Multipath Effects Research Articles

Isometric mapping algorithm based GNSS-R sea ice detection

<abstract> <p>Global navigation satellite system reflectometry (GNSS-R) is based on satellite signals' multipath interference effect and has developed as one of the important remote sensing technologies in sea ice detection. An isometric mapping (ISOMAP)-based method is proposed in this paper as a development in sea ice detection approaches. The integral delay waveforms (IDWs), selected from February to April in 2018, derived from TechDemoSat-1 (TDS-1) Delay-Doppler maps (DDMs) are applied to open water and sea ice classification. In the first, the model for extracting low-dimensional coordinates of IDWs employs the randomly selected 187666 IDW samples, which are 30% of the whole IDW dataset. Then, low-dimensional coordinates of IDWs are used to train three different classifiers of support vector machine (SVM) and gradient boosting decision tree (GBDT), linear discriminant algorithm (LDA) and K-nearest neighbors (KNN) for determining the sea ice and sea water. The remaining 437889 samples, about 70% of the whole datasets, are used to validate with the ground surface type from the National Snow and Ice Data Center (NSIDC) data provided by the National Oceanic and Atmospheric Administration (NOAA). The algorithm performance is evaluated, and the overall accuracy of SVM, GBDT, LDA and KNN are 99.44%, 85.58%, 91.88% and 98.82%, respectively. The sea ice detection methods are developed, and the accuracy of detection is improved in this paper.</p> </abstract>

Introduction to Mediumband Wireless Communication

The multipath is unavoidable in radio frequency (RF) wireless communication, and affects almost every element of the communication systems. The impact of multipath on the received signal depends on whether the delay spread (i.e., spread of time delays associated with different multipath components) is large or small relative to the symbol period of the wireless communication system. In narrowband channels, the symbol period is set such that the delay spread is about one tenth (or less) of it. In broadband channels, it is set such that the delay spread is many times greater than the symbol period. In between these two extremes, there appears to exist an important, yet overlooked, class of channels whose delay spread is neither small nor large enough for them to fall into these two basic channel classes. In this paper, we study the effect of multipath on channels that fall in the transitional region between narrowband and broadband referred henceforth as “ <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>mediumband</i></b> ”. This paper shows that mediumband channels possess a distinct channel model, and pose both challenges and opportunities for reliable wireless communication. For instance, mediumband channels enable signalling at a significantly higher rate than that of narrowband channels, but on the flip side, as the degree of mediumband-ness increases, the quality of the channel deteriorates rapidly due to the excessive inter-symbol-interference (ISI). However, mediumband channels have an inherent ability to avoid deep fading, and if designed properly, mediumband wireless communication, which refers to wireless communication through mediumband channels, could be made to be significantly more reliable too.

A Novel Method for Improving Point Cloud Accuracy in Automotive Radar Object Recognition

High-quality environmental perceptions are crucial for self-driving cars. Integrating multiple sensors is the predominant research direction for enhancing the accuracy and resilience of autonomous driving systems. Millimeter-wave radar has recently gained attention from the academic community owing to its unique physical properties that complement other sensing modalities, such as vision. Unlike cameras and LIDAR, millimeter-wave radar is not affected by light or weather conditions, has a high penetration capability, and can operate day and night, making it an ideal sensor for object tracking and identification. However, the longer wavelengths of millimeter-wave signals present challenges, including sparse point clouds and susceptibility to multipath effects, which limit their sensing accuracies. To enhance the object recognition capability of millimeter-wave radar, we propose a GAN-based point cloud enhancement method that converts sparse point clouds into RF images with richer semantic information, ultimately improving the accuracy of tasks such as object detection and semantic segmentation. We evaluated our method on the CARRADA and nuScenes datasets, and the experimental results demonstrate that our approach improves the object classification accuracy by 14.01% and semantic segmentation by 4.88% compared to current state-of-the-art methods.

