Angle Of Arrival Estimation Research Articles

Device-Free Tracking via Joint Velocity and AOA Estimation With Commodity WiFi

Device-free localization and tracking (DFT) is a novel technique which can estimate the location of a target without equipping it with any devices. Existing DFT systems mainly rely on machine learning techniques with a labor-intensive training. Recently some training-free DFT systems are designed by deriving the angle-of-arrival (AOA) using the dedicated hardware. However, limited by the hardware imperfection, realizing a training-free DFT system using the commodity Wi-Fi is still a challenging task to solve. To address this issue, we develop a novel DFT system which can track the target motion via both target velocity and AOA estimations simultaneously. First, the motion-induced phase shifts are refined from extremely noisy channel state information (CSI) measurements to detect the Doppler shift based on the signal superposition analysis. Then, according to the phased-array signal processing, we realize joint Doppler velocity and AOA estimation of the target path under the compressive sensing framework. It formulates joint Doppler velocity and AOA estimation as a two-dimensional sparse reconstruction problem, which can achieve a high accuracy to further estimate the target velocity and track the target motion at a decimeter level. We implement the proposed DFT system on commercial Wi-Fi devices and validate its performance with extensive evaluations in three indoor scenarios.

Recent Breakthroughs on Angle-of-Arrival Estimation for Millimeter-Wave High-Speed Railway Communication

With significantly improved efficiency, largescale hybrid antenna arrays with tens to hundreds of antennas have great potential to support millimeter-wave (mmWave) communication for high-speed railway (HSR) applications. The significant beamforming gains rely on fast and accurate estimation of the angle-of-arrival (AoA), but this can be impeded by the high train speed, the cost/energy oriented design of arrays, and the severe attenuation of mmWave signals. This article reviews these challenges, and discusses the limitations of existing AoA estimation techniques under hybrid antenna array settings. The article further reveals a few recent theoretical breakthroughs that can potentially enable fast and reliable estimation, even based on severely attenuated signals. Under a speed setting of 500 km/h, a performance study is carried out to confirm the significant improvements of estimation accuracy and subsequent beamforming gains as the results of the breakthroughs.

Digital array radar channel error analysis

The amplitude-phase error caused by the channel inconsistency in the actual digital array radar and the mutual coupling error caused by the mutual coupling effect have a great influence on the performance of the array. This study analyses and models the error characteristics of amplitude-phase error and mutual-coupling error, respectively. Based on the narrow-band and wide-band array received signal models, the spatial signal arrival angle estimation accuracy and the beam-synthesising pattern are used as indicators to analyse the influence of errors on the array. Channel inconsistency and mutual coupling will cause the estimated signal arrival angle to deviate from the true signal direction and the spatial spectrum curve peak will become gentle. In addition, the beam pattern's sidelobe will raise, null orientation will deviate from the interference signal direction, and gain will raise.

Power-Angular Spectra Correlation Based Two Step Angle of Arrival Estimation for Future Indoor Terahertz Communications

The future terahertz (THz) communication is a promising solution to the high data rate and short-range communication due to its broad available bandwidth. However, the high propagation path loss and the limited output power of the amplifier require highly directive antennas with high antenna gain in order to realize a reasonable signal-to-noise ratio (SNR). An angle-of-arrival (AoA) estimation is necessary to adjust the main lobe direction of the antenna. A major challenge of the AoA estimation is the compromise between accuracy and time consumption due to the big number of main lobe directions. This paper proposes a novel efficient AoA algorithm, which utilizes the fact that the power-angular spectrums (PASs) of different frequencies are highly correlated and the THz communication devices are equipped with radio frequency (RF) frontends of lower frequency. By means of a rough and quick estimation at the lower frequency, the range of the true AoA can be significantly confined and the precise estimation can be efficiently carried out. To begin with, an antenna model with discrete main lobe directions is proposed and the relationship between antenna gain, half power beamwidth (HPBW), and number of main lobe directions is analyzed. The PAS correlation is then validated with channel sounding measurement. The two-step AoA estimation algorithm is derived, formulated, and finally validated with hardware demonstration.

