Non-line-of-sight Research Articles

Measurement system for ultraviolet channel modeling and communications.

There has been an increasing interest in ultraviolet (UV) communications as a promising technology for non-line-of-sight (NLOS) networking by exploiting atmospheric scattering at UV wavelengths that enables a unique NLOS UV communication channel. While there has been significant theoretical and simulation-based investigation of the UV channel characteristics, there is limited work in terms of experimental research and validation of the analytical models. In this paper, we present a flexible experimental system for precise UV channel and communications measurements. Specifically, a transceiver system is developed that consists of a gimbal, UV light-emitting-diode array, and photomultiplier tube detector, node synchronization, and LabVIEW-based data acquisition subsystems. Novel techniques to precisely characterize the UV LED array radiation pattern, absolute transmit power, and field of view of the detector are also presented. The utility of the developed system is then demonstrated by performing a variety of experiments including UV channel model validation and steering optimization for UV communication links where the results were in very good agreement with theory and simulation.

Indoor Localization System With NLOS Mitigation Based on Self-Training

Location-awareness has become a fundamental requirement for multiple emerging applications with the rapid development of wireless technologies. The high-accuracy ranging enabled by ultra-wide bandwidth (UWB) signals is often deteriorated by clocks imperfections and non-line-of-sight (NLOS) propagation. Existing supervised learning methods for NLOS identification and mitigation are time-consuming, labor-intensive, and cost-inefficient due to the need for training data acquisition and label assignment. This paper presents an indoor localization system that enables NLOS mitigation based on self-training. The system provides a general information fusion framework that integrates map, inertial sensors, and UWB measurements, where the weak labels for UWB measurements are produced and iteratively refined by multi-sensory information fusion for self-training. In addition, the system utilizes the maximum likelihood ranging estimator that considers the impact of clock drift. The effectiveness of the proposed system is demonstrated via extensive experimentation in multiple real-world environments. In particular, the proposed methods reduce the NLOS ranging error by 80% after 8 iterations and result in a 90th localization error percentile of 0.5m in a complex indoor environment.

A Non-Line-of-Sight Mitigation Method for Indoor Ultra-Wideband Localization With Multiple Walls

Ultra-wideband (UWB) ranging techniques can provide accurate distance measurement in line-of-sight (LOS) conditions. However, various walls and obstacles in indoor non-line-of-sight (NLOS) environments, which obstruct the direct propagation of UWB signals, can generate significant ranging errors. Due to the complex through-wall UWB signal propagation, most conventional studies simplify the ranging error model by assuming that the incidence angle is zero or the relative permittivities for different walls are the same to improve the through-wall UWB localization performance. Considering walls are different in realistic settings, this paper presents a through-multiple-wall NLOS mitigation method for UWB indoor positioning. First, spatial geometric equilibrium equations of UWB through-wall propagation and a numerical method are developed for the precise modeling of UWB through-wall ranging errors. Then, calculated error maps are determined numerically without field measurements. Finally, the determined error maps are combined with a gray wolf optimization algorithm for localization. The proposed method is evaluated via field experiments with four rooms, three walls, and six penetration cases. The results demonstrate that the method can strongly mitigate the multi-wall NLOS effects on the performance of UWB positioning systems. This solution can reduce project costs and number of power supplies for UWB indoor positioning applications.

An Adaptive IMU/UWB Fusion Method for NLOS Indoor Positioning and Navigation

Indoor positioning system (IPS) plays an important role in the applications of Internet of Things (IoT), including intelligent hospital, logistics, and warehousing. Ultra-wideband (UWB) based IPS has shown superior performance due to its strong multipath resistance and high temporal resolution. However, the non-line-of-sight (NLOS) situations noticeably degrade both the positioning accuracy and the communication reliability. To address this issue, we first propose a support vector machine (SVM) based channel detection method to distinguish the line-of-sight (LOS) and NLOS conditions. Then, one base station (BS) based distance and angle positioning algorithm with extended Kalman filter (DAPA-EKF) in NLOS environment is proposed. For the LOS environment, least squares (LSs) with EKF processing of acceleration (LS-AEKF) and velocity (LS-VEKF) are developed. To further improve the performance, the combination of time difference of arrival (TDOA) and KF in LOS environment is proposed. Simulation results show that the positioning accuracy of the proposed algorithm is improved in various environments. Finally, validated using more than 1000 testing positions, the positioning accuracy of LS-AEKF is 73.8% to 74.1% higher than that of LS-VEKF among the two proposed algorithms in terms of 3 or 4 BSs metrics.

