Angle Of Arrival Research Articles

Sonic boom propagation in urban canyons using a combined ray tracing/radiosity method.

Sonic boom propagation over land is being studied to ensure a minimal impact on the population in the event that supersonic flight over land becomes permissible. Sonic boom behavior in and around urban areas is particularly important due to the high density of people residing in cities and the increased probability of impacting large numbers of people if urban areas lie in the flight path. This study models sonic booms around urban canyons using a combined ray tracing/radiosity method. The impacts of several parameters, such as diffusion, arrival angle, building heights, and canyon width, on the sound field were explored. The results showed that without diffusion, there was no real trend as the canyon height and width and arrival angles were varied. There were strong shadow zones where no sound was detected as this model does not include diffraction. In other locations, between one and four boom events were received. The resulting perceived level in dB (PLdB) varied from 0 in the shadow zones to 94 dB. Diffuse reflections was the parameter that had the greatest impact on the resulting signatures. Parameters that caused an increased number of reflections showed significant changes in the signature shape and a dramatic reduction in the PLdB.

GMM-based GNSS spoofing detector using double differential phase measurement

GNSS spoofing is an intentional interference wherein a GNSS receiver tracks counterfeit signals, resulting in incorrect outcome positions. This is the most dangerous type of intervention in the GNSS technology. In this study, we introduce a novel method for detecting spoofing signals using a Gaussian mixture model (GMM) on the double-carrier phase difference (DD) created by two independent receivers. The DD can completely eliminate errors caused by the satellite clock, receiver clock, and atmospheric layers; hence, the signal angle of arrival (AoA) is clearly expressed in the DD measurement. We utilized the GMM to model the probability density function of the DD measurement computed from the phase measurement of the receivers. Theoretically, AoA values of an authentic signal change over time owing to the nature of the signal broadcast from an orbiting satellite. However, fake signals are often transmitted from the generator, resulting in a central distribution of the corresponding AoA values. Existing studies deal with the spoofing detection problem using the above theoretical assumptions. However, it is practical to broadcast spoofing signals from several sources, and random noise can be mixed into the generated phase to render the DD measurement noisy. In such complicated scenarios, existing approaches are not sufficiently robust to detect non-authentic signals. Another observation is that because real satellites are moving in fixed orbits, there should be a correlation among the AoA values of the signals coming from these satellites. In contrast, counterfeit signals (with the main purpose of causing the wrong position or noisy phase) do not follow the pattern of the real signals. Therefore, instead of using the above hard assumption about the signal AoA, we propose a statistical model GMM to learn the hidden relationship of the AoA values among real signals from different real satellites, which are then used to detect spoofing signals.

Enhancing Location Accuracy by Minimizing RMS Using RSS-AMLE in WSN

Over the past decade, there has been significant growth in wireless sensor networks, particularly in the context of industrial applications. Mobile sensor networks have garnered research interest due to their ability to facilitate communication between various devices. Still, the mobility of these nodes gives rise to challenges such as network coverage and connectivity issues. Addressing these challenges necessitates accurate estimation of sensor node locations, a critical factor in network performance. Numerous methods, such as Angle of Arrival (AOA) and Time of Arrival (TOA), have been proposed for node localization. Still, these methods are plagued by localization errors and high implementation costs. To overcome these localization errors in wireless sensor networks, we present an adaptive approach based on the Received Signal Strength (RSS) model. This model views localization as a non-convex problem and employs an adaptive maximum likelihood estimation to minimize localization errors. An extensive simulation study is carried out to measure the performance of the intended approach in minimizing the localization error. The results unequivocally demonstrate that our localization scheme achieves higher accuracy in locating sensor nodes while reducing deployment costs. Comparative analysis against existing methods further underscores the significance of our approach.