3D-WiFi: 3D Localization With Commodity WiFi

Recently, 3D localization has attracted intense interests with its enabled applications, such as smart healthcare, smart home, and smart robots. Conventional 3D localization technologies require to utilize the special-dedicated infrastructures with a large-scale deployment, which results in high cost. However, in order to accomplish an appealing tradeoff between location accuracy and infrastructure cost, designing and implementing a 3D localization system are still a challenging task to solve. To address this issue, we develop a novel 3D localization system by using commercial off-the-shelf (COTS) WiFi infrastructures at a low cost. First, we design a cost-effective hardware scheme to derive the orientation information for 3D localization. Then, we adopt a fusion method of multiple channel state information measurements to improve multipath resolution and identify the target direct path by the azimuth and elevation angle-of-arrival (AOA) and equivalent time-of-arrival estimations, which can be formulated as subspace-based sparse reconstruction with high accuracy. Finally, according to both azimuth and elevation AOA estimations of the target direct path, the target can be localized in 3D space at decimeter-level. We implement the proposed 3D localization system using COTS WiFi infrastructures and validate its performance with extensive evaluations in two indoor scenarios.

Localizing Sources Using a Network of Asynchronous Compact Arrays

The problem of passive acoustic estimating the position of a source using a network of synchronized underwater compact arrays is considered. Maximum-likelihood estimators using angle of arrival (AOA), time difference of arrival (TDOA), as well as a combination of AOA/TDOA estimates are developed. The localization accuracy provided by the AOA-based, TDOA-based, and hybrid estimators is evaluated using Cramer–Rao bounds, statistical simulations, and in situ test. Test results demonstrated that the efficiency of AOA-based and TDOA-based estimators strongly depends on variances of the AOA and TDOA estimates. Relative efficiency of the hybrid estimator is higher than any of the AOA-based and TDOA-based algorithms.

Using of microwave photonic transversal filter for broadband signal direction estimation in microwave photonic beamforming system for linear phased array antennas

An approach is considered to estimate direction of arrival of broadband microwave signals in linear phased array antenna with photonic beamforming system by using additional subsystem – microwave photonic filter. The frequencies of the notches in the spectrum of broadband signal at the microwave photonic transversal filter output allow to measure mutual delay between signals from two antenna array elements, and thus providing estimation of the signal arrival angle . Architecture of the constructed transversal filter is based on the components of two analog fiber-optic links being a part of antenna array photonic beamforming system. Some shown results of test measurements of microwave photonic transversal filter characteristics prove out an operability of the technique.

Partial Discharge Angle of Arrival Estimation Using UHF Wireless Sensor Array

The detection and localization of partial discharge (PD) in air-insulated substations by ultra-high frequency (UHF) technology is widely studied. Existing UHF PD localization system mainly focus on time-difference of UHF signal, which requires very high synchronized sampling rate. This paper proposes a received signal strength (RSS) based angle of arrival (AoA) method by a circle UHF wireless sensor array. An AoA estimation scheme is derived through the radiation pattern of designed UHF antenna. Interpolation and neural network are applied to improve the AOA resolution. A field test is performed to verify the effectiveness of the proposed PD localization system. The results show that the estimated error on average is below 7°. Compared to the time-difference-based method, the accuracy of our designed system is more feasible for practical application considering its significant low-cost feature.