Analysis and Optimization of a Double-IRS Cooperatively Assisted System With a Quasi-Static Phase Shift Design

The analysis and optimization of single intelligent reflecting surface (IRS)-assisted systems have been extensively studied, whereas little is known regarding multiple-IRS-assisted systems. This paper investigates the analysis and optimization of a double-IRS cooperatively assisted downlink system (D-IRS-C), where a multi-antenna base station (BS) serves a single-antenna user with the help of two multi-element IRSs, connected by an inter-IRS channel. The channel between any two nodes is modeled with Rician fading. The BS adopts the instantaneous CSI-adaptive maximum-ratio transmission (MRT) beamformer, and the two IRSs adopt a cooperative quasi-static phase shift design. The goal is to maximize the average achievable rate, which can be reflected by the average channel power of the equivalent channel between the BS and user at low channel estimation and phase adjustment costs and computational complexity. First, we obtain tractable expressions of the average channel power of the equivalent channel in the general (Rician factor), pure line of sight (LoS), and pure non-line of sight (NLoS) regimes, respectively. Then, we jointly optimize the phase shifts of the two IRSs to maximize the average channel power of the equivalent channel in these regimes. The optimization problems are challenging non-convex problems. We obtain globally optimal closed-form solutions for some cases and propose computationally efficient iterative algorithms to obtain stationary points for the other cases. Next, we compare the computational complexity for optimizing the phase shifts and the optimal average channel power of D-IRS-C with those of a counterpart double-IRS non-cooperatively assisted system (D-IRS-NC) and a counterpart single-IRS-assisted system (S-IRS) at a large number of reflecting elements in the three regimes. Finally, we numerically demonstrate notable gains of the proposed solutions over the existing solutions at different system parameters. To our knowledge, this is the first work that optimizes the quasi-static phase shift design of D-IRS-C and characterizes its advantages over the optimal quasi-static phase shift design of the counterpart D-IRS-NC and S-IRS.

Cache-Enabled UAV Emergency Communication Networks: Performance Analysis With Stochastic Geometry

The unmanned aerial vehicles (UAVs) are able to assist damaged cellular networks as aerial base stations (ABSs) due to their flexibility and affordability. This paper investigates the cache-enabled UAV emergency communication networks in the post-disaster area using stochastic geometry approaches. We propose a cache-enabled UAV emergency communication network framework in which a limited number of ABSs are deployed overlaid the partially destructed cellular network. The ABSs are equipped with cache units that adopt probabilistic caching strategy. We design a content-centric association strategy within the specified circular cache area by comprehensively considering the cache status and channel conditions. Based on the framework, we derive the exact expressions of the offloading probability and average rate under the general channel model taking line-of-sight (LOS)/ non-line-of-sight (NLoS) path loss and Nakagami-m fading into account. Simulation results demonstrate that there exist optimal values for ABSs number, ABSs altitude, and cache serving radius to maximize the offloading probability and average rate. Moreover, increasing ABSs number and the cache serving radius in the case that ABSs are deployed at the relatively low altitude is beneficial to improve the system performance. A large number of ABSs helps to reduce the optimal cache serving radius for maximizing the system performance.

