Angle Of Arrival Research Articles

Vehicular Visible Light Positioning System Based on a PSD Detector.

In this paper, we explore the use of visible light positioning (VLP) technology in vehicles in intelligent transportation systems (ITS), highlighting its potential for maintaining effective line of sight (LOS) and providing high-accuracy positioning between vehicles. The proposed system (V2V-VLP) is based on a position-sensitive detector (PSD) and exploiting car taillights to determine the position and inter-vehicular distance by angle of arrival (AoA) measurements. The integration of the PSD sensor in vehicles promises exceptional positioning accuracy, opening new prospects for navigation and driving safety. The results revealed that the proposed system enables precise measurement of position and distance between vehicles, including lateral distance. We evaluated the impact of different focal lengths on the system performance, achieving cm-level accuracy for distances up to 35 m, with an optimum focal length of 25 mm, and under low signal-to-noise conditions, which meets the standards required for safe and reliable V2V applications. Several experimental tests were carried out to validate the results of the simulations.

Signal Processing and Channel Modelling for 5G Millimeter-Wave Communication Environment

Compared to frequency bands below 6 GHz, 5G millimeter waves offer several advantages, including a large bandwidth, minimal null delay, and flexible null port configuration. To comprehend the channel characteristics of 5G millimeter-wave technology, conducting channel measurements on it is essential. Hence, to ensure precise 5G millimeter-wave channel measurements and facilitate channel modelling, this study recommends utilizing a phased array antenna-based method for the channel measurement. The experimental outcomes demonstrated that the shadow fading term of the actual measurement data follows a normal distribution in both line-of-sight and non-line-of-sight scenarios. Additionally, the K-S test confirms that all Boolean variables are equal to 1 and all numerical variables are greater than α. The line-of-sight scenario produces a logarithmic mean delay extension of –7.4 and a standard deviation of 0.12, based on actual measured data. Meanwhile, the 3GPP shows a logarithmic mean of –7.4 and a standard deviation of 0.15. In the non-line-of-sight scenario, the logarithmic mean delay extension is –7.3 with a standard deviation of 0.17, while the 3GPP produces a logarithmic mean of –7.4 and a standard deviation of 0.19. The data presented closely adheres to the 3GPP model. It is evident that the channel measurement method, proposed within the study, effectively measures the parameters within the delay domain. Concerning the prolonged pitch angle and azimuth angle ranges, they measure 14°–31° and 14°–29° in the line-of-sight situation, and 21°–33° and 19°–37° in the corresponding non-line-of-sight situation. Additionally, the logarithmic mean and standard deviation for both the pitch angle and azimuth angle in the line-of-sight scenario are 1.32 and 0.09, respectively. The logarithmic mean and standard deviation of the azimuth angle of arrival are 1.35 and 0.08, respectively. The above results show that the method proposed in the study enables the measurement of 5G millimeter-wave channels and is important for millimeter-wave channel modelling.

Subarray-based joint source localization in shallow water waveguide via subspace intersection

By the use of an underwater bottom-mounted Horizontal Linear Array (HLA), the two-dimensional joint localization technique and its accuracy are analyzed in this article. The proposed method takes advantage of arrival angles measured by a dual-subarray system consisting of a large-aperture HLA. Then, for the system, expressions for source position estimate and error variation are derived. Analysis of the Geometrical Dilution of Precision, which is a measure of localization error defined in Eq. (4), reveals that the localization error is mainly related to the source bearing, subarray interval and their measurement errors. In order to investigate the impact of the underwater waveguide, numerical simulations were carried out based on the normal mode theory. By using the distribution of acoustic intensity and structure of modal arrival, it is proved that the localization accuracy increases as the grazing angle and the subarray interval increase. However, when the measurement errors of source bearing and subarray interval increase, the localization accuracy will decrease. In addition, the obtained bearing estimation result provides an evident characteristic of beam spreading. When sources are near the end-fire direction of the dual-subarray system, significant bearing estimation error occurs, the negative effect of which is then presented. By applying the Subspace Intersection method, unbiased bearing estimation has been achieved, with relatively reduced computational complexity. Most of all, the improved localization performance is validated by experimental data collected in the South China Sea.