Inferring Helmet Compliance on E-Scooters Using Bluetooth and Angle of Arrival (AoA) Technology

Inferring Helmet Compliance on E-Scooters Using Bluetooth and Angle of Arrival (AoA) Technology

DeepEar: Sound Localization With Binaural Microphones

The binaural microphone, which refers to a pair of microphones with artificial human-shaped ears, is widely used in hearing aids and spatial audio recording to improve sound quality. It is crucial for such devices to find the voice direction in many applications such as binaural sound enhancement. However, sound localization with two microphones remains challenging, especially in multi-source scenarios. Most previous work utilized microphone arrays to deal with the multi-source localization problem. Extra microphones yet have space constraints for deployment in many scenarios ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</i> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</i> ., hearing aids). Inspired by the fact that humans have evolved to locate multiple sound sources with only two ears, we propose DeepEar, a binaural microphone-based sound localization system. To this end, we design a multisector-based neural network to locate multiple sound sources simultaneously, where each sector is a discretized region of the space for different angle of arrivals. DeepEar fuses explicit hand-crafted features and implicit latent sound representatives to facilitate sound localization. More importantly, the trained DeepEar model can adapt to new environments with a minimum amount of extra training data. The experiment results show that DeepEar substantially outperforms the state-of-the-art binaural deep learning approach by a large margin in terms of sound detection accuracy and azimuth estimation error.

A Simple Approach for Simultaneous Measurement of Instantaneous Frequency and Angle of Arrival

A Simple Approach for Simultaneous Measurement of Instantaneous Frequency and Angle of Arrival

Wideband Anti-Interference Microwave Photonic Measurement for Doppler Frequency Shift and Angle of Arrival

Wideband Anti-Interference Microwave Photonic Measurement for Doppler Frequency Shift and Angle of Arrival

Transmitter Precoder Design to Improve the Performance of the MUSIC Algorithm

Using the MUSIC (MUltiple SIgnal Classification) algorithm to estimate the angle-of-arrival (AoA), we derive a new asymptotic error variance bound when the transmitted signal can be pre-processed. We next propose a precoder design to achieve this bound. However, such an optimal precoder requires channel state information at the transmitter (CSIT) exclusive of the receiver array which cannot be separately estimated practically. A more feasible precoder design, which leverages on the feedback CSIT estimated at the receiver, is next proposed. Using the performance of the optimal precoder which achieves the bound asymptotically as a benchmark, the practical precoder design performs closely to the optimal precoder even in high-resolution scenario. Both precoder schemes exhibit performance improvement compared with the case when no precoder is used.

Синтез поляризационных светоделителей для дальнего инфракрасного диапазона спектра

The technique and results of the synthesis of polarization beam splitters for the long-wavelength infrared region of the spectrum of 8–14 microns are presented. In this range, bodies with temperatures around zero degrees Celsius begin to emit. In addition, this radiation has a low attenuation in a relatively humid or aerosol-saturated atmosphere. Therefore, it is widely used in night vision devices, rangefinders and thermal imaging cameras, including mobile systems with a variable angle of the optical axis relative to the angle of arrival of the incident electromagnetic wave. In systems with active illumination, polarized radiation and beam splitters are used to reduce the effect of backscattering interference. A review of patents and literature showed the absence of finished products for the considered wavelength range. In this paper, original designs of polarization beam splitters are proposed and their spectral optical characteristics are obtained.

Deep learning aided multi-source passive 3D AOA wireless positioning using a moving receiver: A low complexity approach

Passive localization of multiple sources while the receiver is in motion is a fundamental problem in wireless communication with practical applications in various fields such as acoustic event localization and object tracking. In this study, we propose a novel method for three dimensional (3D) angle of arrival (AOA) localization using a moving receiver with a limited number of sensors. In the proposed method, we record signals from sources in a series of time windows. Then we estimate the elevation and azimuth AOAs of sources along with their pairing by using the proposed three low-complexity deep learning (DL)-based AOA estimators. Following the initial AOA estimation, a low-complexity off-grid based refinement technique is employed to further enhance the accuracy of the estimated elevation and azimuth AOAs based on the maximum likelihood (ML) criterion. Next, a multi-source 3D-localization algorithm is proposed to estimate the positions of sources across the recorded time windows. The simulation results validate the effectiveness and efficiency of the proposed method. The results highlight that the DL-based AOA and location estimators outperform recent studies, while also demonstrating robustness in the face of practical imperfections, such as situations where the receiver’s position or direction is uncertain during movement. Our complexity analysis verifies that the proposed method outperforms existing methods in terms of speed and the number of computations. This enhanced computational efficiency makes the proposed method highly attractive and practically significant for application in various fields.