Joint Localization and Environment Sensing of Rigid Body With 5G Millimeter Wave MIMO

Accurately localizing a target in three-dimensional (3D) space assisted with the fifth generation (5G) wireless systems in an indoor environment could enable a wide variety of new applications, including precise control for factory automation, self-maneuver of the vehicle and so on. However, controlling a target in 3D space relies on a rigid body modelling with six degrees of freedom, which dramatically increases the localization difficulty and complexity. Furthermore, for radio-based localization methods, the lack of line-of-sight (LOS) and the existence of reflection points in the environment will also influence the rigid body localization process. To improve the rigid body localization accuracy as well as unravel useful environmental information from the received signal, a novel rigid body joint active localization and environment sensing scheme is proposed in this paper. Specifically, the multi-path effect of millimeter wave (mm-wave) signal with a single reflection can be exploited to enhance the rigid body localization accuracy, and it can also be utilized to locate the reflection points, which further enables a new way for environmental sensing. Hence, we first propose a two-step hierarchical compressive sensing algorithm to extract the angular and distance information of the LOS (if available) and single-bounce specular reflections. Then a particle swarm optimization (PSO) based method is derived to recover the posture of the rigid body and the location of reflection points. The Cramér-Rao lower bound (CRLB) on angle, rigid body posture and reflection points position uncertainty is also analyzed. The simulation results demonstrate that the proposed scheme can achieve high accuracy rigid body localization and locate the reflection points around the rigid body even under obstructed-line-of-sight (OLOS) conditions in an indoor scene.

A Wide Range Distributed Multi-Node Receive Chain Calibration Method Based on GNSS Software Receiver

The phase calibration of distributed multi-node receiving system is required considering the phase synthesis working mode of distributed radar system for echo signals. Whereas, in respect to the traditional calibration tower and unmanned aerial vehicle scheme, there are many disadvantages. The wide range distributed multi-node receive chain calibration method based on GNSS software receiver was proposed in this paper, taking advantage of the software receiver to calculate and obtain the actual phase difference among the receiving nodes as well as the coordinate information of satellites in transmitting signal. Therefore, the phase difference resulted from different transmit path can be deleted, and the phase inconsistencies among the receiving nodes can be obtained. Besides, the fingerprint maps database regarding inconsistencies among nodes and two-dimensional angle of relative receiving node for satellites was constructed in this paper, the correspondence between the omnidirectional two-dimensional angle and the inconsistencies among different nodes was established by the actual results and two-dimensional fitting, and the phase inconsistency results among nodes can be obtained only by solving the satellite coordinate information. There are many advantages for the method proposed in this paper, such as slight influence of multipath effect, capability of all-weather all-day calibration, low costs.

Spectral Unmixing Successive Variational Mode Decomposition for Robust Vital Signs Detection Using UWB Radar

The evaluation of clinical status and vital signs is a crucial component of remote medical care. Radar provides a non-contact monitoring measurement without consideration of lighting or privacy. However, the multipath effect generated by multiple people gathered in a small indoor space seriously affects the detection of vital sign information. This study presents a non-contact measurement of the vital signs based on stepped-frequency continuous wave ultra-wideband(SFCW-UWB) and impulse radio ultra-wideband(IR-UWB) radars. Additionally, this study suggests the Spectral Unmixing Successive Variational Mode Decomposition (SUSVMD) algorithm since the respiratory components have a probability of mixing with the heartbeat signal’s frequency. The suggested approach extracts band-limited modes through back projection and spectral unmixing optimization. The alternate direction method of multiplication is used to address the optimization problem. The experiments are conducted under two radar measurements and three subjects. The results show that the suggested method is superior and effective.

Physiological Motion Sensing via Channel State Information in NextG Millimeter-Wave Communications Systems

Wireless communications systems provide channel state information (CSI), which can be used to characterize the physical propagation environment. The small physiological motion of human subjects in that environment, such as that associated with respiration, can modulate the CSI, thus allowing wireless physiological sensing through which a communications system detects and monitors cardiopulmonary motion. NextG millimeter-wave communications systems present even greater opportunities for wireless physiological sensing due to the advantages of small wavelength and high directionality. But challenges also arise due to the multipath effect and phase aliasing caused by larger-than-wavelength motion displacement. This work introduces a comprehensive mathematical CSI model to accurately characterize physiological motion captured by the amplitude and phase of the CSI of a millimeter-wave communications system. The model allows for the interpretation of intricate CSI pattern variations to avoid aliasing error and has been validated with experiments involving both parametric measures conducted with a robotic mover and respiration rate measurements for human subjects. In all cases, rate measurements could be consistently resolved within 10%, or 0.02 Hz, demonstrating the potential for incorporating physiological sensing in NextG millimeter-wave communications systems.

