenarios. Existing indoor algorithms also have high requirements for the environment and terminal devices. To address this, we propose a new simultaneous localization and mapping algorithm that improves the Rao-Blackwellized probability hypothesis density filter, achieving target tracking using only Time of Flight with the assistance of an inaccurate floor plan and we incorporate a new data association method into this framework. Additionally, given that most current tracking algorithms are based on a known initial position, we have proposed an algorithm to determine the target's initial position. Simulation results demonstrate the effectiveness of the algorithm.

The vehicle key tracking technology plays a crucial role in modern vehicles. It not only enhances the security of the vehicle but also provides a more convenient user experience, making it an indispensable component of modern smart vehicles. This paper presents a vehicle key tracking system based on the identification of non-line of sight (NLOS) anchors, enabling more accurate tracking. The system first utilizes a direct positioning estimation (DPE) algorithm to estimate the target's position, followed by calculating the distances between the estimated position and anchors. These distances are then compared with the actual measurement data to determine an appropriate threshold for distinguishing between NLOS and LOS signals, with the ranging results from LOS signals used for tracking. Finally, the system was tested using a software-defined radio (SDR) platform. Experimental results demonstrate that the proposed algorithm achieves a median tracking error of 0.24 meters, compared to a median tracking error of 12 meters when tracking without using the NLOS identification algorithm. Furthermore, the 90 % tracking error for the circular trajectory is 0.72 meters.

The Global Navigation Satellite System (GNSS) offers advantages such as all-weather operability and extensive spatial coverage. Utilizing GNSS-reflected signals for ground synthetic aperture radar (SAR) imaging presents a cost-effective and widely applicable technical solution. However, the small bandwidth of GNSS signals results in inadequate resolution, posing challenges for practical applications. To address this issue, an SAR fusion imaging system model is established, consisting of multiple satellites and a single ground-fixed GNSS receiver. The relationship between the ambiguity function of GNSS signals and Fisher information is investigated, allowing for the derivation of the Cramer-Rao lower bound (CRLB) for the system, which is primarily influenced by the geometrical configuration of the bistatic setup. Subsequently, the CRLB expression is employed to identify the optimal resolution direction of the satellites for ground targets, and a dual-satellite SAR imaging fusion method based on optimal matching is proposed. The effectiveness of this approach is validated through simulations and real experimental data, demonstrating that the theoretically optimal resolution direction predicted by the CRLB aligns with the actual imaging results. Furthermore, the proposed method achieves higher resolution compared to traditional techniques, with the fused imaging results demonstrating a clear correspondence with the satellite imagery of the scene map.

Recent research has increasingly focused on utilizing radio frequency (RF) signals for indoor sensing. Traditionally, this involves employing antenna arrays configured across multiple RF links to capture key channel parameters, such as Direction of Arrival (DoA). However, this architecture requires each sensor to have an independent RF link, which increases complexity and cost. Additionally, deploying sensing systems necessitates pre-calibration of the RF links, further laboring the deployment. To address these challenges, we design a switched antenna array (SAA) that can time-division activate each antenna within the coherence time on a single RF link, thus simulating a multi-RF links platform for accurate DoA estimation. Subsequently, we develop a switching strategy and introduce a random forest-based matching algorithm to tackle the issue that signals from different antennas exported from the single RF link cannot be differentiated due to the non-cooperative mode between the SAA and receiver. Additionally, we compensate for the carrier frequency offset caused by asynchrony between transceivers, which affects DoA estimation in the SAA system. However, residuals still remain and can be considered as an enhancement to the noise. We introduce a DoA estimation algorithm based on sparse Bayesian learning that treats noise as its hyperparameter, enhancing the robustness against noise and improving the accuracy of DoA estimation. We build prototype systems and conduct field trials in real-world environments. The experimental results show that our system achieves 4.32° angle of arrival and 4.48° elevation of arrival median estimation errors by utilizing only a single RF link.

In response to the drawbacks of indoor multi-station joint localization, this paper proposes a high-precision indoor single-station localization technique based on the multipath shape factor (SF). A geometric localization model was constructed using the reflection and direct path. The localization equation was constructed using time of flight (ToF) and angle of arrival (AoA), and the Cramér-Rao lower bound (CRLB) of localization error was derived. Through CRLB, this paper introduced multipath SFs and derived the calculation expression of SFs. However, localization accuracy is affected by observation errors and other factors, and no effective method has been proposed yet. To improve localization accuracy and reduce algorithm complexity, this paper first proposes a compressive sensing multidimensional parameter estimation algorithm based on particle swarm optimization (PSO). Then, this paper proposes a multipath selection algorithm based on the SF. Finally, we conduct the simulation using commercial-grade wireless ray propagation modeling software. The experimental results show that the median location error is around 0.18 m by combining the two algorithms proposed in this paper.