The angle of arrival estimation of frequency-hopping cooperative object based on software-defined radio

The primary objective of this research is to propose and examine a technique for accurately determining the azimuth angle of cooperative objects. The proposed methodology aims were to achieve fast processing, increased precision, and enhanced safety. A transmitter emitting a continuous wave with a hopping frequency was utilized in combination with interferometry techniques to measure the angle of arrival (AOA) between two antennas. The efficient method incorporates a coarse-to-fine strategy that improves processing speed and azimuth angle accuracy and to effectively eliminate any signal interference, a Fourier bandpass filter is utilized. The coarse estimation is performed using the fast Fourier transform (FFT) algorithm, while the fine estimation is achieved using the multiple signal classification (MUSIC) algorithm. The fine estimation involves utilizing coarse angle input and a scan limit of 2.5° that is determined from the largest simulated root mean square error (RMSE) value. Modelling and outdoor testing using software defined radio (SDR) have been carried out to assess the proposed methodology. The results from the analysis indicate that the proposed method produces the desired RMSE estimation of less than 1°, thereby validating its accuracy and effectiveness.

Pencil Beams and High-Precision Aiming of Wireless Power

Wireless Power Transfer (WPT) is the technique of delivering energy to specific targets with high-gain antennas. An important global application is SpaceBased Solar Power (SBSP) in which powersats in Earth orbit gather sunlight and beam this power to terrestrial groundstations (rectennae). At the distances involved, and the power levels needed to be economical, any stray (off-axis or off-target) energy can disrupt communications, giving cause for regulatory restriction or public objection of SBSP. Until recently, sidelobe levels (SLL) were limited to no lower than -60 dB, meaning that a gigawatt beam would give off kilowatt-range SLL, leading to desensitization of communications devices for a very wide radius surrounding a rectenna. The author discovered, published, and validated a specific design for a phased array antenna (PAA) that allows SLL to be reduced to -240 dB, creating what is called a "pencil beam" with negligible off-axis energy. Studies have found that this method can be applied to PAA as small as 6 m diameter at 5.8 GHz. This breakthrough discovery was first published in 2016 but appears to have largely escaped notice by the WPT community. The second issue is aiming accuracy, with the de facto solution being phase conjugation at the transmit PAA using a pilot beam issued from the receiver. While elegant, the accuracy of angle of arrival (AOA) using phase conjugtion is limited to about 1 degree (17 milliradians). This is sufficient for 5G signals over 100s of meters, but grossly inadequate for SBSP that requires microradian pointing accuracy. A novel method to detect AOA at the microrad scale that is separated from the transmitter emission electronics allows independent optimization of both. This work presents simulation studies showing microradian capability and laboratory results in the single millirad range, a factor of 10 improvement over the current state of the art. With this new approach, an orbiting powersat can aim its beam to a terrestrial target with stability under 100 m with off-axis energy at -127 dBm (typical surface environments are -100 to -80 dBm). The combination of a pencil beam and highly-accurate AOA detection bode well for realizing the benefits of SBSP.

Developments of a centimeter-level precise muometric wireless navigation system (MuWNS-V) and its first demonstration using directional information from tracking detectors

Various positioning techniques such as Wi-Fi positioning system have been proposed to use in situations where satellite navigation is unavailable. One such system, the muometric positioning system (muPS), was invented for navigation which operates in locations where even radio waves cannot reach such as underwater or underground. muPS takes advantage of a key feature of its probe, cosmic-ray muons, which travel straightforwardly at almost a speed of light in vacuum regardless of the matter they traverse. Similar to other positioning techniques, muPS is a technique to determine the position of a client’s muPS receiver within the coordinate defined by reference detectors. This can be achieved either by using time-of-flight (ToF) or angle of arrival (AoA) measurements. The latter configuration (AoA), called the Vector-muPS has recently been invented and the present paper describes the developments of the first prototype of a vector muometric wireless navigation system (MuWNS-V) with this new vector-muPS concept and its demonstration. With MuWNS-V, the reference tracker and the receiver ran wirelessly with fully independent readout systems, and a positioning accuracy of 3.9 cm (RMS) has been achieved. We also evaluated the outcome of measuring continuous indoor localization of a moving receiver with this prototype. Our results indicated that further improvements in positioning accuracy will be attainable by acquiring higher angular resolution of the reference trackers. It is anticipated that “sub-cm level” navigation will be possible for muPS which could be applied to many situations such as future autonomous mobile robot operations.

