Angle Of Arrival Research Articles

Data-Aided Maximum Likelihood Joint Angle and Delay Estimator Over Orthogonal Frequency Division Multiplex Single-Input Multiple-Output Channels Based on New Gray Wolf Optimization Embedding Importance Sampling.

In this paper, we propose a new data-aided (DA) joint angle and delay (JADE) maximum likelihood (ML) estimator. The latter consists of a substantially modified and, hence, significantly improved gray wolf optimization (GWO) technique by fully integrating and embedding within it the powerful importance sampling (IS) concept. This new approach, referred to hereafter as GWOEIS (for "GWO embedding IS"), guarantees global optimality, and offers higher resolution capabilities over orthogonal frequency division multiplex (OFDM) (i.e., multi-carrier and multi-path) single-input multiple-output (SIMO) channels. The traditional GWO randomly initializes the wolfs' positions (angles and delays) and, hence, requires larger packs and longer hunting (iterations) to catch the prey, i.e., find the correct angles of arrival (AoAs) and time delays (TDs), thereby affecting its search efficiency, whereas GWOEIS ensures faster convergence by providing reliable initial estimates based on a simplified importance function. More importantly, and beyond simple initialization of GWO with IS (coined as IS-GWO hereafter), we modify and dynamically update the conventional simple expression for the convergence factor of the GWO algorithm that entirely drives its hunting and tracking mechanisms by accounting for new cumulative distribution functions (CDFs) derived from the IS technique. Simulations unequivocally confirm these significant benefits in terms of increased accuracy and speed Moreover, GWOEIS reaches the Cramér-Rao lower bound (CRLB), even at low SNR levels.

Simultaneous frequency and angle-of-arrival measurement of microwave signals utilizing the Talbot effect and pulse interference

We propose and demonstrate a simple and efficient scheme for simultaneous frequency and angle-of-arrival (AOA) measurement of microwave signals using the Talbot effect and pulse interference. This method maps the frequency of the microwave signal to the time intervals of output pulses through the Talbot effect, using a specialized sampling and dispersive structure. Concurrently, the phase difference related to the AOA is encoded onto the relative amplitudes of the output pulses via pulse interference. By analyzing both the time intervals and relative amplitudes of the output pulses, precise frequency and AOA values of the microwave signal are simultaneously derived. Simulation results indicate that the system can perform real-time, gap-free frequency and AOA measurements of multiple signals. Proof-of-concept experiments show this method achieves high accuracy, with AOA measurement errors within ±2.9° over a range of −63° to 63°, and frequency measurement errors within ±60 MHz for frequency bands of 2–3 GHz and 12–13 GHz.

Robust algorithms for spherical angle-of-arrival source localization

The performance of traditional algorithms for spherical angle-of-arrival (AOA) source localization will be significantly degraded when there are outliers in the angle measurements. By using the symmetric α-stable (SαS) distribution to describe the measurement noise containing outliers and constructing the cost function using the lp-norm, we propose a robust algorithm for spherical AOA source localization: the spherical iteratively reweighted pseudolinear estimator (SIRPLE). The SIRPLE is similar to the iteratively reweighted least squares (IRLS), with the difference that a homogeneous least squares (HLS) problem is solved in each iteration. The SIRPLE suffers from bias problems owing to the nature of the pseudolinear estimators. To overcome this problem, the instrumental variable (IV) method is introduced and the spherical iteratively reweighted instrumental variable estimator (SIRIVE) is proposed. Theoretical analysis shows that the SIRIVE is asymptotically unbiased and it can achieve the theoretical error covariance of the constrained least lp-norm estimation. Extensive simulation analyses demonstrate the better performance of the SIRIVE compared to the conventional spherical AOA source localization methods and the SIRPLE under SαS noise environment. The performance of the SIRIVE is similar to that of the Nelder–Mead algorithm (NM), but the SIRIVE are computationally more efficient. In addition, the SIRIVE is nearly unbiased and the root mean square error (RMSE) performance is close to the Cramér–Rao lower bound (CRLB).

