Signal Blockage Research Articles

Blockage Effects of Road Bridge on mmWave Channels for Intelligent Autonomous Vehicles

Vehicular communication and sensing technologies are key to enabling 6G Intelligent Autonomous Transportation Systems (IATS). With the introduction of massive sensors and artificial intelligence (AI) fusion applications, IATS is needed to support data transmission rates up to 10 Gb/s. Millimeter-wave (mmWave) technology has attracted extensive attention owing to abundant spectrum resources, which can support the timely transmission of massive data. However, performance degradation of mmWave due to signal blockage has become one of the critical technical challenges. Road bridges as one of the common obstacles in urban scenarios, which has severe blockage effects on communication links. Therefore, this paper comprehensively studies the impact of road bridge blockage effects on mmWave vehicle-to-infrastructure (V2I) links and proposes an empirical model that can accurately characterize the bridge blockage effect. First, we use a self-developed mmWave channel sounder to carry out channel measurements on typical urban roads. Measurement results indicate that a maximum extra propagation loss of up to 23 dB is caused by road bridges. In addition, to address the deficiencies of existing propagation prediction models, the Single Road Bridge (SRB) model is proposed in this work. This model reveals for the first time the extra propagation loss caused by the road bridge to the channel. Compared with existing models, the SRB model can make the mean absolute error (MAE) and root mean square error (RMSE) within 5 dB. The proposed SRB model is of great value for accurately simulating real-world road bridge blockage events when designing future IATS.

Efficient Exclusion Strategy of Shadowed RIS in Dynamic Indoor Programmable Wireless Environments

Recent efforts have promoted programmable wireless environments (PWEs) to enhance the reception quality in high-frequency bands via reconfigurable intelligent surfaces (RISs). However, relevant research efforts are limited to setups with stationary users. This paper shows that crowd mobility in indoor PWEs induces spatio-temporal shadows on the surfaces, resulting in spatio-temporal sparsity in channel gains due to signal blockages. This overlooked aspect impacts the operation strategy of PWEs as the shadowed RIS tiles would contribute to the overheads while offering almost no improvement to the reception quality. Hence, this paper proposes an optimal strategy that excludes the shadowed tiles, which maximizes the utilization efficiency of RISs while minimizing the overheads. Since signal blockage is tied with the details of user mobility, a general model does not exist to identify such shadowed tiles for exclusion. Hence, we follow a data-driven approach that capitalizes on a realistic indoor mobility model and ray-tracing to generate the channel data. However, conventional ray-tracing presents high complexity that hinders data generation. So, we propose an approach to identify the shadow regions with a nine-order of magnitude reduction in complexity to efficiently generate the channel data. Furthermore, we present two exclusion strategies that offer guaranteed and best-effort quality-of-service support, and each can identify the tiles to be excluded via a search method with a complexity of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O(N)</i> for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> tiles. The results indicate that the proposed strategies reduce the overheads by 45 - 50% while maintaining optimal service quality in various environments, operation frequencies, and user and access point density.

為什麼侵入性腦機介面技術是危險的

LANGUAGE NOTE | Document text in Chinese; abstract also in English.&#x0D; 我們反對侵入性腦機介面應用於人類，主要原因是，我們還幾乎不知道人的意識產生的機制，侵入性腦機介面技術有可能抹掉人的意識；除此之外，應用介面的人很容易被外人控制，失去自由。總之，它違反了以下三條“非對稱原則”：1. 從客體到主體這個方向，資訊越通暢越好，控制信號阻滯度越高越好。2. 從主體到客體這個方向，控制信號越暢通越好，資訊密封度越高越好。3. 以上兩條的鬆動調節，以最嚴苛的程式保證以各個主體為主導。作為人機介面的代替方案，我們開以採用作為“擴展心智＂的互聯網和電腦來實行。傳統上，道家哲學按其義理也為反對侵入性腦機介面技術提供了支援。&#x0D; People oppose the application of invasive brain–computer interfaces to humans, mainly because they still know little about the mechanism by which human consciousness is generated and such technology could erase human consciousness. In addition, people using brain–computer interfaces can easily be controlled by outsiders and lose their freedom. In other words, brain–computer interfaces violate the following three asymmetry principles. (1) From the object to the subject, the smoother the information flow, the better, and the higher the degree of control signal blockage, the better. (2) From the subject to the object, the smoother the control signal flow, the better, and the higher the degree of information sealing, the better. (3) The relaxation of the above two principles should be carried out using the most stringent procedure to ensure that each subject takes the lead. As a substitute for brain–computer interfaces, we can use the Internet and computers as a kind of “extended mind.” The principles of traditional Taoist philosophy also support opposition to invasive brain–computer interfaces.