Ranging and localization are crucial elements in the field of sensing applications, with range-based localization techniques being a prevalent approach. However, traditional range-based localization methods are impacted by ranging accuracy, furthermore, ranging accuracy is related to bandwidth. In this paper, we achieve high resolution by splicing the channel state information (CSI) of multiple non-adjacent frequency bands. However, the multiband CSI of GHz-level sub-band spacing leads to ambiguous delay estimation. To address this issue, we first explain the reason for the delay ambiguity caused by multiband CSI. Then, a set of candidate delays is constructed using the multiband CSI estimation delay. We propose a lo-calization algorithm that utilizes the set of candidate delays to achieve accurate localization. In turn, we use the estimated accurate location for accurate ranging. Finally, we validate our proposed localization and ranging algorithm through simulation experiments, achieving a localization error of 0.02m in 90% of cases, and a ranging accuracy of 0.01m in 94% of cases. Experimental results demonstrate that the algorithm proposed in this paper effectively exploits the advantages of multiband CSI for enhanced ranging and localization.

As the demand for location-based services has surged, indoor localization has become a research hotspot. In this paper, we employ convolutional neural network (CNN) and configurable multipath fingerprint methods for indoor lo-calization. To validate this approach, we design an indoor localization system based on only a single access point (AP). In this system, we first configure active reflectors within the indoor environment to stabilize the configurable multipaths, and model the wireless channel under conditions where configurable multipaths exist. Next, we utilize the two-dimensional recursive applied and projected multiple signal classification algorithm to jointly estimate the angle of arrival and time of flight, and then convert the results into spectral images. Then, these spectral images are fed into our designed CNN model for training, and the model’s output is subsequently matched with the target location. Finally, we conduct experiments using the commercial-grade electromagnetic propagation simulation software Wireless InSite. The experimental results show that with two deployed reflectors, the median error is 0.40 m, which outperforms other localization systems based on a single AP. Moreover, the introduction of active reflectors further enhances the system’s generalization ability.

Indoor accurate ranging using WiFi signals is a challenge since it is affected by the signal bandwidth, indoor environment, and phase distortions introduced by the underlying hardware. In this paper, we improve channel impulse response (CIR) resolution by splicing channel state information (CSI) measurements from multiple non-contiguous bands. However, most previous efforts have directly used non-contiguous CSI measurements for parameter estimation, which leads to multi-band gain loss and ranging performance degradation. To solve this problem, we propose a novel time of flight (TOF) estimation scheme that includes two stages. In the first stage, we build an equivalent optimization model of the inverse non-uniform discrete Fourier transform (INDFT) to transform the CSI samples to the CIR. In the second stage, we construct a time domain super-resolution TOF estimation model and combine the estimation results of the first stage to obtain the distance information using a subspace projection method. Finally, we conduct the simulation using commercial-grade wireless ray propagation modeling software. Based on the simulation result, the ranging error of our approach is less than 10 cm 85 % with the spliced bandwidth of 240 MHz.

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.

Some published WiFi-based passive human tracking systems have achieved sub-meter accuracy. However, they require the location of WiFi devices to be known in advance for passive human tracking, which limits their application in practical indoor scenarios. In this paper, we propose WiSen, a novel passive human tracking system using one pair of commodity WiFi devices with unknown locations. First, we introduce a signal power model for human-related signal extraction and multi-dimensional parameter estimation. Due to low-resolution parameter estimates and noise, we further design a confidence-aware-based path pruning method that combines the distribution of path parameters from successive windows to select reliable paths of interest. Before that, we adopt a data augmentation method to increase the number of available paths to learn parameter distributions better. Then, we statistically estimate the transmitter's location using a kernel density estimation method and ultimately yield the user's location using an improved Gaussian Sum filter approach. We validate the performance of WiSen in real-life indoor environments. The experimental results show that WiSen can realize the sub-meter level accuracy for passive human tracking and device localization.

Indoor localization systems in wireless sensor networks (WSNs), such as WiFi BLE, are still not widely adopted despite years of continuous research. One possible impediment is that many prior systems perform worse in non-line-of-sight (NLoS) indoor scenarios than in line-of-sight (LoS) scenarios or even fail to work. Nevertheless, realizing localization in both LoS and NLoS scenarios is indispensable for a viable system working in indoor environments. Since existing state-of-the-art solutions mainly aim to localize a target in LoS indoor scenarios, we present 3DLoc that uses multipath channel parameters to achieve 3D localization with a single AP in indoor NLoS scenarios. 3DLoc introduces deployable and simple custom-designed radio relays to assist localization, which can receive signals from the target and forward them to the AP in NLoS scenarios. Field trials conducted in a real NLoS scenario show that our system achieves a median 3D localization accuracy of 86 cm with a uniform circular array (UCA) in NLoS conditions.