A Dual-Branch Convolutional Neural Network-Based Bluetooth Low Energy Indoor Positioning Algorithm by Fusing Received Signal Strength with Angle of Arrival

Indoor positioning is the key enabling technology for many location-aware applications. As GPS does not work indoors, various solutions are proposed for navigating devices. Among these solutions, Bluetooth low energy (BLE) technology has gained significant attention due to its affordability, low power consumption, and rapid data transmission capabilities, making it highly suitable for indoor positioning. Received signal strength (RSS)-based positioning has been studied intensively for a long time. However, the accuracy of RSS-based positioning can fluctuate due to signal attenuation and environmental factors like crowd density. Angle of arrival (AoA)-based positioning uses angle measurement technology for location devices and can achieve higher precision, but the accuracy may also be affected by radio reflections, diffractions, etc. In this study, a dual-branch convolutional neural network (CNN)-based BLE indoor positioning algorithm integrating RSS and AoA is proposed, which exploits both RSS and AoA to estimate the position of a target. Given the absence of publicly available datasets, we generated our own dataset for this study. Data were collected from each receiver in three different directions, resulting in a total of 2675 records, which included both RSS and AoA measurements. Of these, 1295 records were designated for training purposes. Subsequently, we evaluated our algorithm using the remaining 1380 unseen test records. Our RSS and AoA fusion algorithm yielded a sub-meter accuracy of 0.79 m, which was significantly better than the 1.06 m and 1.67 m obtained when using only the RSS or the AoA method. Compared with the RSS-only and AoA-only solutions, the accuracy was improved by 25.47% and 52.69%, respectively. These results are even close to the latest commercial proprietary system, which represents the state-of-the-art indoor positioning technology.

Performance analysis and experimental evaluation of modulating retroreflector and UAV based free space optical transmission systems.

This paper introduces a novel unmanned aerial vehicle (UAV) based free space optical (FSO) communication system that integrates differential detection with a modulating retro-reflector (MRR) to enhance performance. We provide a comprehensive analysis of the system's performance, taking into account critical factors including atmospheric path loss, non-zero boresight pointing error, atmospheric turbulence, and angle of arrival (AoA) fluctuations induced by UAV orientation fluctuations. To facilitate this analysis, we have derived the probability density function (PDF) and cumulative distribution function (CDF) for the end-to-end signal-to-noise ratio (SNR) under these channel conditions. Our study presents new closed-form solutions for pivotal performance metrics: outage probability (OP), average bit error rate (ABER), and ergodic capacity (EC), which are corroborated through Monte Carlo simulations. Additionally, we perform asymptotic analyses to gain deeper insights into the behavior of OP, ABER, and EC. A pioneering experimental setup has been constructed to empirically validate the communication performance of our proposed system. The numerical findings underscore the presented system's feasibility and the significant impact of atmospheric turbulence and pointing errors on system performance. Moreover, our experimental results indicate that the proposed system outperforms current UAV-based MRR FSO systems under identical conditions.

Real-Time Indoor Visible Light Positioning (VLP) Using Long Short Term Memory Neural Network (LSTM-NN) with Principal Component Analysis (PCA).

New applications such as augmented reality/virtual reality (AR/VR), Internet-of-Things (IOT), autonomous mobile robot (AMR) services, etc., require high reliability and high accuracy real-time positioning and tracking of persons and devices in indoor areas. Among the different visible-light-positioning (VLP) schemes, such as proximity, time-of-arrival (TOA), time-difference-of-arrival (TDOA), angle-of-arrival (AOA), and received-signal-strength (RSS), the RSS scheme is relatively easy to implement. Among these VLP methods, the RSS method is simple and efficient. As the received optical power has an inverse relationship with the distance between the LED transmitter (Tx) and the photodiode (PD) receiver (Rx), position information can be estimated by studying the received optical power from different Txs. In this work, we propose and experimentally demonstrate a real-time VLP system utilizing long short-term memory neural network (LSTM-NN) with principal component analysis (PCA) to mitigate high positioning error, particularly at the positioning unit cell boundaries. Experimental results show that in a positioning unit cell of 100 × 100 × 250 cm3, the average positioning error is 5.912 cm when using LSTM-NN only. By utilizing the PCA, we can observe that the positioning accuracy can be significantly enhanced to 1.806 cm, particularly at the unit cell boundaries and cell corners, showing a positioning error reduction of 69.45%. In the cumulative distribution function (CDF) measurements, when using only the LSTM-NN model, the positioning error of 95% of the experimental data is >15 cm; while using the LSTM-NN with PCA model, the error is reduced to <5 cm. In addition, we also experimentally demonstrate that the proposed real-time VLP system can also be used to predict the direction and the trajectory of the moving Rx.