SAPIL: single access point based indoor localisation using Wi‐Fi L‐shaped antenna array

Single access point (AP) based indoor localisation (SAPIL) has attracted much attention recently. Most of the existing SAPIL systems require frequency hopping, which cannot be easily achieved in commercial APs. In response to this compelling problem, the authors proposed the SAPIL using Wi-Fi L-shaped antenna array without modifying the existing Wi-Fi protocol. Specifically, the scenario in which the target is located is determined based on the maximum weight firstly. If the target is located in the line-of-sight scenario, the angle-of-arrival (AoA) corresponding to direct path is identified from the AoAs of multipath signals which are estimated using the smooth multiple signal classification algorithm. Meanwhile, the adaptive signal propagation model trained from the channel impulse response in offline phase is adopted to calculate the distance of direct path. Then the target is located by the estimated AoA and distance of direct path. Otherwise the target is located by combining its speed and heading with pedestrian dead reckoning. Finally, the experimental results demonstrate that the proposed system achieves the median AoA estimation error of 5°, median distance estimation error of 0.5 m, and median localisation error of 1.8 m.

Network Design for Accurate Vehicle Localization

Network-based localization plays a key role on the introduction of emerging road applications, such as connected autonomous driving. These applications demand unprecedented precise, reliable and secure positioning, with localization requirements below 1 m. This stringent demand is pushing for the use of road-side units (RSUs) from fifth generation and vehicular networks for accurate vehicle localization. However, these networks are not typically designed for positioning but for data communication purposes, which follow a different paradigm for the network deployment and operation. This paper provides design guidelines on dedicated network deployments with the aim of achieving accurate vehicle-to-infrastructure positioning in road scenarios. First, the network layout or site placement is assessed with a geometrical metric. Then, the minimum density of RSUs along the road is bounded with line-of-sight probability models for urban street and highway scenarios. Finally, the Cramér-Rao bound for joint time-of-arrival (ToA) and angle-of-arrival (AoA) localization is used to maximize the distance between RSUs along the road, by exploiting multi-antenna deployments. According to the simulation results, the network sites are recommended to be located at alternate sides of the road, with a maximum distance between RSUs of 40 and 230 m for urban and rural environments, respectively. Following these design guidelines, there is also the need to exploit antenna arrays to combine uplink ToA and AoA estimates, in order to ensure a vehicle location accuracy below one meter on the 95% of the cases. The use of eight antenna elements at the RSUs is found to reduce nearly one and a half times the minimum network density in highway localization deployments.

Enhanced Authentication Based on Angle of Signal Arrivals

In the scope of vehicular communication networks, the nature of exchanged safety/warning messages renders itself highly location dependent, as it is usually for incident reporting. Vehicles are required to periodically exchange beacon messages that include speed, time, and GPS location information. Reported position information must be accurate and authentic. To mitigate the potential risk of location spoofing/falsifying attack, this paper presents Angle-of-Arrival (AoA) estimation as a physical layer authentication scheme for cross verification of the reported location information. We consider a multi-antenna road side unit under location spoofing attack. Within the considered vehicular communication settings, fundamental limits of AoA estimation are developed in terms of its Cramer–Rao Bound and existence of efficient estimator. The problem of deciding whether the received signal originated from the claimed GPS location is formulated as a two-sided hypotheses testing problem whose solution is given by Wald test statics . We give correct decision $P_D$ and false alarm $P_F$ probabilities in a closed form. To offset the gap between information theoretical studies and practical implementations of physical layer security architectures, theoretical results presented in this paper are further supported by detailed hardware implantation using software-defined radios and field experimental results. We show that the keystone for obtaining reliable AoA measurements is the accurate characterization of the array response vector. Our hardware measurements showed that, unlike idealized models, hardware imperfections leave the actual array response vector of non-structured form. To address this problem, we introduce the method for obtaining the actual array response vector empirically, which led to accurate and stable AoA measurements. Field experimental results shows the feasibility of the proposed AoA authentication scheme.