Asynchronous Hyperbolic UWB Source-Localization and Self-Localization for Indoor Tracking and Navigation

Hyperbolic localization measures the time difference of arrivals (TDOAs) of signals to determine the location of a wireless source or receiver. Traditional methods depend on precise clock synchronization between nodes so that time measurements from independent devices can be meaningfully compared. Imperfect synchronization is often the dominant source of error. We propose two new message based TDOA equations for hyperbolic localization which require no synchronization and meet or exceed state-of-the-art accuracy. Our approaches leverage anchor nodes that observe each other’s packet arrival times and a novel reformulation of the TDOA equation to reduce the effect of clock drift error. Closed-form equations are derived for computing TDOA in both self-localization and source-localization modes of operation along with bounds on maximum clock drift error. Three experiments are performed including a clock drift simulation, a non-line-of-sight (NLOS) simulation, and an indoor validation experiment on custom ultra wideband (UWB) hardware all of which involved eight anchor nodes and one localizing node in a 128m3 capture volume. Our source-localization approach achieved unprecedented accuracy with lower cost equipment and trivial setup. Our self-localization matched state-of-the art accuracy but with infinite scalability and high privacy. These results could enable economical and infinite density indoor navigation and dramatically reduce the economic cost and increase the accuracy of implementing industrial and commercial tracking applications.

Modeling blockages and link dependency in millimeter wave communication networks using Matérn Hard-Core Point Process

With the pursuit of higher transmission rate, millimeter wave (mmWave) communication has become one of the propelling technologies in wireless communications. Due to its short wavelength, a mmWave signal suffers from significant path loss and is heavily affected by obstructions and reflections caused by blockages. Moreover, the interference links are dependent as the locations of base stations are not independent in practical deployment. And the link dependency in the densely deployed mmWave communication networks cannot be ignored, which makes coverage analysis even more complicated. In this paper, we investigate the coverage model incorporating the Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) links of both signal and interference, to comprehensively portray the performance of mmWave networks with randomly distributed blockages. Further, we capture the dependency between interfering links with the copula function to model the received aggregate interference more accurately. The probability density function of the received power, Signal-to-Interference-plus-Noise Ratio (SINR), and outage probability are characterized to theoretically quantify the impact of blockages and interference. The proposed analytical model perfectly models the impact of blockages and link dependency, and obtains the cumulative distribution function (CDF) of the SINR with a gap less than 0.2% compared to the Monte-Carlo simulation.

Ultraviolet communication with a large scattering angle via artificial agglomerate fog.

Considering strong signal attenuation of the large-angle non-line-of-sight (NLOS) link achieved due to the ultraviolet (UV) scattering properties, we propose to increase the UV communication link gain under a large scattering angle via generating agglomerate fog within a certain range as a secondary light source. In this study, a channel model with locally strong scatterers from agglomerate fog is proposed based on Monte Carlo ray-tracing approaches. Mie theory is adopted to calculate the atmospheric channel parameters, to further evaluate the link gain of a channel under non-uniform atmosphere. The performance of scattering system in the presence of fog conditions depends on the relative positions of the light source to the fog and to the receiver. The link gain results reveal the transmission capabilities for different scattering channel geometries, and give the optimal spray point location within a 10 m communication range. We further establish a foggy NLOS system using an agglomerate fog generator to verify our research in the real environment. The results show that the received signal strength of the NLOS link can be effectively enhanced by constructing scatterers in the atmospheric channel, which significantly decreases the bit-error rate (BER).

An Improved UWB/IMU Tightly Coupled Positioning Algorithm Study.

The combination of ultra-wide band (UWB) and inertial measurement unit (IMU) positioning is subject to random errors and non-line-of-sight errors, and in this paper, an improved positioning strategy is proposed to address this problem. The Kalman filter (KF) is used to pre-process the original UWB measurements, suppressing the effect of range mutation values of UWB on combined positioning, and the extended Kalman filter (EKF) is used to fuse the UWB measurements with the IMU measurements, with the difference between the two measurements used as the measurement information. The non-line-of-sight (NLOS) measurement information is also used. The optimal estimate is obtained by adjusting the system measurement noise covariance matrix in real time, according to the judgment result, and suppressing the interference of non-line-of-sight factors. The optimal estimate of the current state is fed back to the UWB range value in the next state, and the range value is dynamically adjusted after one-dimensional filtering pre-processing. Compared with conventional tightly coupled positioning, the positioning accuracy of the method in this paper is improved by 46.15% in the field experimental positioning results.