Tracking is promising in various applications, such as elderly care and security monitoring. Prior works require multiple access points (AP) deployed in the tracking region or to utilize elaborately selected multipaths in an indoor environment to achieve tracking. However, this prevents their wide adoption because scenarios like homes typically only have a single AP, and using multipaths is vulnerable to unexpected interferences due to the complex indoor environment. This paper proposes a tracking system using only a single AP. The insight behind the proposed system is to mine the multi-dimensional channel parameters from the direct path to realize tracking. Therefore, we develop a model that establishes the correlation between the target motion and channel parameters, including the direct path's angle of arrival (AoA) and angle of departure (AoD). Then, we can formulate the tracking problem as the state transition with the observations. Since the nonlinear observation equation, we use particle filters to solve the problem accurately. Finally, we validate the proposed system via simulation using ray-tracing software. The simulation results show that the median tracking error is 0.21 m based on a 20 dB signal-to-noise ratio (SNR), comparable to previous state-of-the-art systems.

Wireless sensing attracts significant attention in recent years, due to its ubiquitous nature, individual privacy-preserving ability, and potential for future applications, such as home security and the Internet of Things (IoT). Among the sensed information, the dynamic human target acceleration, which can be used for gait analysis and passive localization, plays an irreplaceable role. Considering the target velocity is composed of initial velocity and acceleration, this paper proposes a model to describe the relationship between the acceleration of the dynamic target and the CSI phase change between adjacent CSI packets. Based on the proposed model, the target acceleration can be estimated directly from CSI via the fractional Fourier transform, which is essentially different from the existing algorithm that derives the acceleration from target velocity. The real-world evaluation shows that the median acceleration estimation error can reach about 0.69 m/s2, verifying the effectiveness of the proposed model.

In recent years, the research on indoor 3D localization has attracted a lot of attention in the applications of smart home, smart factory and other fields. However, as it is difficult for the hardware to meet the Nyquist sampling rate of half wavelength in the 5G/6G cases, using conventional subspace methods will lead serious pseudo peaks in parameter estimation, resulting in a sharp decline in estimation accuracy. In order to solve this problem, we propose a sparse parameter estimation and 3D localization method based on orthogonal matching pursuit algorithm (OMP). Firstly, we design an L-shaped sparse antenna to construct a sparse array manifold. Based on 2D angle of arrival (AoA) and time of flight (ToF), we construct a 3D parameter estimation model and a 3D localization method based on direct path. Then we convert the 3D parameter coupling estimation into two 2D parameter coupling estimation. Finally, we verify the feasibility of the proposed method through Wireless Insite simulation platform.

Wi-Fi based passive human detection has attracted numerous research interests recently. For the real-world application, however, the human detection under the through-the-wall (TTW) scenario needs to be addressed. In this paper, we consider the signal spatial distribution from a statistical perspective and propose TWPad, a unified scheme for TTW stationary and moving human detection based on Wi-Fi channel state information (CSI). Specifically, TWPad first extracts the angle of arrival (AoA) of the multipath signals under the TTW scenario and conduct the Jarque-Bera (JB) test on AoA to analyze the normality of the signal spatial distribution. Then, a novel joint Mann-Whitney U (for non-normal distribution) and T-test (for normal distribution) hypothesis test algorithm is proposed to monitor changes in signal spatial distribution. By doing this, TWPad can capture the disturbance in the spatial distribution of multipath signals caused by the moving or stationary human and realize detection under the TTW scenario. The experimental evaluation shows that the TWPad’s F1-measure of stationary human detection can reach about 0.975 and 0.967, under the TTW scenario of glass and brick wall, respectively, outperforming the state-of-the-art solutions and shedding promising lights on ubiquitous human detection in practice.

Indoor localization using WiFi signal has received great attentions since it is ubiquitous. So, in this paper we propose the design, implementation and evaluation of a real-time indoor localization system using commodity WiFi signal. The proposed system can localize the target by using Angle of Arrival (AOA) without any hardware modification and large localization latency. The contributions of this paper are follows. Firstly, an Angle estimation algorithm based on WiFi signals is proposed, which can quickly estimate AOA of Line of Sight (LOS) path in the case of fewer antennas and packets, ensuring real-time localization. Secondly, the influence of multipath signals on the energy spectrum of direct signals is analyzed by using the IEEE 802.11 Saleh-Valenzuela (S-V) channel model. Then, in order to improve localization accuracy, we propose a method of antenna selection. Finally, to deliver a real-time localization system we realize the proposed system by developing a software framework including a localization sever and a web-based location displayer. We have implemented the system on the commodity WiFi APs and the experimental results show that it can achieve accurate localization and has less the time cost.