A robust GNSS sensors in presence of signal blockage for USV application

Unmanned surface vehicle (USV) can navigate autonomously via the global navigation satellite system (GNSS). However, the traditional GNSS scalar tracking loop easily loses lock in low carrier-to-noise ratio (CNR) situations, such as signal occlusion and weak signals. Meanwhile, an increase in the carrier/code phase error leads to an increase in the measurement error of the navigation filter, which decreases the accuracy of the position estimation. To solve this problem, this paper proposes a carrier and code tracking structure based on a forward and backward Kalman filter to dynamically adjust the gain of the vector tracking loop. The carrier and code phase errors calculated by the loop discriminators were linearly transformed into pseudo-range rate and pseudo-range errors after filtering and smoothing, which were used as the measurements of the navigation filter. The signal CNR was used to adaptively adjust the measurement noise covariance matrix of the loop filters. The field tests used a commercial receiver’s navigation solution as the reference. In the stationary test, the proposed structure reduced the localization error by 44.3% compared with the traditional methods. In kinematic experiments, the proposed structure reduced the carrier and code phase errors in a harsh signal environment and improved the positioning accuracy at the source. The test results demonstrate that the proposed GNSS tracking method can provide a possible solution for the development of navigation systems for USV.

An analytical approach to the estimation of vehicular communication reliability for intersection control applications

Connected and automated vehicles bring the capability to generate traffic data for intersection control applications. Unfortunately, for various reasons, such as signal blocking and interference, vehicles may experience communication failures leading to inefficiencies, e.g., unnecessarily long waiting queues, increased noise and greenhouse gas emissions or increased delays of emergency vehicles at intersections resulting in lower life-saving rates. This paper proposes an analytical approach to estimate vehicular communication reliability for road intersection control applications. The road segment in front of the intersection is divided dynamically into two areas. Due to the physical proximity to the roadside unit, the adjacent area is assumed to have more reliable communication than the more distant area. Consequently, the information about the approaching traffic in the more distant area is deduced from received communication in both areas. The effectiveness of the proposed method is verified by simulation experiments. First, we evaluate the communication reliability estimator by prediction accuracy metrics. Second, we study the benefits of the deployment of this estimator for emergency vehicle preemption. Our results provide evidence that the estimator has the potential to improve the performance of intersection control applications.

Direct Tumor Irradiation Potentiates Adoptive NK Cell Targeting Against Parental and Stemlike Cancer in Human Liver Cancer Models