ReLoki: A Light-Weight Relative Localization System Based on UWB Antenna Arrays.

Ultra Wide-Band (UWB) sensing has gained popularity in relative localization applications. Many localization solutions rely on using Time of Flight (ToF) sensing based on a beacon-tag system, which requires four or more beacons in the environment for 3D localization. A lesser researched option is using Angle of Arrival (AoA) readings obtained from UWB antenna pairs to perform relative localization. In this paper, we present a UWB platform called ReLoki that can be used for ranging and AoA-based relative localization in 3D. To enable AoA, ReLoki utilizes the geometry of antenna arrays. In this paper, we present a system design for localization estimates using a Regular Tetrahedral Array (RTA), Regular Orthogonal Array (ROA), and Uniform Square Array (USA). The use of a multi-antenna array enables fully onboard infrastructure-free relative localization between participating ReLoki modules. We also present studies demonstrating sub-50cm localization errors in indoor experiments, achieving performance close to current ToF-based systems, while offering the advantage of not relying on static infrastructure.

Impact of Antenna Orientation on Localization Accuracy Using RSSI-based Trilateration

The goal of the indoor localization is to determine the position and orientation of people, devices, and mobile robots. With the rise of Industry 4.0, wireless communication technologies have emerged as a rapidly evolving and crucial area for achieving this goal. Various radiocommunication-based technologies, including Bluetooth, Bluetooth Low Energy (BLE), Wi-Fi, Ultra-Wideband (UWB), and ZigBee offer means to indirectly estimate distance. These methods leverage diverse principles such as time-based measurements, signal strength, and angle of arrival. Indoor positioning systems can be categorized into two approaches: distance-based and distance-independent techniques. The Free Space Path Loss (FSPL) model describes the connection between distance and Received Signal Strength Indicator (RSSI). The parameters within this model significantly impact distance estimation and localization accuracy. Therefore, a method that accurately characterizes the model is critical. This work proposes an orientation-based localization technique utilizing RSSI and trilateration. Measurements were conducted between two ESP32 units in various orientations to obtain optimal parameters for each specific scenario. To assess the effectiveness of this approach, two scenarios were evaluated: one considering orientation and another neglecting it. The results show that incorporating orientation information leads to significantly more accurate positioning compared to the orientation-agnostic approach.

A location fingerprinting approach for the automated radio telemetry of wildlife and comparison to alternative methods

BackgroundAutomated radio telemetry (ART) systems enable high-temporal resolution data collection for species unsuited to satellite-based methods. A challenge of ART systems is estimating the location of radio tagged animals from the radio signals received on multiple antennas within an ART array. Localisation methods for ART systems with omni-directional receivers have undergone rapid development in recent years, with the inclusion of machine learning techniques. However, comparable machine learning methods for ART systems with directional antennas are unavailable, despite their potential for improved accuracy and greater versatility. To address this, we introduce an open-source machine learning-based location fingerprinting method for directional antenna-based ART systems. We compare this method to two alternative localisation approaches. Both alternatives use relative signal strengths recorded among multiple antennas to estimate the signal’s angle of arrival at each receiver. In the ‘biangulation’ approach, the location is estimated by finding the intersection of these angles from two receivers. In contrast, the ‘linear regression’ approach uses a linear regression model to estimate the distance from the receiver along the angle of arrival, providing a location estimate. We evaluate these methods using an ART data set collected for the southern black-throated finch (Poephila cincta cincta), in the Desert Uplands Bioregion of Queensland, Australia.ResultsThe location fingerprinting method performed slightly better than the best performing alternative, the linear regression method, with mean positional errors of 308 m (SE = 17.7) and 335 m (SE = 18.5), respectively. The biangulation method performed substantially worse, with a mean positional error of 550 m (SE = 42.9, median = 540 m). Improved accuracy was observed with shorter distances between transmitters and receivers, higher signal strengths, and a greater number of detecting receivers, suggesting that increasing receiver density improves localisation accuracy, albeit with potential trade-offs in system coverage or cost. Furthermore, shorter pulse intervals of transmitters resulted in greater accuracy, highlighting the trade-offs among battery life, transmitter weight and radiative power.ConclusionsThe open-source location fingerprinting method offers an improved and versatile localisation approach suitable for a wide variety of ART system designs, addressing the challenge of developing study-specific localisation methods using alternative approaches.