Since that phase error exists in Channel State Information (CSI) measured by commodity WiFi device, the existing device-free tracking systems, based on Doppler extracted from CSI, suffer from the ambiguity of moving direction, which results in tracking error. Therefore, in this paper, we propose the design and evaluation of a device-free tracking system to solve this challenge. The contributions of this paper are listed as follows. Firstly, we creatively propose to use signal fading phenomenon, which is considered to be enemy to communication system, to estimate moving direction of the target. Secondly, we introduce ray tracing model to analyze the influence of moving target on received signal power and then build the profiles of received power of two cases, including the target moving towards and away from the link between transmitter and receiver. Thirdly, we rely on short-time Fourier transform (STFT) to process CSI, and then extract the power of Doppler in a time window. Then, Derivative Dynamic Time Warping (DDTW) is utilized to identify the moving direction. Consequently, the target can be tracked by moving speed and direction. We implement the proposed system on the commodity WiFi device and the experimental results show that it can achieve median tracking error 0.84m.

Device-Free Localization and Tracking (DFLT) acts as a key component for the contactless awareness applications such as elderly care and home security. However, the random phase errors in WiFi signal and weak target echoes submerged in background clutter signals are mainly obstacles for current DFLT systems. In this paper, we propose the design and implementation of MuTrack, a multiparameter based DFLT system using commodity WiFi devices with a single link. Firstly, we select an antenna with maximum reliability index as the reference antenna for signal sanitization in which the conjugate operation removes the random phase errors. Secondly, we design a multi-dimensional parameters estimator and then refine path parameters by optimizing the complete data of path components. Finally, the Hungarian Kalman Filter based tracking method is proposed to derive accurate locations from low-resolution parameter estimates. We extensively validate the proposed system in typical indoor environment and these experimental results show that MuTrack can achieve high tracking accuracy with the mean error of 0.82 m using only a single link.

Multipath signals, which are suppressed in conventional localization algorithms, usually relate target location and structure of indoor environment through geometry parameters such as Angle of arrival (AOA) and Time of Flight (TOF). Thus, they can be exploited to locate the target and construct maps, which describe the structure topology of indoor environment. In this paper, we propose WalkAround, a multipath-assisted indoor localization and mapping system using the commodity WiFi signals, which contain phase errors caused by imperfect hardware and non-synchronized clocks. To realize accurate localization without interference of phase errors, we firstly construct a geometry model for jointly estimating the locations of target and scatterers which can be regarded as objects such as wall and furniture, by using TOF differences between the reflection paths and direct path. Then, with the help of AOAs we develop a locations searching algorithm based on Particle Swarm optimization (PSO). After that, we propose a density-based mapping algorithm with the locations of the scatterers and target, which does not need any anchor nodes or landmarks. We have implemented WalkAround in actual indoor environments by using the commodity WiFi devices. Based on the experiment results, the median location error of the target is 1. 49m and the constructed map matches the real structure topology of indoor environment well using only one receiver.

In indoor environment, multipath signals are rich and contain indoor geometry information, which can be used to locate targets. Based on this, a multipath-assisted indoor localization algorithm is proposed, which is different from the conventional localization algorithm regarding multipath signal as enemy. Firstly, differential Time of Flight (ToF) of multipath signals are used to construct the fitness function of Particle Swarm Optimization (PSO) with respect to the locations of target and scatterers. Then, PSO is used to jointly estimate the locations of target and scatterers, in which the AoAs of target and scatterers are used to determine location ranges. Secondly, to improve localization accuracy further, we propose a novel detection algorithm of bad scatterers based on the mutually exclusive characteristic manifested by the correct and wrong locations of scatterers. Then, Affine Propagation Clustering (APC) is used for all target locations estimated by scatterers to determine if the result of PSO is acceptable with the proposed criterion.