Radiation therapy (RT) has been shown to effectively induce the expression of intercellular adhesion molecule-1 (ICAM-1), which is recognized by lymphocyte function-associated antigen 1 (LFA-1) expressed on natural killer (NK) cells. However, the potential synergistic antitumor immune response of tumor irradiation and administered NK cells has not been explored in intractable human liver cancers. Furthermore, NK cell targeting against both parental and cancer stemness has never been investigated. Highly activated ex vivo NK cells were administered into the human liver tumor-bearing mice. Tumor direct RT was optimized according to tumor bearing site. HepG2 and Hep3B ICAM-1 knockout cells were generated using CRISPR/CAS9. Stemness tumor spheres were generated. NK cell cytolysis against parental and tumor sphere was evaluated using flow cytometry and real-time cytotoxicity assay. A combination of adoptive NK cell therapy with RT significantly improved therapeutic efficacy over monotherapies against subcutaneous, orthotopic, and metastatic human liver tumor models. Direct tumor irradiation potentiated NK cell recognition and conjugation against liver cancer through the LFA-1/ICAM-1 axis. Suppression of immune synapse formation on NK cells using high-affinity LFA-1 inhibitors or ICAM-1 knockout liver cancer induced "outside-in" signal blocking in NK cells, resulting in failure to eliminate liver tumor despite the combination therapy. NK cells effectively recognized and targeted triple-high epithelial cell adhesion molecule+CD133+CD24+ liver cancer expressing upregulated ICAM-1 in the irradiated tumor microenvironment, which led to prevention of the initiation of metastasis, improving survival in a metastatic model. In addition, the LFA-1/ICAM-1 axis interruption between NK cells and stemness liver tumor spheres significantly diminished NK cell cytolysis. Consistent with our preclinical data, the LFA-1/ICAM-1 axis correlated with survival outcomes in patients with metastatic cancer from the The Cancer Genome Atlas databases. NK cells in combination with tumor irradiation can provide synergistic therapeutic effects for NK cell recognition and elimination against both parental and stemlike liver cancer through LFA-1/ICAM-1.

Joint Relay Selection and Beam Management Based on Deep Reinforcement Learning for Millimeter Wave Vehicular Communication

Cooperative relays improve reliability and coverage in wireless networks by providing multiple paths for data transmission. Relaying will play an essential role in vehicular networks at higher frequency bands, where mobility and frequent signal blockages cause link outages. To ensure connectivity in a relay-aided vehicular network, the relay selection policy should be designed to efficiently find unblocked relays. Inspired by recent advances in beam management in mobile millimeter wave (mmWave) networks, this paper addresses the question: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">how can the best relay be selected with minimal overhead from beam management?</i> In this regard, we formulate a sequential decision problem to jointly optimize relay selection and beam management. We propose a joint relay selection and beam management policy based on deep reinforcement learning (DRL) using the Markov property of beam indices and beam measurements. The proposed DRL-based algorithm learns time-varying thresholds that adapt to the dynamic channel conditions and traffic patterns. Numerical experiments demonstrate that the proposed algorithm outperforms baselines without prior channel knowledge. Moreover, the DRL-based algorithm can maintain high spectral efficiency under fast-varying channels.

Ultrasonic- and IMU-Based High-Precision UAV Localization for the Low-Cost Autonomous Inspection in Oil and Gas Pressure Vessels

With the increasing demands for unmanned aerial vehicle (UAV) based autonomous inspections in the oil and gas industry, one of the challenging issues for 3D UAV positioning has emerged due to the satellite signal blocking. Considering the existing characteristics of the ultrasonic based technique, such as the low cost, extremely lightweight and high positioning accuracy, it can be promising as the potential solution. Nevertheless, the low position update rate and vulnerable positioning performance to the changing environment still limit its applications on UAV. Therefore, in this article, an ultrasonic and inertial measurement unit (IMU) based localisation algorithm and low cost UAV autonomous inspection system are presented. With the incorporation of the IMU, the position update rate, accuracy and stability of the algorithm can all be significantly improved. This is done by the adaptively estimated noise covariance matrices through the proposed adaptive extended Kalman filter (AEKF) algorithm and the added weighting factors. Followed by, an additional virtual observation process is presented to overcome the unavailability of the observation information for further performance improvement. Finally, extensive numerical results and field tests demonstrate that the proposed algorithm and system can achieve the high update rate, reliable, accurate and precision UAV positioning in oil and gas pressure vessels and are feasible for the UAV autonomous inspection in these environments.