Improved GPS-Based Single-Frequency Orbit Determination for the CYGNSS Spacecraft Using GipsyX

<h3>Summary</h3> This paper presents methods for the precise orbit determination (POD) of a satellite in the CYGNSS constellation based on available single-frequency GPS code and carrier measurements. The contributions include the development and evaluation of procedures for single-frequency POD with GipsyX, improvement of CYGNSS orbit knowledge, and an assessment of its final accuracy. Ionospheric effects are mitigated using the GRAPHIC processing method, and spacecraft multipath effects are calibrated with an azimuth/elevation-dependent antenna calibration map. The method is demonstrated using comparable data from the GRACE mission, from which we infer the expected accuracy of the CYGNSS results. Processing more than 170 days of data from each mission, a 1<i>σ</i> CYGNSS orbit accuracy of 2.8 cm radial, 2.4 cm cross-track, and 6 cm in-track is demonstrated. We expect that achieving this level of performance will expand the set of future scientific investigations that can be undertaken using satellites equipped with single-frequency GNSS.

Geometric Arithmetic Mean Method for Low Altitude Target Elevation Angle Tracking

In tracking operation, the signal reflected from the target comes to the antenna in two paths. The first is the direct path, from the target to the antenna, and the second is the indirect path, due to the multipath phenomenon, from the target to the surface then to the antenna. In a low altitude situation, the indirect path can cause severe errors in tracking operation as it enters the antenna main beam and combines with the direct path signal. In such a scenario, the antenna fails to track the elevation angle by the target's image located under the ground. A significant challenge in tracking systems is to mitigate the multipath effect. To do so, we extended the elevation geometric mean method and presented the target and image elevation angles separation method. Two monopulse equations are proposed to calculate the target elevation angle. The proposed method offers low computational load and can be implemented on monopulse system. The complete noise analysis is derived for the method. It is shown that the error of the proposed method reaches the Carmer Rao Lower Bound by proper selection of frequencies. Simulation results show accurate target tracking in a multipath environment.

Performance Comparison of TR and FSRUWB System Using Particle Filter: Effects of Frequency, Data Rate, Multi-Path and Multi-Channel Communication

In this study, we introduced a novel scheme based on Transmitted References (TR) and Frequency Shifted Reference (FSR) for ultra-wideband (UWB) system. By taking into account tracking loop-based particle filtering together with a data collecting approach for single and multi-path channel situations, the suggested method is an enhanced model. Each particle's location is determined using this filtering technique, which is then utilised to calculate the timing inaccuracy and regulate the UWB system's timing pulse. Also, it can tackle the multimodal distribution of errors then effectively approximate the optimal solution. The data distribution is discretised via a number of particles that are weighted samples evolving concerning time duration. The simulation results show that, in terms of error rate, number of particles, and delay response, the recommended model of FSR-UWB with particle filter performs better than the TR-UWB with and without considering particle filter.

Laboratory Analysis on the Performance of 5G NSA Communication in a Suburban Scenario

The propagation of information by electromagnetic waves suffers different types of interference, according to the characteristics of the environment. The 5G system relies on adaptive modulation and coding techniques to better suit the channel and maximize effective data exchange between the user equipment and the network. Practical studies on the behaviour of the system under different environmental conditions, subject to attenuation processes such as fading, are important to understand and improve the 5G efficiency. This work has analysed the effect of the MCS (Modulation and Coding Scheme) variation on throughput for channel degraded by the multipath fading effect in a mobile communication. The analysis was carried out showing that the decision algorithms in terms of MCS switching to maintain adequate data rates according to the requirement (QoS) is an important factor. Considering both 64 QAM and 256 QAM, the throughput degradation effect was more evident in higher-order modulations due to the higher probability of bit error in the symbol constellation. This study can be a key for understanding and developing robust MCS switcher for 5G and beyond communications.