NLOS node localization for improving warning message distribution in VANETS

SummaryVehicular Ad‐hoc Network (VANET) is a vehicle‐to‐vehicle communication technology, which is highly promising due to its continuous demand, applicability, and suitability for public safety. The vehicular nodes establish communication with each other for exchanging traffic data and road maps, especially when warning messages need to be broadcasted on the network during emergency situations. Therefore, accurate vehicle information is necessary in such circumstances to improve the accuracy of Non‐Line‐of‐Sight (NLOS) node localization. In this paper, a Driving Training‐Based Optimization for NLOS Node Localization (DTO‐NL) Scheme is proposed for facilitating better warning message delivery during emergency situations in vehicular networks through a dynamic increase and decrease in the search based on the position of unknown NLOS nodes from the considered anchor nodes employed for localization. The performance of the suggested technique has been analyzed based on parameters like warning message delivery rate, latency, localization error, channel utilization rate, energy consumption, and packet delivery ratio. The proposed DTO‐NL scheme enhances the warning message delivery rate by 9.81%, 11.28%, 14.38%, and 17.64% compared to WIDVBA , ROA‐APS , ILL‐WOA‐NLOS , and WDHPBDS techniques .

Non-line-of-sight snapshots and background mapping with an active corner camera

The ability to form reconstructions beyond line-of-sight view could be transformative in a variety of fields, including search and rescue, autonomous vehicle navigation, and reconnaissance. Most existing active non-line-of-sight (NLOS) imaging methods use data collection steps in which a pulsed laser is directed at several points on a relay surface, one at a time. The prevailing approaches include raster scanning of a rectangular grid on a vertical wall opposite the volume of interest to generate a collection of confocal measurements. These and a recent method that uses a horizontal relay surface are inherently limited by the need for laser scanning. Methods that avoid laser scanning to operate in a snapshot mode are limited to treating the hidden scene of interest as one or two point targets. In this work, based on more complete optical response modeling yet still without multiple illumination positions, we demonstrate accurate reconstructions of foreground objects while also introducing the capability of mapping the stationary scenery behind moving objects. The ability to count, localize, and characterize the sizes of hidden objects, combined with mapping of the stationary hidden scene, could greatly improve indoor situational awareness in a variety of applications.

Non-line-of-sight imaging and location determination using deep learning

Non-line-of-sight (NLOS) imaging methods have been proposed and rapidly developed to address the challenge of detecting objects hidden from the direct line of sight. One critical issue in NLOS imaging is acquiring the spatial position of hidden objects. Active NLOS imaging methods solve this problem using complex designs and sophisticated equipment while the conventional passive NLOS imaging scheme, which does not have a controllable light source and time-gating detector, rarely captures the spatial information of objects. Using speckle patterns and the assistance of a deep learning method, we experimentally demonstrated that the passive NLOS imaging system could simultaneously visualize and locate objects hidden in corners like an active NLOS imaging system could. High-fidelity reconstructed images and high-accuracy location recognition were realized using the proposed network. The network could provide 99% recognition accuracy for the object's position with an axis spacing of 36 μm. These results were significant for NLOS imaging with high-accuracy positioning requirements.

Robust localization under NLOS environment in the presence of isolated outliers by full-Set TDOA measurements

Different from the traditional approach of using the reduced set of time-difference-of-arrival (TDOA) measurements, localization by the full TDOA set is discussed here. It avoids choosing a specific reference sensor and exploits more measurements, conferring robustness against outliers. Nevertheless, the none-line-of-sight (NLOS) propagation still presents challenges for the existing full-set positioning algorithms. In this work, we develop an iterative scheme that utilizes the full TDOA measurements for localization capable of simultaneously mitigating the influence of outliers and NLOS-path measurements. The proposed scheme consists of the localization and identification modules, and they are designed with low complexity to meet real-time positioning demands. The theoretical optimal TDOA methods using full-set measurements are proposed in the localization module. Besides, to identify the NLOS measurements, the identification module adopts a greedy search-based random sample consensus (GS-RSC) algorithm. It can work cooperatively with the proposed lightweight localization module, returning a set free of outliers, NLOS measurements, and an accurate source location estimate. The theoretical analysis and experiments illustrate the superiority of the proposed scheme over the state-of-the-art TDOA full-set algorithms for handling outliers and NLOS measurements in localization.