Adapting UWB AoA estimation towards unseen environments using transfer learning and data augmentation

Ultra-wideband technology has become increasingly prevalent in localization systems, particularly with the emergence of multi-antenna systems capable of estimating both distance and angle of arrival (AoA) for incoming signals. However, most scientific research analyzes the accuracy of AoA in one specific environment for which the estimator is trained. In this paper, we analyze the performance of various AoA estimation algorithms, such as deep convolutional neural networks (DCNN), multiple signal classification (MUSIC), and phase difference of arrival (PDoA), in unseen environments. Prior work already demonstrated the superior performance of ML for AoA estimation compared to PDoA or MUSIC. We show that MUSIC, PDoA and ML solutions suffer from degradation in unseen environments at the 90th percentile of error, with ML-based AoA estimation degrading by about 14 degrees in unseen environments compared to 4 degrees for PDoA. We demonstrate that while PDoA more effectively corrects AoA at the median level in unseen environments, ML-based methods excel at correcting higher-percentile AoA errors, including outliers. Finally, we propose a novel framework to further improve the correction of AoA outliers for DCNN-based AoA estimators using data augmentation and transfer learning, resulting in a median angular error of only 5 degrees in unseen environments, even considering a field of view up to 90 degrees.

Robust Bluetooth AoA Estimation for Indoor Localization Using Particle Filter Fusion

With the growing demand for positioning services, angle-of-arrival (AoA) estimation or direction-finding (DF) has been widely investigated for applications in fifth-generation (5G) technologies. Many existing AoA estimation algorithms only require the measurement of the direction of the incident wave at the transmitter to obtain correct results. However, for most cellular systems, such as Bluetooth indoor positioning systems, due to multipath and non-line-of-sight (NLOS) propagation, indoor positioning accuracy is severely affected. In this paper, a comprehensive algorithm that combines radio measurements from Bluetooth AoA local navigation systems with indoor position estimates is investigated, which is obtained using particle filtering. This algorithm allows us to explore new optimized methods to reduce estimation errors in indoor positioning. First, particle filtering is used to predict the rough position of a moving target. Then, an algorithm with robust beam weighting is used to estimate the AoA of the multipath components. Based on this, a system of pseudo-linear equations for target positioning based on the probabilistic framework of PF and AoA measurement is derived. Theoretical analysis and simulation results show that the algorithm can improve the positioning accuracy by approximately 25.7% on average.

ULiDR: An inertial-assisted unmodulated visible light positioning system for smartphone-based pedestrian navigation

Visible Light Positioning (VLP) techniques have attracted considerable attention owing to their cost-effectiveness, high precision, and ubiquitous infrastructure. However, conventional VLP demands supplementary hardware for Light Emitting Diode (LED) modulation, which entails retrofitting existing indoor light systems for positioning applications. Thus, it is costly for wide-scale adoption. Moreover, the built-in Photodiodes (PDs) in smartphones can only provide a limited sampling rate (around 10 Hz), making it challenging to identify LEDs and extract each LED’s light intensity by Frequency Division Multiplexing and Fast Fourier Transform. To alleviate these problems, we develop a novel inertial-aided Unmodulated Visible Light Positioning system (uLiDR) for resource-constrained platforms like smartphones. Firstly, we evaluate the feasibility of unmodulated LED for precise ranging using smartphones. Next, we introduce a selective fusion strategy to mitigate LED occlusion in pedestrian navigation. As slight tilts of the phone are unavoidable during walking, we leverage attitude information calculated by the built-in sensors to handle the phone’s tilt in VLP. Then, we implement the tightly-coupled integration of uVLP and Pedestrian Dead Reckoning (PDR) in an optimization-based framework. The initial location is vital for PDR, and thereby, we proposed an Angle of Arrival (AoA)-based method to provide a precise initial location estimation for PDR. Experimental results show that the proposed uLiDR can achieve precise pedestrian navigation and effectively suppress the error accumulation of PDR.