Observability-Constrained Resampling-Free Cubature Kalman Filter for GNSS/INS with Measurement Outliers

Integrating global navigation satellite systems (GNSSs) with inertial navigation systems (INSs) has been widely recognized as an ideal solution for autonomous vehicle navigation. However, GNSSs suffer from disturbances and signal blocking inevitably, making the performance of GNSS/INSs degraded in the occurrence of measurement outliers. It has been proven that the sigma points-based Kalman filter (KF) performs better than an extended KF in cases where large prior uncertainty is present in the state estimation of a GNSS/INS. By modifying the sigma points directly, the resampling-free sigma point update framework (SUF) propagates more information excepting Gaussian moments of prescribed accuracy, based on which the resampling-free cubature Kalman filter (RCKF) was developed in our previous work. In order to enhance the adaptivity and robustness of the RCKF, the resampling-free SUF depending on dynamic prediction residue should be improved by suppressing the time-varying measurement outlier. In this paper, a robust observability-constrained RCKF (ROCRCKF) is proposed based on adaptive measurement noise covariance estimation and outlier detection, where the occurrence of measurement outliers is modelled by the Bernoulli variable and estimated with the state simultaneously. Experiments based on car-mounted GNSS/INSs are performed to verify the effectiveness of the ROCRCKF, and result indicates that the proposed algorithm outperforms the RCKF in the presence of measurement outliers, where the heading error and average root mean square error of the position are reduced from 1.96° and 6.38 m to 0.27° and 5.95 m, respectively. The ROCRCKF is robust against the measurement outliers and time-varying model uncertainty, making it suitable for the real-time implementation of GNSS/INSs in GNSS-challenged environments.

OPTIMAL BLUETOOTH LOW ENERGY (BLE) SYSTEM DEPLOYMENT STRATEGY PRIOR TO SYSTEM APPLICATION ON THE CONSTRUCTION SITE

The application of Bluetooth Low Energy (BLE) systems for labour tracking on construction sites is a popular research trend. Strategic system deployment is crucial for ensuring the overall reliability and feasibility of these systems. This study aims to identify the optimal BLE system deployment strategy for tiling work zones in three different renovation sites. The BLE system consists of BLE beacon, BLE gateway, Raspberry Pi (data logger), and MATLAB for data analysis. To ensure optimal placement of the BLE gateway, site visits were conducted to evaluate various conditions, such as power supply availability, signal blockage by structures, and potential disruption to the tiling work. In addition, the beacon transmission (Tx) levels were adjusted to enhance the detection accuracy of the system. The findings show that the optimum beacon Tx powers at sites A, B, and C are -12dBm (86.3% accuracy), -16dBm (94.6% accuracy), and -16dBm (91.1% accuracy), respectively. These findings will be adopted in future research on these sites. The study contributes to the understanding of optimal BLE system deployment strategies in construction sites, which can improve labour tracking accuracy and overall project management.

Precise GNSS Time Synchronization With Experimental Validation in Vehicular Networks

Time synchronization utilizing the Global Navigation Satellite System (GNSS) is being increasingly investigated for vehicular networks. Due to GNSS signal blockages, the availability and accuracy of GNSS timing solutions in various road settings is a recognized challenge. With the recent improvement of Multi-GNSS technology and the increased capacity of consumer-grade receivers, the application of GNSS in vehicular environments has brightened up. This paper systematically analyzes the required time synchronization of vehicular networks and presents a GNSS-based time synchronization solution. It also experimentally demonstrates the availability and capabilities of GNSS time synchronization using commercial-grade GNSS receivers and off-the-shelf communication devices. Our experiments show that the timing accuracy of an individual vehicular node can be as good as ±2 microseconds, resulting in synchronization accuracy of sub-10 microseconds among nodes. A momentary complete outage of the GNSS time solution due to signal blockage on the road adds clock error, leading to synchronization inaccuracy of up to sub-20 microseconds. This level of inaccuracy still meets the desired requirement for most applications in vehicular communication.