Being essential for many emerging applications, the device-free localization systems have gained increasing interest, of which the through-the-wall device-free localization is of great challenge. This paper presents the design and implementation of TWPalo, a through-the-wall device-free localization system based on Wi-Fi channel state information (CSI). To this end, we first develop an algorithm for three dimensional joint estimation of angle of arrival (AoA), time of flight (ToF) and Doppler frequency shift (DFS). Combining with this algorithm, we then separate the CSI and obtain the parameters of each propagation path through the iteration of parameter estimation, channel reconstruction and cancellation. At last, the human induced reflection is found out and its relevant parameters are translated into the precise location of the human behind the wall. Our implementation and evaluation on commodity Wi-Fi devices demonstrate that TWPalo is better than existing systems in the form of AoA estimation and localization accuracy under the through-the-wall scenario.

Recently, hand gesture detection (HGD) system have become increasingly interesting to researchers in the field of human-computer interfaces. However, the traditional HGD has low robustness and detection accuracy, as well as privacy protection problem. Therefore, we present Rammar, a residual attention module (RAM) assisted Mask R-CNN, for frequency modulated continuous wave (FMCW) sensor based on HGD system. Firstly, by analyzing the time domain and frequency domain of the FMCW sensor signal, the three-dimensional feature maps of Range-Time-Map (RTM), Doppler-Time-Map (DTM) and Angle-Time-Map (ATM) of each hand gesture are obtained, respectively, avoiding insufficient information of single dimension parameter. Secondly, RTM, DTM and ATM images of each hand gesture are simultaneously sent to Rammar for training. To focus on the features of gesture, RAM in Rammar employs average-pooling and max-pooling to extract time and spatial features. Finally, the extracted three-dimensional feature maps are merged in the fully connected layer. The experimental results show that Rammar not only makes the average detection accuracy of the hand gestures to 98.1%(increased by 5%), but also reduces the detection time effectively.

Due to the rapid development of indoor Unmanned Aerial Vehicles (UAVs) in recent years, the localization of indoor UAVs has become a focus of attention in UAVs applications. Among them, the Wireless Local Area Network (WLAN) based localization approach has become an effective means to achieve indoor localization due to the widely-deployed WLAN infrastructure. At the same time, with the increased use of WLAN module in the state-of-the-art mobile devices, various types of mobile WLAN Access Points (APs) exist in indoor environment. In this circumstance, the mobile WLAN APs deteriorates localization accuracy since their associated Received Signal Strength (RSS) data become unstable with the variation of locations. To address this problem, a new approach based on the Density-based Spatial Clustering of Applications with Noise (DBSCAN) is proposed to detect mobile WLAN APs for manifold alignment localization of UAVs. Specifically, first of all, the DBSCAN is conducted at the Reference Points (RPs) on motion paths to detect mobile APs. Second, the RSS data from mobile APs are removed from the database to enhance the location-dependency of RSS data used for the localization. Third, the concept of augmentation process is considered in manifold alignment to achieve satisfactory localization accuracy. Finally, the extensive experimental results show that the proposed system performs better in localization accuracy compared with the existing CIMLoc and WILL under the presence of mobile WLAN APs.

The Internet of Things (IoT) has gradually changed the way of people’s lives due to its ability of connecting everything together, and meanwhile the accurate location sensing plays a crucial role in achieving this goal. Up to now, as one of the most representative outdoor localization systems, the global positioning system has been widely used, but its performance may be dramatically declined in indoor environment due to the serious multipath effect and signal attenuation caused by the complicated indoor structure. At the same time, the location fingerprint-based localization approach has become a popular one in indoor environment, and meanwhile the corresponding calibrated signal simplification in location database construction has been primarily considered due to its significant practical meaning in avoiding the blind signal sampling. In this paper, we propose to use an information-theoretic lens to construct the energy-efficient location fingerprint database for the localization in IoT. Interestingly, by analyzing the information loss in signal sampling, we analogize the database construction process into the information propagation process in a lossy channel, and then formulate the relations of sample capacity and localization error from an information-theoretic view. After that, by selecting an appropriate time interval to sample the independent and nonredundant signal, the minimum number of sampled signal under the given expected localization accuracy is determined. Finally, the extensive experimental results show that compared with the state-of-the-art approaches, the proposed one can effectively simplify the calibrated data for the energy-efficient location database construction in different wireless localization networks.

To facilitate green and secure communications, the energy efficiency (EE) and physical (PHY) layer security are desirable for cloud radio access network (C-RAN). However, the problem of EE optimization with guaranteed PHY layer security in C-RAN is challenging, especially in the presence of channel state information (CSI) uncertainty of both information receivers (IRs) and eavesdropper receivers (ERs). Thereby, in this paper, we investigate the worst-case EE maximization problem in a downlink C-RAN, subject to limited power budget of each BS and infinite number of PHY layer security constraints, which is a non-convex fractional programming problem and is NP-hard even in the event of perfect CSI. To solve this non-trivial problem, a fast-converging algorithm is developed. Specifically, based on successive convex approximation (SCA) technique, we transform the original problem into a semi-definite program (SDP) one, allowing for solving it iteratively. The tightness of the SDR is proved, and the SCA algorithm is also proved to converge to a Karush- Kuhn-Tucker point. Extensive simulations are carried out to verify the effectiveness of the proposed algorithm.