CAE-CNN-Based DOA Estimation Method for Low-Elevation-Angle Target

For the DOA (direction of arrival) estimation of a low-elevation-angle target under the influence of a multipath effect, this paper proposes a DOA estimation method based on CAE (convolutional autoencoder) and CNN (convolutional neural network). The algorithm firstly inputs the signal covariance matrix of the array of the low-elevation target containing direct and reflected waves into the convolutional autoencoder to realize the de-multipath, and uses the spatial features extracted by the convolutional autoencoder as the input of the extreme learning machine to realize the DOA preclassification of direct waves; based on the preclassification results, one branch of the three parallel convolutional neural nets is selected, and the output of the convolutional autoencoder is used as the input of this branch to realize DOA estimation. The simulation results show that the algorithm has better estimation accuracy and efficiency than the conventional algorithms, especially when the DOA of the target is in the lower range. The analysis of the simulation results shows that the algorithm is effective, in which the convolutional autoencoder can effectively realize the de-multipath, and the use of parallel convolutional neural networks can avoid overfitting and underfitting and realize DOA estimation more accurately.

Design of IM-DD Optical Communication using Matlab

A modulation scheme where the intensity of an optical source is modulated by the RF or mm-wave signal. Demodulation is achieved through direct detection of the optical carrier and conversion using a photo detector is called IM-DD Systems. In these systems the multi-layer modulation (MLM) is used to design both asymmetric clipped optical OFDM (ACO-OFDM) and dc-biased optical OFDM (DCO-OFDM) as a function. In this systems the DCO OFDM is more spectrally efficient than non-DC biased systems because this have better tradeoff between spectral and power efficiency. In the direct detection system, the detection process is non-linear due to the photo current proportional to absolute square of electric field intensity due to this the signal is slow. The equalizer is comprised of one hidden layer which leads to super-fast of the signal .The channel capacity for this systems can be measured by the transmitting of the signals. This IM/DD is a cost-effective optical communication which finds wide applications in fiber communication, free-space optical communication, and indoor visible light communication. In these systems when the bandwidth of signal exceeds the modulation bandwidth of LED, multi path effect occurs. Therefore, we use ACO-OFDM to avoid this multi path distortions. OFDM is a suitable efficient cost-effective solution for GPON deployment with benefit of use of low-cost laser bandwidth in comparison with DCO-OFDM. At the same BER performance, in comparison with D-C ACO-OFDM at high bit rates transmission, DCO-OFDM promises to deliver higher throughput.

Innovation-Based Fault Detection and Exclusion Applied to Ultra-WideBand Augmented Urban GNSS Navigation

Due to their ability to provide a worldwide absolute outdoor positioning, Global Navigation Satellite Systems (GNSS) have become a reference technology in terms of navigation technologies. Transportation-related sectors make use of this technology in order to obtain a position, velocity, and time solution for different outdoor tasks and applications. However, the performance of GNSS-based navigation is degraded when employed in urban areas in which satellite visibility is not good enough or nonexistent, as the ranging signals become obstructed or reflected by any of the numerous surrounding objects. For these situations, Ultra-Wideband (UWB) technology is a perfect candidate to complement GNSS as a navigation solution, as its anchor trilateration-based radiofrequency positioning resembles GNSS’s principle. Nevertheless, this fusion is vulnerable to interferences affecting both systems, since multiple signal-degrading error sources can be found in urban environments. Moreover, an inadequate location of the augmenting UWB transmitters can introduce additional errors to the system due to its vulnerability to the multipath effect. Therefore, the misbehavior of an augmentation system could lead to unexpected and critical faults instead of improving the performance of the standalone GNSS. Accordingly, this research work presents the performance improvement caused by the application of Fault Detection and Exclusion methods when applied to a UWB-augmented low-cost GNSS system in urban environments.