Software Defined Radio for GNSS Radio Frequency Interference Localization.

The use of radio direction finding techniques in order to identify and reject harmful interference has been a topic of discussion both past and present for signals in the GNSS bands. Advances in commercial off-the-shelf radio hardware have led to the development of new low-cost, compact, phase coherent receiver platforms such as the KrakenSDR from KrakenRF whose testing and characterization will be the primary focus of this paper. Although not specifically designed for GNSSs, the capabilities of this platform are well aligned with the needs of GNSSs. Testing results from both benchtop and in the field will be displayed which verify the KrakenSDR's phase coherence and angle of arrival estimates to array dependent resolution bounds. Additionally, other outputs from the KrakenSDR such as received signal strength indicators and the angle of arrival confidence values show strong connections to angle of arrival estimate quality. Within this work the testing that will be primarily presented is at 900 MHz, with results presented from a government-sponsored event where the Kraken was tested at 1575.42 MHz. Finally, a discussion of calibration of active antenna arrays for angle of arrival is included as the introduction of active antenna elements used in GNSS signal collection can influence angle of arrival estimation.

BUILDING HEAT SUPPLY SYSTEM BASED ON HYBRID SOLAR COLLECTORS

Increasing the efficiency of solar heat supply systems is one of the important problems of solar energy. The research presented in this article is aimed at improving the efficiency of hybrid solar collectors without a transparent coating for building heating systems. One of the key challenges in the field of solar energy is the development of new technologies to ensure high collection of solar energy and to integrate it into traditional heating and hot water systems. The study shows that hybrid solar collectors with the placement of heat carrier circulation circuit tubes above the heat absorber can increase the thermal efficiency factor with a certain change in the angle of inclination and the density of solar radiation. A nomogram was also developed that determines the dependence of this coefficient on the angles of arrival of solar radiation and its density.

RLoc

In recent years, decimeter-level accuracy in WiFi indoor localization has become attainable within controlled environments. However, existing methods encounter challenges in maintaining robustness in more complex indoor environments: angle-based methods are compromised by the significant localization errors due to unreliable Angle of Arrival (AoA) estimations, and fingerprint-based methods suffer from performance degradation due to environmental changes. In this paper, we propose RLoc, a learning-based system designed for reliable localization and tracking. The key design principle of RLoc lies in quantifying the uncertainty level arises in the AoA estimation task and then exploiting the uncertainty to enhance the reliability of localization and tracking. To this end, RLoc first manually extracts the underutilized beamwidth feature via signal processing techniques. Then, it integrates the uncertainty quantification into neural network design through Kullback-Leibler (KL) divergence loss and ensemble techniques. Finally, these quantified uncertainties guide RLoc to optimally leverage the diversity of Access Points (APs) and the temporal continuous information of AoAs. Our experiments, evaluating on two datasets gathered from commercial off-the-shelf WiFi devices, demonstrate that RLoc surpasses state-of-the-art approaches by an average of 36.27% in in-domain scenarios and 20.40% in cross-domain scenarios.