Semantic Proximity Update of GNSS/INS/VINS for Seamless Vehicular Navigation Using Smartphone Sensors

The advancement of microelectromechanical systems (MEMS) and the Internet of Things (IoT) have enabled a wide range of applications based on smartphones. However, the existing navigation methods using these low-cost MEMS sensors cannot provide acceptable information for location-based applications in various environments. Their technical limitations, such as severe signal attenuation, reflections, blockages, error accumulation, and low quality of images degrade the performance of GNSS, INS, and camera. To mitigate these limitations, especially in indoor vehicle navigation, we first analyze the performance of the existing fusion algorithm, then we propose Semantic Proximity Update (SPU) based on a pre-trained model of real-time object detection to enhance the integration of Global Navigation Satellite System (GNSS), Inertial Navigation System (INS) and Visual Inertial Navigation System (VINS). SPU consists of the detection of geo-referenced objects and the relative movement to infer the absolute position. The proposed INS/GNSS/VINS/SPU can maintain long-term acceptable accuracy regardless of the indoor/outdoor environment. It only requires the use of smartphone sensors; thus, this scheme has no additional cost for users. Experimental results indicated that the errors of this scheme in horizontal positioning and three-dimensional positioning were 51.6% and 86.8% lower, respectively than those of a conventional integration.

BDS PPP ambiguity resolution using B1I/B2I/B3I/B1C/B2a observations with regional reference network augmentation

ABSTRACT BeiDou navigation satellite system with global coverage (BDS-3) has been fully operational since July 2020, currently providing the positioning, navigation and timing service together with regional BDS-2. In addition to the legacy signals of B1I and B3I, the BDS-3 also transmits several new signals such as BIC, B2a and B2b, which brings new opportunities for rapid ambiguity resolution (AR) of BDS precise point positioning (PPP). In this contribution, a multi-frequency (MF) rapid PPP-AR method with regional network augmentation was proposed. Firstly, BDS five-frequency observations were introduced into uncombined double-differenced models to retrieve regional augmentation corrections at the server. Thereafter, a cascade PPP-AR strategy using extra-wide-lane, wide-lane and narrow-lane ambiguity was employed at the user. Once ambiguities were fixed to integers, the phase correction accuracy could reach about 3 cm on average overall BDS frequencies in the network with inter-station distances of 100–200 km. Subsequently, the statistical results of seven-day simulated kinematic experiments showed that over 99% of epochs on average realized PPP-AR. Correspondingly, the positioning accuracy of the MF fixed solution reached 1.8, 1.9, 4.7 cm in the east, north and up components, respectively, improving by 5–15% compared with the dual-frequency scheme. Moreover, several vehicle-borne experiments under different urban scenarios were also conducted. Under an open-sky or a relatively open highway scene, the BDS-MF scheme similarly exhibited good performance, and over 98% of epochs achieved rapid PPP-AR with a positioning accuracy better than 3 cm. More encouragingly, for this BDS-challenged experiment with an average satellite number of 8.6, although only 72.06% of epochs were available due to serious signal blockages caused by overpass, tunnels or tall buildings, the horizontal positioning accuracy could still remain 7 cm on average.

Reconfigurable intelligent surface-assisted free-space optical communication link for theSatellite-Internet of Things.

The Satellite-Internet of Things (S-IoT) is envisaged as one of the promising technological enhancements in this decade due to its outright connectivity, especially in the areas of terrestrial communication and weather forecasting. Invariably, turbulent atmospheric conditions and varied terrain profiles are hindrances for satellite based free-space optical (FSO) communication, and immensely disrupt signal levels. Recently, a new technological invention called a reconfigurable intelligent surface (RIS) empowered radio transmission environments for next generation wireless/optical technology has emerged. Elements of RIS are used to intelligently tune the striking beam towards desired locations. In this paper, we propose an RIS based FSO link to manueuver S-IoT. The challenges of pointing errors and turbulent atmospheric connectivity from a satellite to ground station and vice versa with the cooperation of RIS have been derived and substantiated with closed form solutions. The proposed system's performance has been analyzed using packet and bit error rates, and outage probability over atmospheric turbulence, pointing errors, and signal blockage scenarios. Results are evaluated for different RIS elements as a function of average signal to noise ratio. The proposed system is extremely productive, since this scheme has the capability to provide dependable connectivity in rural, urban, and suburban environments where reliable connectivity is difficult in spite of traditional infrastructures.