A method to build feature descriptor for GNSS spoofing detection by carrier phase double difference measurement

GNSS spoofing is a type of attack that aims to deceive GNSS receivers by transmitting fake signals that imitate the authentic ones. To detect such attacks, a possible solution is to calculate the double difference (DD) of carrier phase measurements between two antennas of two separated receivers. This DD measurement represents the angle of arrival (AoA) information of the signal. In the case of authentic signals, each signal comes from a different satellite, so the AoA values are different for each satellite. However, in the case of a spoofing attack, all the counterfeit signals from the same broadcaster come from the same direction, and thus the AoA values are the same for all DD measurements. Sum of Squares (SoS) detector and Dispersion of Double Difference (D3) detector are known as simple but effective methods for spoofing detection. However, these detectors are not robust in some special regions where cycle slips are large and vary rapidly. This paper proposes a new method for detecting GNSS spoofing by using carrier phase double difference measurements to generate a feature vector as a descriptor for each satellite in a set of observed satellites. The feature vectors are formed by statistical parameters of a series of consecutive double difference values computed for each reference satellite. Our proposed feature descriptor has been evaluated on a dataset containing both real and fake signals using an SVM classifier. The study shows that the proposed feature descriptor is highly effective in detecting spoofing satellite signals.

A Performance Comparison between Different Industrial Real-Time Indoor Localization Systems for Mobile Platforms.

The flexibility and versatility associated with autonomous mobile robots (AMR) have facilitated their integration into different types of industries and tasks. However, as the main objective of their implementation on the factory floor is to optimize processes and, consequently, the time associated with them, it is necessary to take into account the environment and congestion to which they are subjected. Localization, on the shop floor and in real time, is an important requirement to optimize the AMRs' trajectory management, thus avoiding livelocks and deadlocks during their movements in partnership with manual forklift operators and logistic trains. Threeof the most commonly used localization techniques in indoor environments (time of flight, angle of arrival, and time difference of arrival), as well as two of the most commonly used indoor localization methods in the industry (ultra-wideband, and ultrasound), are presented and compared in this paper. Furthermore, it identifies and compares three industrial indoor localization solutions: Qorvo, Eliko Kio, and Marvelmind, implemented in an industrial mobile platform, which is the main contribution of this paper. These solutions can be applied to both AMRs and other mobile platforms, such as forklifts and logistic trains. In terms of results, the Marvelmind system, which uses an ultrasound method, was the best solution.

Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G gNB

High-accuracy positioning has become a fundamental enabler for intelligent connected devices. Nevertheless, the present wireless networks still rely on model-driven approaches to achieve positioning functionality, which are susceptible to performance degradation in practical scenarios, primarily due to hardware impairments. Integrating artificial intelligence into the positioning framework presents a promising solution to revolutionize the accuracy and robustness of location-based services. In this study, we address this challenge by reformulating the problem of angle-of-arrival (AoA) estimation into image reconstruction of spatial spectrum. To this end, we design a model-driven deep neural network (MoD-DNN), which can automatically calibrate the angular-dependent phase error. The proposed MoD-DNN approach employs an iterative optimization scheme between a convolutional neural network and a sparse conjugate gradient algorithm. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed method in enhancing spectrum calibration and AoA estimation.