Modelling the elevation power angle spectrum of a MIMO channel

&lt;p&gt;Multiple-Input Multiple-Output (MIMO) channel modelling is constantly researched, and the innovation of its three-dimensional (3D) models is the introduction of the elevation domain parameters, in particular the elevation angle of arrival (EOA) and elevation angle of departure (EOD) where their power angle spectrum (PAS) and their angular spread (AS) greatly impact the 3D MIMO performance. PAS is the basic characteristic used to estimate and model angular dispersion in wireless channels. Propagating signal waves are constrained into a path having an angle within which the power is contained. One of the scenes that will have a significant effect on these parameters is the relative height between the BS and the building. They will also depend on the distance between the base station (BS) and the user equipment (UE). In this work, we investigate the effect of distance and height on the power angle spectrum of a street canyon and a high-rise scenario in the urban environment. The wireless Insite X3D ray tracing engine and a 3D digital map of the environment were used for the simulation. It is seen that the elevation power spectrum (EPS) in the elevation domain decreases with distance and height in both scenarios for the arrival and departure. The appearance of multiple peaks leads to a double-normal distribution in the arrival of the high-rise as height increases, which is a result of reflections from multiple clusters. The azimuth power spectrum (APS) in the azimuth domain also decreases with distance and height at the arrival but increases with distance and height at the departure. The result of the power angle spectrum in this work can be used for modelling angular dispersion as well as designing adaptive antennas for wireless communication.&lt;/p&gt;

Study of convective air turbulence based on the probability distribution function of angle of arrival fluctuations

This study investigates the probability density function (PDF) of angle of arrival (AoA) fluctuations of a laser beam propagating through convective air turbulence in two settings: indoor turbulence and atmospheric surface layer. The indoor turbulence is generated by an electric heater with a 50 cm × 100 cm surface area placed horizontally on the bottom. The atmospheric turbulence experiments are conducted over a level at 60 cm height from an asphalted area using a slightly divergent laser beam that propagates along 350 m. Image motion monitoring of four sub-apertures installed in front of the receiving telescope aperture is used to record the AoA fluctuations and their PDFs over the entrance pupil at the sub-aperture locations for both types of turbulence. The observations are made in two perpendicular directions to the propagation of the laser beam. The experimental PDFs of the AoA fluctuations are fitted with Gaussian, Lognormal, Weibull, and Gamma distribution functions. The results show that the Weibull and Gamma models are more consistent with the experimental data for the indoor convective air and atmospheric turbulence respectively. For the indoor convective turbulence, the full width at half maximum and the peak location of the measured PDFs increase with the heater temperature, as well as the goodness of fit, which saturates at high temperatures.

A weather-dependent UAV relay-assisted hybrid FSO/RF airborne communication system

High-altitude airborne platforms inter-connected by free-space optical (FSO) links have been widely envisioned as a promising aeronautical network architecture. The use of unmanned aerial vehicles (UAVs) as relays was proposed to significantly increase the availability for airborne optical communication links. In this work, an additional UAV as a relay is deployed to connect the FSO link from the airborne platform to the ground station to avoid cloud blockage, presenting a novel solution for the hybrid FSO/RF airborne communication under the influence of adverse weather conditions. Further, a weather-dependent link switching scheme is proposed. Additionally, we derive closed-form expressions for the outage probability and bit error rate (BER) of the considered system, under the combined effects of atmospheric attenuation, turbulence, pointing error, and the angle-of-arrival (AOA) due to the hovering UAV. Numerical results quantitatively confirm the effectiveness of our proposal under different weather conditions. Finally, effective design guidelines that can be useful for system designers are presented.

A hybrid RSSI and AoA indoor positioning approach with adapted confidence evaluator

Aiming at the indoor positioning system of hybrid received signal strength indicator (RSSI) and angle of arrival (AoA), this paper proposes an indoor positioning algorithm with an adaptive confidence based multi-objective optimization evaluator (ACMOOE) which improves the positioning accuracy by adjusting the influence of the two positioning methods adaptively. A multi-objective optimization algorithm based positioning model is established and particle swarm optimization is applied to reduce the positioning accuracy loss caused by the approximate transformation process. An adaptive confidence evaluation method of RSSI and AoA target is designed, which reduces the positioning accuracy loss caused by unreasonable weight setting. Finally, in order to verify the proposed algorithm, an indoor wireless sensing system is built in the actual indoor scene. Experimental results show that compared with the traditional hybrid positioning algorithm, the positioning error of the proposed ACMOOE is 0.45 m which improves the positioning accuracy by 18.7%.