Performance analysis of channel estimation techniques for IRS assisted MIMO

The need for low latency and high data rates is increasing rapidly since the advent of wireless communication. The current fifth-generation (5G) networks are unable to fulfill the requirements of upcoming technologies. So, researchers are commencing their research beyond 5G. Terahertz (THz) frequency is one candidate to satisfy the large bandwidth requirement and intelligent reflecting surface (IRS) is incorporated to mitigate signal blockage which is the main problem for communication at high frequencies. Channel estimation is a process of identifying coefficients of the channel matrix. The compressive sensing technique is of great importance as it decreases the number of pilot symbols required for channel estimation. As mmWave and THz signals are naturally sparse applying a compressive sensing technique is reasonable. Unlike other papers, this paper considers the imperfect IRS elements, which is the real case, by varying the value of beta (amplitude perturbations). The channel estimation performance of the conventional least squares (LS), orthogonal matching pursuit (OMP) and Oracle is analyzed with respect to signal-to-noise ratio (SNR) and pilot length (T). Normalized mean square error (NMSE) and spectral efficiency (SE) are used as performance metrics and the OMP algorithm is found to perform better than LS even at a fewer number of pilot symbols.

Hybrid-Fidelity: Utilizing IEEE 802.11 MIMO for Practical Aggregation of LiFi and WiFi

We present Hy-Fi, a system that aggregates light fidelity (LiFi) and radio frequency (RF)-based communication on the 802.11 (WiFi) physical layer by utilizing the MIMO capabilities in IEEE 802.11-compliant commodity WiFi chips. Hy-Fi is based on two key ideas. First, we use inexpensive commodity hardware to facilitate direct transmission of WiFi waveforms over the optical wireless channel, as this is proposed in the IEEE P802.11bb task group. Second, we use the MIMO signal processing from WiFi to aggregate LiFi and radio signals directly at the physical layer. Hy-Fi was implemented as a prototype and evaluated in a small testbed. Experimental results reveal that our approach offers excellent robustness against signal fading, blockage and external interference in both, the optical and radio channels making it suitable for applications with very strict requirements to the packet delay and loss ratio. Moreover, the two channels, LiFi and RF, can be aggregated to double the link capacity in the best case. Finally, we demonstrate how Hy-Fi could be used as wireless access technology in next-generation indoor enterprise networks providing both high capacity and seamless mobility.

Invariant natural killer T cells drive hepatic homeostasis in Non-alcoholic fatty liver disease via sustained IL-10 expression in CD170+ Kupffer cells.

Kupffer cells (KCs) are liver-resident macrophages involved in hepatic inflammatory responses, including non-alcoholic fatty liver disease (NAFLD) development. However, the contribution of KC subsets to liver inflammation remains unclear. Here, using high-dimensional single-cell RNA sequencing, we characterised murine embryo-derived KCs (Em-KCs) and identified two KC populations with different gene expression profiles: KC-1 and KC-2. KC-1 expressed CD170, exhibiting immunoreactivity and immune-regulatory abilities, while KC-2 highly expressed lipid metabolism-associated genes. In a high-fat diet (HFD)-induced NAFLD model, KC-1 cells differentiated into pro-inflammatory phenotypes and initiated more frequent communications with invariant natural killer T (iNKT) cells. In KC-1, interleukin (IL)-10 expression was unaffected by the HFD but impaired by iNKT cell ablation and upregulated by iNKT cell adoptive transfer in vivo. Moreover, in a cellular co-culture system, primary hepatic iNKT cells promoted IL-10 expression in RAW264.7 and primary KC-1 cells. CD206 signal blocking in KC-1 or CD206 knockdown in RAW264.7 cells significantly reduced IL-10 expression. In conclusion, we identified two Em-KC subpopulations with distinct transcriptional profiles. The CD206-mediated crosstalk between iNKT and KC-1 cells maintains IL-10 expression in KC-1 cells, affecting hepatic immune balance. Therefore, KC-based therapeutic strategies must consider cellular heterogeneity and the local immune microenvironment for enhanced specificity and efficiency. This article is protected by copyright. All rights reserved.