DOA Estimation of Ultrasonic Signal by Indirect Phase Shift Determination

The paper presents the concept of the method of determining the direction of ultrasonic signal arrival, i.e., the azimuth and elevation angles. This method is an extension of the previous approach which was proposed to determine only the azimuth angle. The approach is based on the indirect phase determination. This makes it possible to tolerate spacing of receivers greater than half the wavelength of the received signal. At the same time, it provides increased measurement accuracy and reduced hardware requirements. To check the robustness of the method, simulations were carried out for the geometric arrangement of the receivers of the sonar module, for which the method was then implemented. This sonar module was used in the conducted experiments. The results of these simulations and experiments are included in the paper and discussed.

Interference Situational Aware Beam Pointing Optimization for Dense LEO Satellite Communication System

Recently, the low earth orbit (LEO) mega-constellation faces serious time-varying interferences due to spectrum sharing, dense deployment, and high mobility. Therefore, it is important to study the interference avoidance techniques for the dense LEO satellite system. In this paper, the interference situational aware beam pointing optimization technique is proposed. Firstly, the angle of departure (AoD) and angle of arrival (AoA) of the interfering links are obtained to represent the time-varying interference. Then, the interference avoidance problem for dense LEO satellite systems is modeled as a non-convex optimization problem, and a particle swarm optimization (PSO) based method is proposed to obtain the optimal beam pointing of the user terminal (UT). Simulations show that the relative error of the mean signal-to-interference plus noise ratio (SINR) obtained by the proposed method is 0.51%, so the co-channel interference can be effectively mitigated for the dense LEO satellite communication system.

A Long-Range Signal-Based Target Localization Algorithm

Indoor target localization is pivotal across various applications, encompassing security monitoring, behavioral analysis, and elderly care. This work proposes an advanced target localization algorithm that harnesses antennas endowed with sensing capabilities to capture the phase change in the signal ratio, derived from the signal amplitude. This phase change, indicative of the target’s movement direction, is analyzed alongside the front and rear arrival angle information and signal amplitude characteristics obtained from LoRa signals. The algorithm, through a comprehensive examination of the phase change patterns, amalgamated with arrival angle data and signal amplitude characteristics, effectively estimates the precise location of the target. Experimental validations underscore the algorithm’s efficacy in determining the target’s location during continuous walking activity. Conducted within a 6 m × 12 m open platform, the algorithm achieves an average localization error of 48.5 cm, underscoring its superior performance compared to existing methodologies.

High-performance deep-sea long-range underwater acoustic communication: Deconvolved conventional beamforming based approach

To address the challenges posed by large propagation loss and severe multipath delay spread, while aiming to improve communication rates in long-range underwater acoustic (UWA) communication, this paper introduces a novel approach: multi-beam (MB) Orthogonal Frequency Division Multiplexing (OFDM) based on deconvolved conventional beamforming (dCv). The proposed method establishes a signal processing framework for UWA channel receivers, wherein received signals corresponding to each angle beam are isolated and concentrated using the dCv technique. Subsequently, the separated signals from each angle undergo maximal-ratio combining (MRC) channel equalization followed by demodulation. Compared to conventional single-beam (SB) processing methods, the proposed approach capitalizes on multipath diversity gain achieved by combining MB outputs originating from various arrival angles. Moreover, employing dCv processing demonstrates superior performance compared to popularly employed beamforming techniques, especially when dealing with paths arriving from closely aligned angles. Additionally, the array beamforming utilized significantly enhances the signal-to-noise ratio (SNR) of each beam output, mitigating the elevated error rates in channel estimation associated with combining low SNR signals received from individual elements. Simulation results utilizing BELLHOP and experimental data from the South China Sea showcase notably improved bit error rate (BER) performance for the proposed method compared to SB equalization, MB-MRC equalization based on conventional beamforming (CBF), minimum variance distortionless response (MVDR), and worst-case performance optimization (WCPO), as well as standard MRC equalization techniques. The proposed receiver achieves error-free decoding results at a communication distance of 80 km with a data rate of 247 bps.