Numerical model of the QiTai radio Telescope PAF receiver signal and simulation of interference mitigation

Xinjiang Qitai is constructing a 110-m fully steerable radio telescope (QiTai radio Telescope [QTT]) equipped with a phased array feeds (PAF) receiver, which will install the focal plane operating from 0.7 to 1.8 GHz. In this article, we introduce a PAF receiver model for beamforming, and the numerical model of the internal and external noise for this PAF receiver is provided using electromagnetic field simulation software. The linear constraint minimum variance (LCMV) algorithm is used to simulate the interference mitigation. The interference mitigation rate is from 0.581 to 0.921, and the signal restoration rate is from 0.998 to 1.512 within the error range of an interference arrival angle of 10°. This conclusion can be used in the signal correction of the PAF receiver for interference mitigation.

Multi‐Frequency SuperDARN Interferometer Calibration

AbstractThe ground‐based, high‐frequency radars of the Super Dual Auroral Radar Network (SuperDARN) observe backscatter from ionospheric field‐aligned plasma irregularities and features on the Earth's surface out to ranges of several thousand kilometers via over‐the‐horizon propagation of transmitted radio waves. Interferometric techniques can be applied to the received signals at the primary and secondary antenna arrays to measure the vertical angle of arrival, or elevation angle, for more accurate geolocation of SuperDARN observations. However, the calibration of SuperDARN interferometer measurements remains challenging for several reasons, including a 2π phase ambiguity when solving for the time delay correction factor needed to account for differences in the electrical path lengths between signals received at the two antenna arrays. We present a new technique using multi‐frequency ionospheric and ground backscatter observations for the calibration of SuperDARN interferometer data, and demonstrate its application to both historical and recent data.

A new statistical fading model for loss factor induced by angle-of-arrival fluctuations in free space optical communication.

The impact of angle-of-arrival (AOA) fluctuations on the loss factor is investigated, and a new fading model is proposed to describe the associated attenuation. Distinct decay characteristics induced by AOA fluctuations are revealed, which depend on the ratio of detector size to beam width (M). The fading model can be divided into two sub-models based on the value of M0, where M0 is the value of M when the root-mean-square errors of the two sub-models are equal. In the case of M⩾M0, this fading model follows an inverse S-shaped pattern to describe the impact of AOA fluctuations. When M < M0, an exponential function to characterize the attenuation induced by AOA fluctuations. The numerical computation results indicate that the proposed fading model offers higher accuracy compared to traditional methods. Furthermore, the continuous analytical expressions for the probability density function (PDF) and cumulative density function (CDF) of the presented fading model caused by AOA are derived. Based on the proposed PDF and CDF models, the outage probability of inter-satellite laser link is analyzed.

Metalens optical receiver for multi-beam free-space optical communication

A conventional optical receiver with a single detector accepts optical beams from a small range of angles of arrival (AoAs) and hence is not suitable for simultaneous communication with nodes with angular diversity. Here, we demonstrate a novel, to our knowledge, two-metalens-single-detector optical receiver that can collect optical beams coming from a wide range of AoAs. We show that the capability to collect broad AoAs with high efficiency makes the proposed receiver suitable for multi-beam free-space optical communication. An analysis for an exemplary three-beam system shows more than 70% collection efficiency at 2° AoA, which is 35× higher than conventional receivers.