In this paper, we present a Real-time Interacting with Hand Description system via Millimeter-wave Sensor (Riddle) for human-computer interaction. Firstly, we describe a new approach to developing a radar-based system. When hand motions are captured by millimeter- wave radar sensor, the unique range information can be observed in the spectrogram. Compared to traditional hand gesture recognition systems based on optical sensors, the radar-based system avoids the influence of ambient light conditions. Secondly, we employ deep neural networks combined with connectionist temporal classification algorithm to recognize diverse hand gestures in real-time. Besides, we visualize the feature maps extracted from different layers to understand the deep neural networks. The deep neural networks are powerful to extract hand gesture features as well as class boundaries through a training process. Finally, we demonstrate that Riddle is capable of detecting six hand gestures and achieving high recognition accuracy of 96%.

The rapid development of ubiquitous and high-speed wireless communication technology has driven the increasingly serious demand for the Location-based Services (LBSs). In this circumstance, we propose a new crowd-sourced calibration-free and inertial sensor- independent indoor pedestrian localization approach, namely Mann-Whitney rank-sum testing via segments labeling (Marvel). In concrete terms, first of all, the motion paths are modeled by using the A* algorithm with the floor plan provided by the merchant, and then each motion path is segmented according to the preset expected localization accuracy. Second, by setting the signal similarity threshold, the Received Signal Strength (RSS) sequences which are collected by the human subjects following their daily routines in target environment are also segmented. Third, the proposed Marvel is adopted to cluster the motion path segments as well as RSS sequence segments respectively to construct the physical and signal logic graphs. Finally, by using the concept of backbone nodes diffusion mapping to establish the mapping relations between the physical and signal spaces, the pedestrian localization and the related motion analysis are conducted by the server. Furthermore, the extensive experimental results show that the proposed approach is capable of achieving higher localization accuracy compared with the current state-of-the-art approaches.

The Internet of Thing (IoT) based applications can provide various services and be widely applied in intelligent home. With the tendency of house safety protection, the detecting accuracy and privacy of intrusion detection system, which detects the human motion in indoor environment, has become a continuing concern. Up to now, there are emerging many intrusion detection systems which employ different devices such as camera and infrared. However, the poor privacy and deployment of specialized devices are mainly disadvantages of the afore-mentioned systems for deploying in home environment. In this paper, we propose WLID, a whole-home level intrusion detection system based on RSSI (Received Signal Strength Indicator) measurements of WiFi in indoor complex environment. In order to expand the area of human presence detection, WLID cooperates with WiFi-enabled IoT devices such as smart TV, air conditioner and other smart devices. The detection system constructs a detection algorithm with the non-parametric statistical method by only using RSSI and realize whole-home level real-time detection by using software implementation. The experimental results show that WLID can achieve the consistent detection rate close to 100% in a practical home environment.

To address low efficiency of traditional fingerprint database construction approach, we propose a fingerprint database expansion approach based on Multiquadric Radial Basis Function (RBF) interpolation. First of all, multi-directional fingerprints are collected dynamically, and sparse fingerprint database is generated by combining Received Signal Strength (RSS) and coordinates. Subsequently, RSS of each new Reference Point (RP) is estimated by using optimized RBF approach. In particular, Genetic Algorithm (GA) is applied to optimize shape parameter, so as to improve interpolation accuracy. Extensive experimental results show that the proposed approach is able to achieve high localization accuracy as well as significantly reduce fingerprint database construction effort.

The indoor localization systems based on wireless local area network received signal strength (RSS) have been widely applied due to the simplicity of system deployment as well as easy implementation on various mobile devices like the smartphones. However, they are often suffered by the major drawback of the extensive effort for location fingerprinting which is significantly labor-intensive and time-consuming. In response to this compelling problem, we design an improved manifold alignment approach to construct a cost-efficient radio map which consists of the sparsely collected location fingerprints and crowdsourcing RSS data with the purpose of reducing the overall fingerprints calibration effort. A new graph construction scheme which is proved to be the optimal choice to model the smoothness assumption in semi-supervised learning is proposed to explore the informativeness conveyed by location fingerprints during the process of radio map construction. In addition, the concept of execution characteristic function is considered to minimize the RSS sample capacity at each reference point to reduce fingerprints calibration effort further. Finally, the extensive experimental results demonstrate the performance improvement by the proposed system with the probability of localization errors within 3 m, 79.60%, which is at most 26.30 percentages higher than the one by the existing systems using location fingerprints solely.