Deep Neural Network for Visual Localization of Autonomous Car in ITS Campus Environment

Intelligent Car (I-Car) ITS is an autonomous car prototype where one of the main localization methods is obtained through reading GPS data. However the accuracy of GPS readings is influenced by the availability of the information from GPS satellites, in which it often depends on the conditions of the place at that time, such as weather or atmospheric conditions, signal blockage, and density of a land. In this paper we propose the solution to overcome the unavailability of GPS localization information based on the omnidirectional camera visual data through environmental recognition around the ITS campus using Deep Neural Network. The process of recognition is to take GPS coordinate data to be used as an output reference point when the omnidirectional camera takes images of the surrounding environment. Visual localization trials were carried out in the ITS environment with a total of 200 GPS coordinates, where each GPS coordinate represents one class so that there are 200 classes for classification. Each coordinate/class has 96 training images. This condition is achieved for a vehicle speed of 20 km/h, with an image acquisition speed of 30 fps from the omnidirectional camera. By using AlexNet architecture, the result of visual localization accuracy is 49-54%. The test results were obtained by using a learning rate parameter of 0.00001, data augmentation, and the Drop Out technique to prevent overfitting and improve accuracy stability.

Enhanced Autonomous Vehicle Positioning Using a Loosely Coupled INS/GNSS-Based Invariant-EKF Integration.

High-precision navigation solutions are a main requirement for autonomous vehicle (AV) applications. Global navigation satellite systems (GNSSs) are the prime source of navigation information for such applications. However, some places such as tunnels, underpasses, inside parking garages, and urban high-rise buildings suffer from GNSS signal degradation or unavailability. Therefore, another system is required to provide a continuous navigation solution, such as the inertial navigation system (INS). The vehicle's onboard inertial measuring unit (IMU) is the main INS input measurement source. However, the INS solution drifts over time due to IMU-associated errors and the mechanization process itself. Therefore, INS/GNSS integration is the proper solution for both systems' drawbacks. Traditionally, a linearized Kalman filter (LKF) such as the extended Kalman filter (EKF) is utilized as a navigation filter. The EKF deals only with the linearized errors and suppresses the higher orders using the Taylor expansion up to the first order. This paper introduces a loosely coupled INS/GNSS integration scheme using the invariant extended Kalman filter (IEKF). The IEKF state estimate is independent of the Jacobians that are derived in the EKF; instead, it uses the matrix Lie group. The proposed INS/GNSS integration using IEKF is applied to a real road trajectory for performance validation. The results show a significant enhancement when using the proposed system compared to the traditional INS/GNSS integrated system that uses EKF in both GNSS signal presence and blockage cases. The overall trajectory 2D-position RMS error reduced from 19.4 m to 3.3 m with 82.98% improvement and the 2D-position max error reduced from 73.9 m to 14.2 m with 80.78% improvement.

Joint Power Control and Passive Beamforming Design for RIS-Assisted Secure Communication Systems

In this paper, considering the problem of signal blockage in the scenario of dense buildings, we study joint transmit power and phase shift design for the reconfigurable intelligent surface (RIS) assisted secure uplink communication systems. In particular, a friendly user with better channel state information than other users is selected as a jammer to interfere the eavesdropper. We aim to maximize the secrecy rate under the constant-modulus constraint for RIS phase shift. To handle the non-convex problem, we propose an alternating optimization based algorithm, where the successive convex approximation technique and manifold optimization are utilized for the optimization of transmission power and RIS phase shift, respectively. By introduce RIS into this system, the problem of signal blockage is solved. Moreover, simulation results show that the proposed scheme improves the system secrecy rate compared with the other benchmark schemes.