Expeditious Estimation of Angle-of-Arrival for Hybrid Butler Matrix Arrays

Arrays of Butler matrices provide a promising front-end design for massive MIMO transceivers with low cost and low complexity. However, this advanced design does not necessarily translate to effective applications, unless the angle-of-arrival (AoA) of signals avails to the Butler matrices. This paper presents an efficient approach to the unprecedented AoA estimation for the arrays of Butler matrices. Specifically, we design a new beam synthesis method to recursively narrow down and increasingly focus on the angular region of interest, and hence achieving robust estimation of the phase offset between Butler matrices. With the phase offset canceled in the received signals, we are able to identify the set of critical Butler beams with the dominating effect on the AoA estimation, and estimate the AoA accordingly with minimum signaling. The mean squared error of the proposed estimation is analyzed in the presence of non-negligible noises, with closed-form lower bounds derived. Validated by simulations, the proposed algorithm is able to indistinguishably approach the lower bounds, and significantly outperforms the state-of-the-art developed for discrete antenna arrays by orders of magnitude in terms of accuracy, especially in low signal-to-noise regimes.

Optimal Optical Omnidirectional Angle-of-Arrival Estimator With Complementary Photodiodes

Angle-of-arrival (AOA) estimator is the core device in visible light positioning (VLP) systems with AOA algorithms. However, existing AOA estimators suffer from high computational complexity, narrow field-of-view (FOV), low accuracy, or high power consumption. In this work, we propose a novel AOA estimator based on an array of tilted complementary photodiodes (CPDs), where the estimator's FOV can be $2\pi$ rad, and the AOA estimation only requires the solution of a linear equation set. The orientations of the CPDs in the AOA estimator are optimized with respect to the average error power, resulting in closed-form optimal orientation expressions for an arbitrary number of CPDs. We also derive closed-form expressions for the probability density function and the cumulative distribution function (CDF) of the AOA estimation error. On the basis of CDF expression, we derive closed-form asymptotic bounds for the positioning outage probability of a fundamental VLP system. Analytical, simulation, and experimental results verify that the optimal AOA estimator can minimize the estimation error, and it can be employed in AOA positioning systems perusing high accuracy, low complexity, large FOV, low cost, low power consumption, and high response speed.

Synchronization of Multiple Independent Subarray Antennas: An Application for Angle of Arrival Estimation

In this paper, we present a new algorithm to synchronize multiple individual receiving subarray antennas to form an augmented array antenna system. This augmented array antenna has a large antenna aperture that can be used for angle of arrival (AoA) estimations and it provides a better AoA estimation accuracy than the individual subarray antennas. The proposed algorithm synchronizes the time and the frequency of the different subarray antennas, using a joint maximum likelihood (ML) optimization for a data-aided received signal. The phase coherency between the subarray antennas is achieved by using a combination of the least mean square and ML algorithms. The experimental results and the simulation example show that the proposed algorithm: 1) is stable in estimating the augmented array antenna pattern; 2) provides more accurate AoA estimations than the individual subarray antennas; 3) has no limitation to the structure of the augmented array antenna in space; and 4) converges very fast. Furthermore, the experimental results prove the feasibility of using AoA estimation techniques for Internet of Things localization in outdoor environments.

Exploiting AoA Estimation Accuracy for Indoor Localization: A Weighted AoA-Based Approach

For angle-of-arrival (AoA)-based localization methods, the AoA estimation accuracy would notably influence the localization performance. In this letter, we evaluate the estimation accuracy of different AoAs in a multipath environment and propose a weighted AoA-based localization approach. Specifically, the AoA estimation accuracy is quantified by deriving a closed form expression of asymptotic variance of the AoA estimation error. However, in practice, the accessible information for receivers is insufficient to retrieve the variance. Accordingly, we still find an approximate but effective substitution to evaluate the AoA estimation accuracy on basis of the limited information. Aided by the above analysis, we design the weighted localization method, where each estimated AoA is weighted with corresponding factor related to the AoA estimation accuracy. Simulations show that our proposed localization method decreases the median localization error by 20% compared with the unweighted method.

A Closed-Form Localization Algorithm Using Angle-of-Arrival and Difference Time of Scan Time Measurements in Scan-Based Radar

This letter presents a closed-form localization algorithm using the angle-of-arrival and difference time of scan time measurements from the scan-based radar (SBR). With the known radar scanning velocity, the difference time of the scan time can be converted into a subtended angle, which has linear relationship with the angle-of-arrival (AOA). The best linear unbiased estimator is applied for the optimal and reliable the AOA estimation. The weighted least square (WLS) method is used for localization of the SBR system. The presented solution is computationally efficient and statistically consistent in that the measured mean squared errors achieve the Cramer–Rao lower bounds.