Non-Line-of-Sight Detection Based on Neuromorphic Time-of-Flight Sensing

Non-line-of-sight (NLOS) detection and ranging aim to identify hidden objects by sensing indirect light reflections. Although numerous computational methods have been proposed for NLOS detection and imaging, the post-signal processing required by peripheral circuits remains complex. One possible solution for simplifying NLOS detection and ranging involves the use of neuromorphic devices, such as memristors, which have intrinsic resistive-switching capabilities and can store spatiotemporal information. In this study, we employed the memristive spike-timing-dependent plasticity learning rule to program the time-of-flight (ToF) depth information directly into a memristor medium. By coupling the transmitted signal from the source with the photocurrent from the target object into a single memristor unit, we were able to induce a tunable programming pulse based on the time interval between the two signals that were superimposed. Here, this neuromorphic ToF principle is employed to detect and range NLOS objects without requiring complex peripheral circuitry to process raw signals. We experimentally demonstrated the effectiveness of the neuromorphic ToF principle by integrating a HfO2 memristor and an avalanche photodiode to detect NLOS objects in multiple directions. This technology has potential applications in various fields, such as automotive navigation, machine learning, and biomedical engineering.

Imaging operator in indirect imaging correlography.

Indirect imaging correlography (IIC) is a coherent imaging technique that provides access to the autocorrelation of the albedo of objects obscured from line-of-sight. This technique is used to recover sub-mm resolution images of obscured objects at large standoffs in non-line-of-sight (NLOS) imaging. However, predicting the exact resolving power of IIC in any given NLOS scene is complicated by the interplay between several factors, including object position and pose. This work puts forth a mathematical model for the imaging operator in IIC to accurately predict the images of objects in NLOS imaging scenes. Using the imaging operator, expressions for the spatial resolution as a function of scene parameters such as object position and pose are derived and validated experimentally. In addition, a self-supervised deep neural network framework to reconstruct images of objects from their autocorrelation is proposed. Using this framework, objects with ≈ 250 μ m features, located at 1 mt standoffs in an NLOS scene, are successfully reconstructed.

Adaptive LDPC coded high data rate based IR-UWB combined with VMIMO system for vehicular communication links

The automotive industry is developing significantly with the evolution of vehicular networks, intelligent transportation systems, and autonomous cars. These technologies are made achievable by recent advances in software, hardware, and communication systems, along with various applications and standardization efforts. On the other side, many impairments have existed due to these evolving, limiting the process of vehicular networks. So, this paper proposes an implementation of adaptive LDPC-coded, high data rate-based Impulse Radio-Ultra Wide Band (IR-UWB) combined with Virtual Multiple Input Multiple Output (VMIMO) systems for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) wireless communications. Furthermore, they are merged with orthogonal pulse shaping using a modified Hermite pulse. Besides, the paper compares Zero-Forcing (ZF) and Minimum Mean Square Error (MMSE) detection techniques. To enhance and mitigate the existing impairments facing the vehicular networks. The proposed system’s performance is evaluated using both techniques, utilizing Bit Error Rate (BER), processing time, and resultant throughput analysis for both channel models. The simulation results include Non-Line of Sight (NLoS) and Line of Sight (LoS) scenarios for both channel models. Moreover, the simulation considers the effect of path loss and noise on the transmitted signal. In addition, the recent LDPC decoding algorithm termed “Modified Implementation Efficient Reliability Ratio Weighted Bit Flipping” (MIERRWBF) is evaluated through all latter channel models. At last, a novel adaptive LDPC decoder is proposed to further enhance the performance of all vehicular communication links under study. Consequently, the paper proposed an integrated adaptive LDPC-coded high data rate communication system to enhance vehicular communications using VMIMO mixed with modified Hermite orthogonal pulse shaping and deploying the MMSE recommended detection technique.