Photonics-assisted simultaneous frequency and angle-of-arrival measurement using parameter mapping method

A photonics-assisted simultaneous frequency and angle of arrival (AOA) measurement system based on parameter mapping method is proposed and demonstrated by simulation and experiment. Using optical sideband sweeping and envelope detection, the frequencies of the signals under test (SUT) are mapped to the time domain of the output amplitude of the electrical pulses, and the AOA-dependent phase differences are mapped to the amplitudes of the output electrical pulses. The experiment achieved multiple-frequency signal measurements, and the absolute measurement error was between 1.8 MHz and 5.4 MHz in the range from 9.4 GHz to 13 GHz. The frequency range can be adjusted by modifying the bandwidth of the frequency-swept signal or the DC bias voltage. Furthermore, the unambiguous AOA measurement from −90° to 90° is successfully realized simultaneously, and the error of the AOA measurement ranging from −66.44° to 66.44° is confined to within ±1.4°. Due to its robustness and flexibility, this dual-function measurement system can find various applications in future electronic warfare systems.

Maximizing Electric Power Recovery through Advanced Compensation with MPPT Algorithms

The present investigation introduces an advanced methodology for maximum power point tracking (MPPT) applied to a piezo harvester scheme. A comprehensive rectifier circuit, equipped with an embedded MPPT component, is established to optimize energy production by monitoring a DC-DC inverter connected to the rectifier. Furthermore, the system’s sensitivity error has been finely tuned to dynamically adjust its impedance unit in real time, thereby optimizing load acquisition. This innovative approach seamlessly integrates the MPPT algorithm into the piezo harvester circuit. Moreover, the vehicle’s road handling is significantly augmented through the incorporation of a robust steering front and an active differential control system. Leveraging the MPPT module, the rectifier consistently achieves a power recovery efficiency exceeding 85%, independent of varying load conditions. Additionally, a DC-DC converter circuit has been seamlessly integrated to finely adjust the output voltage to meet specified levels. Numerical simulations demonstrate the effectiveness of the harvesting scheme, extracting a substantial output power of 90 W with an overall efficiency of 70%. The improved MPPT approach, employing angles of arrival (AoA) DV-Hop control strategies, minimizes the system’s power consumption based on the Global Positioning System (GPS). The utilization of Harris Hawks optimization (HHO) and the generation of quadrants in the four-quadrant operation mode of DC motors in the wireless sensor network (RCSFs) have been significantly enhanced in this study. Simulations reveal that, at a velocity of 50 km/h, shock absorbers utilizing the received signal strength indication (RSSI) can harvest between 60 and 90 W on a class C road, based on the time of arrival (TOA). Striking a balance in ride comfort using the time difference of arrival (TDOA) as a trade-off constitutes approximately 30% of the piezoelectric harvester (PEH) system’s power consumption when operating in active suspension mode, optimized by particle swarm optimization (PSO).

Multi-Subject 3D Human Mesh Construction Using Commodity WiFi

This paper introduces MultiMesh, a multi-subject 3D human mesh construction system based on commodity WiFi. Our system can reuse commodity WiFi devices in the environment and is capable of working in non-line-of-sight (NLoS) conditions compared with the traditional computer vision-based approach. Specifically, we leverage an L-shaped antenna array to generate the two-dimensional angle of arrival (2D AoA) of reflected signals for subject separation in the physical space. We further leverage the angle of departure and time of flight of the signal to enhance the resolvability for precise separation of close subjects. Then we exploit information from various signal dimensions to mitigate the interference of indirect reflections according to different signal propagation paths. Moreover, we employ the continuity of human movement in the spatial-temporal domain to track weak reflected signals of faraway subjects. Finally, we utilize a deep learning model to digitize 2D AoA images of each subject into the 3D human mesh. We conducted extensive experiments in real-world multi-subject scenarios under various environments to evaluate the performance of our system. For example, we conduct experiments with occlusion and perform human mesh construction for different distances between two subjects and different distances between subjects and WiFi devices. The results show that MultiMesh can accurately construct 3D human meshes for multiple users with an average vertex error of 4cm. The evaluations also demonstrate that our system could achieve comparable performance for unseen environments and people. Moreover, we also evaluate the accuracy of spatial information extraction and the performance of subject detection. These evaluations demonstrate the robustness and effectiveness of our system.