Knowing whether the propagation path between transmitter and receiver is Line-Of-Sight (LOS) or No-Line-Of-Sight (NLOS) propagation is a important factor for improving the performance of communication services and vast of mobile computing applications. Several promising systems in the current commodity WiFi networks analyze frequency- dependent amplitude and phase of Channel State Information (CSI) to achieve a LOS/NLOS recognition scheme, but robustness not be considered enough since there are many frequently-used wireless channels. With the development of Multiple-Input- Multiple-Output (MIMO), the spatial properties of channel can be obtained effortlessly. Consequently, it provides a potential to utilize the unchanged spatial information of multipath signals to get a robustness LOS recognition system. Here, we propose Wi-vision, an accurate and robust LOS recognition system based on Single-Input-Multiple-Output (SIMO) measurements in indoor environment. We creatively introduce the Hopkins statistic to measure the distribution of Angle-Of-Arrival (AOA) and relative Time-Of-Flight (TOF) of multipath under LOS and NLOS condition, respectively. The test results show that Wi-vision possesses consistent LOS and NLOS detection rate of above 91% and 87% respectively with different system configurations.

The indoor positioning system based on Micro Electro Mechanical Systems (MEMS) sensors is featured with short-term high accuracy, whose current positioning performance depends on the historical positioning result. Therefore, MEMS positioning has long-time error accumulation. The fingerprint positioning of Bluetooth Low Energy (BLE) is independent of accumulative error, but there is an irregular jump error in the positioning result, which limits the positioning accuracy. Furthermore, the actual commercial positioning systems generally require the consecutive positioning in multi-floor environment. Based on this, this paper proposes a data fusion algorithm based on BLE and MEMS for indoor cross-floor positioning. Firstly, we denoise the fingerprint database by clustering, outlier detection, and filtering algorithms. Then, the extended Kalman filter is employed to complete the optimal estimation of the two-dimensional target position according to the robust M estimation. Finally, the barometer and geographical position information are used to achieve the height estimation of the target. This paper also carries out a large number of engineering verification. The experimental results show that the algorithm can suppress the cumulative error effectively caused by low-cost MEMS sensors, and solve the problem of irregular jump error caused by Received Signal Strength Indicator (RSSI) jitter. In the indoor multi-layer environment, the proposed system achieves the horizontal and vertical positioning Root Mean Square (RMS) errors less than 0.9 m and 0.35 m respectively. In addition, we have verified the stability of the designed system through the long-time test.

With the rapid development of Wireless Local Area Network (WLAN) technique, the indoor WLAN localization has caught significant attention. In this paper, a novel indoor WLAN localization approach by using the high-dimensional manifold alignment with limited calibration load is proposed. Different from the conventional dimension-reduction based manifold alignment approach which preserves a limited part of the Received Signal Strength (RSS) data information, we first construct an innovative objective function from the augmented physical locations and the corresponding RSS data. Second, the closed-form solution to the objective function is obtained by applying the Lagrange multiplier approach. Finally, the target location is estimated at the closest point in the manifold. Furthermore, we present some preliminary analysis towards the generalization of the proposed objective function to the scenario with multiple types of measurements used for the localization. The extensive analytical and experimental results demonstrate that the performance of the proposed approach is well with limited calibration load and can be further improved by using more calibrated locations with known RSS data.

Device-free Passive (DfP) localization is an emerging technology that uses the widely deployed wireless networks to detect and localize the people and other entities in target environment. The existing DfP localization systems realize the detection and localization under the WLAN indoor environment, but the low transmission power of the Access Points (APs) restricts their application. In this paper, we propose a novel DfP motion detection system based on the cellular network with the purpose of achieving the accurate, robust, low-overhead, and long-distant motion detection capability. To overcome the poor detection performance resulted from the time-varying signal, the signal strength difference at different timestamp is adopted as the characteristic parameter of the system. We apply the non-parametric kernel density estimation technique to calculate the optimum detection threshold of each signal stream. Furthermore, a joint detection mechanism is introduced to reduce the impact of noisy readings, as well as enhance the detection performance of the system using the cellular network. The results in two typical test beds show that the proposed system can achieve high detection accuracy with false negative (FN) rate 0.8% and false positive (FP) rate 4.6%, while require significantly lower deployment overhead and be more robust to the environmental changes compared with the WLAN DfP detection system.