Non-line-of-sight imaging with arbitrary illumination and detection pattern

Non-line-of-sight (NLOS) imaging aims at reconstructing targets obscured from the direct line of sight. Existing NLOS imaging algorithms require dense measurements at regular grid points in a large area of the relay surface, which severely hinders their availability to variable relay scenarios in practical applications such as robotic vision, autonomous driving, rescue operations and remote sensing. In this work, we propose a Bayesian framework for NLOS imaging without specific requirements on the spatial pattern of illumination and detection points. By introducing virtual confocal signals, we design a confocal complemented signal-object collaborative regularization (CC-SOCR) algorithm for high-quality reconstructions. Our approach is capable of reconstructing both the albedo and surface normal of the hidden objects with fine details under general relay settings. Moreover, with a regular relay surface, coarse rather than dense measurements are enough for our approach such that the acquisition time can be reduced significantly. As demonstrated in multiple experiments, the proposed framework substantially extends the application range of NLOS imaging.

Resilient Pseudorange Error Prediction and Correction for GNSS Positioning in Urban Areas

Positioning, navigation and timing (PNT) is essential for internet of things (IoT) communications and location-based services. Although GNSS can provide accurate PNT in open areas, obtaining reliable PNT is still a considerable technical challenge in complex urban environments. This is because the GNSS signals are more likely to be affected by multipath interference and non-line of sight (NLOS) reception issues arising from the obstructions and reflections in built environments. These introduce range measurement errors that degrade the GNSS positioning accuracy. This paper proposes two resilient pseudorange error prediction and correction strategies to improve the GNSS positioning accuracy in urban environments. In particular, considering the carrier-to-noise density (C/N0), satellite elevation angle and local positional information, the random forest based pseudorange error prediction and correction models are constructed in two variations, including: 1) the point-based correction (PBC), and 2) the grid-based correction (GBC). The final improved positioning solution is then calculated by using the least square method (LSM) of the corrected pseudoranges. Kinematic test results in urban environments show that both variations of the proposed model can improve the positioning accuracy by 42.9% and 40.8% in horizontal, and by 60.1% and 63.3% in 3D, respectively, compared to the positioning results obtained by traditional method without pseudorange error corrections. The improvements are 41.1% and 38.9% in horizontal, and 45.7% and 50.0% in 3D, respectively, compared with traditional elevation angle weighting method.

Stepped-Frequency Binary Offset Carrier Modulation for Global Navigation Satellite Systems

In this paper, we propose Stepped-Frequency Binary Offset Carrier (SFBOC) modulation for Global Navigation Satellite Systems (GNSS) that use spread spectrum signals. SFBOC modulation generates a Binary Offset Carrier (BOC) signal of which the subcarrier frequency varies stepwise within each period of the pseudorandom-noise (PRN) code. The generated SFBOC signal can have a sharp autocorrelation function (ACF) output and an almost flat spectrum over a wide frequency band. These characteristics result in correlation properties, multipath mitigation, and anti-interference that are superior to those of conventional GNSS modulations. To testify, we compare the performance of SFBOC in various aspects to conventional GNSS modulations and other time-varying subcarrier frequency (TVSF) modulations. From numerical simulations, we show that the proposed SFBOC has higher ranging accuracy, lower tracking ambiguity, and more robustness to noise, interference, and multipath than the conventional GNSS modulations. In addition, we demonstrate that the proposed SFBOC moulation has two advantages while other modulations with TVSF cannot have both; that SFBOC is robust to code-Doppler and that, when filterbank is employed, SFBOC improves the signal-to-noise ratio (SNR) significantly without any loss of weak early arrival path in the Non-line of sight (NLOS) multipath environments.