Range and depth estimates from array invariant and model inversion, compared with machine learning approach

The array invariant is derived from the waveguide invariant, and can be used to predict the shape of multipath arrival patterns on a 2D plane with axes of time and angle of arrival. The shape observed on a vertical line array is an ellipse that is a function of the waveguide invariant and the range. Once the range is obtained, ray tracing can be used to invert the multipath pattern for a depth estimate. We will compare results of using the physics-based range and depth estimator with results obtained by training a neural network on synthetic data produced by a ray tracer. We will produce two sets of exemplars to train our estimator. In one, we will provide the range as an input, and train the network to produce a depth. In the second, we will train the network to produce both range and depth. The method derived from the array invariant is expected to be brittle with respect to range-dependent bathymetry. We will compare how well the machine learning approach deals with such variability, compared to the array invariant approach.

An Underwater Source Localization Method Using Bearing Measurements.

Angle-of-arrival (AOA) measurements are often used in underwater acoustical localization. Different from the traditional AOA model based on azimuth and elevation measurements, the AOA model studied in this paper uses bearing measurements. It is also often used in the Ultra-Short Baseline system (USBL). However, traditional acoustical localization needs additional range information. If the range information is unavailable, the closed-form solution is difficult to obtain only with bearing measurements. Thus, a localization closed-form solution using only bearing measurements is explored in this article. A pseudo-linear measurement model between the source position and the bearing measurements is derived, and considering the nonlinear relationship of the parameters, a weighted least-squares optimization equation based on multiple constraints is established. Different from the traditional two-step least-squares method, the semidefinite programming (SDP) method is designed to obtain the initial solution, and then a bias compensation method is proposed to further minimize localization errors based on the SDP result. Numerical simulations show that the performance of the proposed method can achieve Cramer-Rao lower bound (CRLB) accuracy. The field test also proves that the proposed method can locate the source position without range measurements and obtain the highest positioning accuracy.

Source localization by Matching the Multipath Arrival Angles based on Sparse Bayesian Learning

In the deep-sea direct arrival region, a significant multipath characteristic is presented by the propagation of acoustic signals. This characteristic is closely linked to the distance and depth of the sound source and can be utilized for source localization. In this study, the changes in incidence angle characteristics of multipath signals at different distances and depths are analysed initially. Subsequently, the high-resolution azimuthal spectra are obtained using the Sparse Bayesian Learning (SBL) method. The azimuthal spectra are then matched with multiple incidence angles using the Gaussian kernel function, facilitating the localization of sound sources. Throughout this paper, the impacts of different distance, depth, and SNR conditions on the model are assessed through simulations. Furthermore, the model’s validity is confirmed by utilizing experimental data from an explosion sound source.

On the Generalization of Deep Learning Models for AoA Estimation in Bluetooth Indoor Scenarios

Indoor positioning remains as one of the major challenges of the Internet of Things (IoT), where assets are usually deployed over environments where the Global Navigation Satellite Systems (GNSS) are denied. Angle-of-arrival (AoA) estimation techniques are among the most prominent candidates to address this challenge and multiple techniques based on conventional signal processing, such as the Multiple Signal Classification (MUSIC) algorithm, have been extensively studied. However, they require a deep knowledge of the elements involved in the AoA system, such as the characterization of the receivers’ antennas. On the other hand, Deep Learning (DL) models are considered as promising solutions, as they can provide AoA estimations without resorting to a deep knowledge of the physical system. Instead, they only need to be trained with labeled AoA signal measurements. Nevertheless, in the training process, non-desired effects like overfitting appear, yielding models with poor generalization capabilities. Although these models have already been considered in the literature, the problem of DL models generalization has not been addressed in depth. Therefore, this manuscript first compares the performance of DL models to the MUSIC algorithm as a baseline, to then analyze their generalization to different situations. These include changing the position of the receivers within the same area, considering measurements at different time instants and deployments in new scenarios, specifically in locations different from the training one. The assessment showed that the generalization of the DL models is weak compared to the MUSIC algorithm, especially when applied to environments not previously observed.