Predefined-time robust adaptive gradient neural network for solving linear time-varying equations and its applications

Adaptive gradient neural networks (AGNN) have been extensively employed as effective models for addressing time-varying problems. However, the existing AGNN are convergent in infinite time and only considered in noiseless environment. Consequently, the AGNN with a certain convergence time and noise tolerance is urgently required. Motivated by the advantages of predefined-time convergence, a novel predefined-time robust adaptive gradient neural network (PRAGNN) is developed for solving linear time-varying equations. It can be concluded that PRAGNN can efficiently obtain real-time solutions of linear time-varying equations within predefined time, independent of initial conditions, by properly selecting parameters. In the process of solving linear time-varying equations, PRAGNN can avoid matrix inversion, which reduces the computing cost compared with zeroing neural networks (ZNN). Furthermore, PRAGNN can completely resist both bounded vanishing noise and bounded non-vanishing noise. Numerical experiments demonstrate the effectiveness of PRAGNN. Finally, PRAGNN has been successfully utilized in 2-D dynamic positioning based on the angle of arrival (AOA) positioning algorithm, as well as in alternating currents (AC) computing.

Indoor position estimation using angle of arrival measurements: An efficient multi-anchor approach with outlier rejection

This paper addresses the inherent nonlinear problem in indoor position estimation utilizing Angle of Arrival (AoA) measurements. We investigate the influence of deployment geometry on system performance through both analytical methods and Monte-Carlo simulations, shedding light on the limitations of single and multi-anchor setups and underscoring the imperative for advanced multi-anchor localization methods. In response to this problem, we propose a multi-anchor solution with outlier rejection that efficiently considers the nonlinearity of the model. For each anchor, our approach approximates the probability distribution of the node position by leveraging a geometrically-derived unscented transformation of AoA estimates. The approximations are then integrated into a majority voting scheme, effectively eliminating outliers induced by multipath or other adverse effects. To derive the final enhanced position estimate, Bayesian inference is applied to fuse the selected information. Finally, the efficacy of the solution is validated conducting a comparative analysis against commonly used approaches in a real-world Bluetooth indoor localization system. The results obtained solely from high-level angular measurements underscore the practicality, robustness and high accuracy of the proposal.

Angle of Arrival Estimator Utilizing the Minimum Number of Omnidirectional Microphones

In sound signal processing, angle of arrival indicates the direction from which a propagating sound signal arrives at a point where multiple omnidirectional microphones are positioned. Considering a small underwater platform (e.g., underwater unmanned vehicle), this article addresses how to estimate a non-cooperative target’s signal direction utilizing the minimum number of omnidirectional microphones. It is desirable to use the minimum number of microphones, since one can reduce the cost and size of the platform by using small number of omnidirectional microphones. Suppose that each microphone measures a real-valued sound signal whose speed and frequency information are not known in advance. Since two microphones cannot determine a unique AOA solution, this study presents how to estimate the angle of arrival using a general configuration composed of three omnidirectional microphones. The effectiveness of the proposed angle of arrival estimator utilizing only three microphones is demonstrated by comparing it with the state-of-the-art estimation algorithm through computer simulations.

A Co-Localization Algorithm for Underwater Moving Targets with an Unknown Constant Signal Propagation Speed and Platform Errors.

Underwater mobile acoustic source target localization encounters several challenges, including the unknown propagation speed of the source signal, uncertainty in the observation platform's position and velocity (i.e., platform systematic errors), and economic costs. This paper proposes a new two-step closed-form localization algorithm that jointly estimates the angle of arrival (AOA), time difference of arrival (TDOA), and frequency difference of arrival (FDOA) to address these challenges. The algorithm initially introduces auxiliary variables to construct pseudo-linear equations to obtain the initial solution. It then exploits the relationship between the unknown and auxiliary variables to derive the exact solution comprising solely the unknown variables. Both theoretical analyses and simulation experiments demonstrate that the proposed method accurately estimates the position, velocity, and speed of the sound source even with an unknown sound speed and platform systematic errors. It achieves asymptotic optimality within a reasonable error range to approach the Cramér-Rao lower bound (CRLB). Furthermore, the algorithm exhibits low complexity, reduces the number of required localization platforms, and decreases the economic costs. Additionally, the simulation experiments validate the effectiveness of the proposed localization method across various scenarios, outperforming other comparative algorithms.