Access Point (AP) optimization is one of the most important components in indoor Wireless Local Area Network (WLAN) localization technique since the AP number and locations have significant impact on the variations of Received Signal Strength (RSS) in target environment. Different from the conventional AP optimization approaches, we propose to use the concept of adaptive channel power allocation to construct a water-filling model, and then conduct AP optimization based on the weights of candidate AP locations which are calculated by the fast discrete water-filling algorithm. The experimental results demonstrate that the proposed approach is able to achieve high localization precision, and meanwhile consume low time overhead.

Multi-path fading, environmental shadowing and channel interference always result in the significant temporal and spatial variations of Received Signal Strength (RSS), and eventually lead to the low accuracy in Wireless Local Area Networks (WLAN) fingerprint based indoor localization. Motivated by this, we focus on deriving out the positioning error bound which can be applied to characterize the theoretical relationship between the positioning errors and signal distributions by using Fisher Information Matrix (FIM). Furthermore, the positioning error bound is recognized as an effective criterion of designing a more beneficial WLAN deployment with higher positioning accuracy. Extensive simulations are conducted in a regular Lineof-sight (LOS) environment as well as in a complex irregular Non-line-of-sight (NLOS) environment.

In recent decade, the Received Signal Strength (RSS) based indoor localization has caught significant attention, but it always suffers from the time-consuming and labor intensive fingerprint calibration. At the same time, the Simultaneous Localization and Mapping (SLAM) technique is considered with the low time and laboring cost, whereas the dedicated hardware is often required. To solve these problems, a novel indoor WLAN SLAM approach by using the Edge Detection based Gene Sequencing (EDGES) is proposed. First of all, a batch of RSS sequences is sporadically collected in target area. Second, the spectral clustering is conducted on RSS sequences to construct the cluster graphs, and then the EDGES approach is applied to assemble the cluster graphs into a logic graph. Finally, the mapping from the logic graph into ground-truth graph is established to realize indoor WLAN SLAM. The extensive experimental results prove that the proposed approach can achieve satisfying localization accuracy without site survey of location fingerprinting or motion sensing.

In the area of Wireless Local Area Network (WLAN) based indoor localization, the Received Signal Strength (RSS) fingerprinting based localization technique has been studied extensively. Site survey phase in RSS fingerprinting is always considered to be time-consuming and labor intensive. To solve this problem, we propose a novel Indoor Mapping and Localization Using RSS Solely (IMLours) approach, which utilizes the spectral clustered time-stamped WLAN RSS data to characterize environmental layout, as well as conduct target localization. First of all, we use the off-the-shelf smartphones to sporadically record a batch of WLAN RSS data in indoor environment. Second, spectral clustering is applied to classify the RSS data in each sequence into different clusters. The clusters are then used to construct the logic graphs. Third, we do the mapping from logic graphs into ground-truth graph. Finally, based on the extensive experiments conducted in a real WLAN indoor environment, our proposed IMLours approach is proved to achieve satisfactory localization accuracy.

In the area of Wireless Local Area Network (WLAN) based indoor localization, the Received Signal Strength (RSS) fingerprinting based localization technique has been s-tudied extensively. Site survey phase in RSS fingerprinting is always considered to be time-consuming and labor intensive. To solve this problem, we propose a novel Indoor Mapping and Localization Using RSS Solely (IMLours) approach, which utilizes the spectral clustered time-stamped WLAN RSS data to characterize environmental layout, as well as conduct target localization. First of all, we use the off-the-shelf smartphones to sporadically record a batch of WLAN RSS data in indoor environment Second, spectral clustering is applied to classify the RSS data in each sequence into different clusters. The clusters are then used to construct the logic graphs. Third, we do the mapping from logic graphs into ground-truth graph. Finally, based on the extensive experiments conducted in a real WLAN indoor environment, our proposed IMLours approach is proved to achieve satisfactory localization accuracy.

In this paper, we present a fingerprint-based particle filtering (PF) approach for the mobility tracking in cellular networks. With the popularity of location-based services (LBSs) in a recent decade, the mobility tracking has now become one of the indispensable techniques to fulfill the pervasive and location-aware computing. However, the tracking results from the conventional K nearest neighbors (KNN) and Kalman filtering (KF) by the cellular data appear to be coarse due to the reflection, refraction and diffraction of received signal code power (RSCP). Therefore, we propose the KNN/PF as an effective way to improve the stability and accuracy of mobility tracking in cellular networks. Furthermore, the experiments conducted in Pudong New District, Shanghai, China, show that the KNN/PF approach can achieve better cumulative density function (CDF) of tracking errors compared with the KNN and the mixed KNN and KF (KNN/KF) approaches.