Wireless positioning based on hierarchical symbolic dynamic filtering of RSSI time series

Hierarchical symbolic dynamic filtering (HSDF) is used in literature for anomaly detection by unsupervised classification of time series data. This paper proposes a novel wireless positioning method based on the HSDF concept. This approach aims to tackle the main problems in fingerprinting-based localization methods by eliminating the offline phase, which is the most time-consuming part of a fingerprinting localization. Online learning of Received Signal Strength Indicator (RSSI) time series data causes this method to be efficient in dealing with indoor environments’ dynamical behavior, which is another problem in the localization methods domain. Due to SDF (symbolic dynamic filtering)-based structure, it shows robustness to noise and multipath effect. This paper demonstrates these achievements by results obtained from both simulation of noisy datasets and real-time positioning in an indoor testbed.

EVALUATION OF THE MULTIPATH EFFECT ON THE QUALITY OF RADIO COMMUNICATION IN THE TECHNOLOGICAL RANGE IEEE 802.11xx

Background. As a result of the rays’ reflection from buildings, from the surface of the Earth or from horizontal boundaries between different layers of the atmosphere, occur multipath effects that cause fast fading, which degrades the quality of service.&#x0D; Objective. The purpose of the paper is to determine the multipath effect on the signal-to-noise ratio in a wireless communication channel. Compare the results obtained for a communication channel at operating frequencies of the IEEE 802.11xx standard: 5 GHz and 2.4 GHz.&#x0D; Methods. The useful signal powers and the total interference powers for a communication channel with multipath are calculated. The signal-to-noise ratio for such a channel is compared at operating frequencies of 5 GHz and 2.4 GHz.&#x0D; Results. The results of the study showed that the higher the frequency of the carrier of the wireless radio link, the less pronounced the effect of multipath is.&#x0D; The frequency rating significantly affects the quality of communication in multipath conditions. In this case, lowering the frequency significantly improves the reception condition outside of multipath effect. However, it deteriorates significantly due to the effects of rays reflected from the Earth's surface.&#x0D; In the considered case, the useful signal power in the 2.4 GHz band is 4.2 times higher; at the same time, the power of reflected interference is 19 times higher than in the 5 GHz band.&#x0D; Conclusions. Suggested model makes it possible to determine the effect of interference power in the ranges traditionally assigned in IEEE 802.11xx technology, specifically 2.4 GHz and 5 GHz.

Performance of GPS and IMU sensor fusion using unscented Kalman filter for precise i-Boat navigation in infinite wide waters

The Unmanned Surface Vehicle (USV) navigation system needs an accurate, firm, and reliable performance to avoid obstacles, as well as carry out automatic movements during missions. The Global Positioning System (GPS) is often used in these systems to provide absolute position information. However, the GPS measurements are affected by external conditions such as atmospheric bias and multipath effects. This leads to the inability of the stand-alone GPS to provide accurate positioning for the USV systems. One of the solutions to correct the errors of this sensor is by conducting GPS and Inertial Measurement Unit (IMU) fusion. The IMU sensor is complementary to the GPS and not affected by external conditions. However, it accumulates noise as time elapses. Therefore, this study aims to determine the fusion of the GPS and IMU sensors for the i-Boat navigation system, which is a USV developed by Institut Teknologi Sepuluh Nopember (ITS) Surabaya. Using the Unscented Kalman filter (UKF), sensor fusion was carried out based on the state equation defined by the dynamic and kinematic mathematical model of ship motion in 6 degrees of freedom. Then the performance of this model was tested through several simulations using different combinations of attitude measurement data. Two scenarios were conducted in the simulations: attitude measurement inclusion and exclusion (Scenarios I and II, respectively). The results showed that the position estimation in Scenario II was better than in Scenario I, with the Root Mean Square Error (RMSE) value of 0.062 m. Further simulations showed that the presence of attitude measurement data caused a decrease in the fusion accuracy. The UKF simulation with eight measurement parameters (Scenarios A, B and C) and seven measurement parameters (Scenarios D, E and F), as well as analytical attitude movement, indicated that yaw data had the largest noise accumulation compared to roll and pitch.