IRS-Assisted Anti-Jamming Transmission for an Integrated Satellite-UAV-Terrestrial Network With Imperfect CSI: A Game-Based Perspective

In this paper, an intelligent reflecting surface (IRS)-assisted integrated satellite-unmanned aerial vehicle (UAV)-terrestrial (SUT) Internet of Things (IoT) network faced with a smart jammer under imperfect channel state information (CSI) conditions is considered. We propose a Stackelberg game model to describe the adversarial relationship between the satellite, UAV, and IRS and the jammer, which are modeled as the leader and the follower, respectively. For the follower subgame, the jammer aims to minimize the jamming power while guaranteeing that the jamming energy efficiency surpasses a certain threshold. Under this setup, the angle of arrival (AoA)-based discretization method is utilized to address the imperfect CSI issue. Then, the use-and-then-forget method and the Lagrangian function are employed to obtain a closed-form expression for the jammer’s power. Finally, a feasible jamming power solution is obtained by means of the Cauchy-Schwarz inequality. For the leader subgame, we aim to optimize the hybrid beamforming design of the satellite, UAV and IRS, with the goal of minimizing the total transmit power, while guaranteeing that the downlink received signal-to-interference-plus-noise ratio (SINR) surpasses the minimum communication threshold. We propose an alternating optimization scheme, in which the Cauchy-Schwarz inequality, the AoA-based discretization method, and nonsmooth penalty functions are employed to alternately obtain the optimal satellite beamforming vector, UAV beamforming vector and IRS phase matrix when the other variables are fixed. Through our analytical and numerical results, the proposed beamforming scheme achieves a reduction of 16.9% in average power consumption compared with other benchmark schemes when obtaining the same anti-jamming performance.

A radar extended target angle estimation method based on effective phase clustering

AbstractFor high‐resolution radar, traditional direction of arrival (DOA) angle measuring algorithms for point‐like targets cannot be directly applied to extended targets. Instead, pixel‐based detection and clustering must be performed first, followed by angle estimation. However, the presence of factors such as clutter and system noise makes it impossible to guarantee a one‐to‐one correspondence between the multiple scattering points of an extended target and the real target, which may result in significant discrepancies between the angle estimation and localization results and the real target. To address this issue, this paper proposes and discusses a radar extended target arrival angle estimation method based on effective phase clustering. The proposed method selects effective target scatterers through the interferometric phase information between channels, clusters these points, and then estimates the DOA angle of the extended target, effectively reducing the impact of interference scatterers on DOA angle estimation and improving the angular measurement accuracy. The effectiveness of the proposed method was validated by the measured data obtained from an airborne digital beamforming radar system.

TDLoc: Passive Localization for MIMO-OFDM System via Tensor Decomposition

Passive localization is an important aspect of integrated sensing and communication (ISAC). However, it is challenging to estimate the target position and velocity accurately from the receiving signals due to complex multipath propagation. This paper presents TDLoc, a multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) based passive localization and tracking system, using channel state information (CSI). We first proposed a fourth-order tensor model that contains angle-of-departure (AoD), angle-of-arrival (AoA), time-of-flight (ToF), and Doppler frequency shifts (DFS) information, followed by developing an efficient joint estimation algorithm. We also show that with more than one pair of transceivers, our method can obtain the target velocity from the relativistic Doppler effects, leading to additional DFS-based trajectory information. Moreover, the Cramér-Rao lower bound (CRLB) for multipath parameter estimation and positioning is derived for performance evaluation. Numerical results show that TDLoc outperforms state-of-the-art methods in terms of localization accuracy.