MEASURING NUT VOLUMES USING THE AZURE KINECT

Abstract. Rapid and accurate nut count and volume measurement techniques are critical for nut production and its automation. In this work, a three-dimensional (3D) depth camera, the Azure Kinect, was used to measure the volume of the nuts, along with our proposed point cloud processing framework. A group of nut16rowns was first collected as point clouds using the Azure Kinect, and then the point nearest neighbor (PNN) algorithm was used to perform fast nut single segmentation. After that, noise caused by the multi-path effect was identified from the collected point clouds. Most of the noise was eliminated using the surface boundary filter (SBF) algorithm. After the preprocessing, the volumes of the nut point cloud were estimated using the least-squares ellipsoid fitting (LSEF). We selected three different nuts in the experiment: 15 each of walnuts, pecans, and macadamia nuts. Their volumes were first measured by the Azure Kinect, and then by the water displacement method (WDM) as control. The results show that the proposed measurement setup can accurately count nuts and the average nuts volume estimation accuracy is 92.1% when compared to the reference volumes. The Azure Kinect can effectively count the number of nuts and accurately estimate the volume of nuts of different types and sizes, based on our proposed framework.

On Mitigating the Effects of Multipath on GNSS Using Environmental Context Detection

Accurate, ubiquitous and reliable navigation can make transportation systems (road, rail, air and marine) more efficient, safer and more sustainable by enabling path planning, route optimization and fuel economy optimization. However, accurate navigation in urban contexts has always been a challenging task due to significant chances of signal blockage and multipath and non-line-of-sight (NLOS) signal reception. This paper presents a detailed study on environmental context detection using GNSS signals and its utilization in mitigating multipath effects by devising a context-aware navigation (CAN) algorithm that detects and characterizes the working environment of a GNSS receiver and applies the desired mitigation strategy accordingly. The CAN algorithm utilizes GNSS measurement variables to categorize the environment into standard, degraded and highly degraded classes and then updates the receiver’s tracking-loop parameters based on the inferred environment. This allows the receiver to adaptively mitigate the effects of multipath/NLOS, which inherently depend upon the type of environment. To validate the functionality and potential of the proposed CAN algorithm, a detailed study on the performance of a multi-GNSS receiver in the quad-constellation mode, i.e., GPS, BeiDou, Galileo and GLONASS, is conducted in this research by traversing an instrumented vehicle around an urban city and acquiring respective GNSS signals in different environments. The performance of a CAN-enabled GNSS receiver is compared with a standard receiver using fundamental quality indicators of GNSS. The experimental results show that the proposed CAN algorithm is a good contributor for improving GNSS performance by anticipating the potential degradation and initiating an adaptive mitigation strategy. The CAN-enabled GNSS receiver achieved a lane-level accuracy of less than 2 m for 53% of the total experimental time-slot in a highly degraded environment, which was previously only 32% when not using the proposed CAN.

A Comparative Study of Error Vector Magnitude Measurement in a Reverberation Chamber

This letter shows a comparative analysis between the conventional and reverberation chamber (RC) measurements of error vector magnitude (EVM). The conventional methods, including direct link (DL) and anechoic chamber (AC), have been used to measure EVM for several years. However, the over-the-air testing of devices under test (DUT) is emphasized, and the EVM measurement in RC is a difficult question. For mass production testing, instead of using two chambers (RC and AC), it is expected to integrate EVM testing into the existing RC measurement system to give a pass/fail conclusion quickly. Hence, we measured EVM in 2 GHz (2412 MHz) and 5 GHz (5180 MHz) using RC, AC, and DL methods. From the measurement results, we find that the measured EVM is repeatable, stable, and insensitive to the location of DUT. Besides, the measured EVMs obtained from all scenarios deteriorate with the decrease of received power. Comparing the results from RC with that from the conventional methods, it could be concluded that with the reduction of multipath effect, the measured EVMs approach to the ideal values measured in AC. The differences among the methods could be used to calibrate the measured EVMs and give a pass/fail conclusion efficiently for mass production.