Angle-of-Arrival Estimation Using an Adaptive Machine Learning Framework

Angle-of-arrival (AoA) estimation is of great interest, particularly for using radio to localize a device; good estimates of angles result in good estimates of location. In this letter, we propose a signal processing and machine learning combined tool for the AoA estimation. In particular, we utilize regression models trained using the snapshot data collected using multiple antennas for estimating the angle of arrival. Based on a set of simulation and real measurements underthe Bluetooth 5 low-energy system in an indoor environment, the proposed method is able to provide a considerable and consistent improvement without significant additional computational effort. We show that the proposed approach for AoA estimation provides an improvement of at least 20% compared with the baseline approach of traditional Multiple Signal Classification algorithm. We evaluate the performance of the proposed methods and show a consistent improvement using a range of channel parameters, including elevation angles, SNRs, and channel configurations.

Radio Source Localization in Multipath Channels Using EM Lens Assisted Massive Antennas Arrays

The focusing ability of the lens antennas can significantly contribute to perform precise radio localization with low complexity in a massive antennas array (MAA) system. Thus, by combining lens antennas with an MAA, we propose an approach for radio source localization through an angle of arrival (AoA) estimation in a multipath propagation scenario. We discuss two simplified antenna selection techniques to select and process the focused subsets of antennas. Then, we implement a low-complexity time of arrival estimation scheme to detect the first arrival path (FAP) that contains the actual AoA information. Finally, the FAP signal is processed to estimate the AoA very efficiently, by applying one of the traditional subspace-based methods. To further simplify the system architecture with only slight degradation in performance, we also propose a mixed analog–digital AoA estimation structure based on the application of the sum–difference signal patterns technique. The simulated root mean squared error performance curves as a function of the signal-to-noise-ratio demonstrate that the proposed approach is capable of providing good AoA estimation results while considerably reducing the overall system complexity compared with the MAA system without the lens.

Angle-of-Arrival Estimation in Formation Flying Satellites: Concept and Demonstration

One of the challenges of future space missions is the deployment of instruments distributed across multiple satellites that fly in an autonomous formation. This type of mission requires that the spacecraft communicate using inter-satellite links (ISL) and collaborate to fulfil the role of a large-scale, complex scientific instrument. In this context, precise information regarding the relative positioning of the satellites in terms of distance and orientation is essential. In radio frequency (RF) based techniques, obtaining the relative orientation between the satellites starts with estimating the angle-of-arrival (AoA) of the radio signal. This paper proposes an AoA estimation technique that can be used in Orthogonal Frequency-Division Multiplexing (OFDM) communications. Our approach consists in obtaining the AoA using the ISL, specifically the preamble whose primary roles are data link synchronization and channel estimation. Our technique allows a more accurate measurement of the AoA due to an improved ambiguity resolution of phase measurements. We give theoretical results regarding the accuracy of the AoA estimator and we validate them by tests on a real-time functioning testbed consisting in multiple National Instruments Universal Software Radio Peripheral platforms. We show that the carrier frequency offset introduces only a negligible bias in the AoA estimation and we discuss the effect of multipath propagation.

A Fuzzy-Based Angle-of-Arrival Estimation System (AES) Using Radiation Pattern Reconfigurable (RPR) Antenna and Modified Gaussian Membership Function

Angle-of-arrival (AOA) estimation is an important factor in various wireless sensing applications, especially localization systems. This paper proposes a new type of AOA estimation sensor node, known as AOA-estimation system (AES) where the received signal strength indication (RSSI) from multiple radiation pattern reconfigurable (RPR) antennas are used to calculate the AOA. In the proposed framework, three sets of RPR antennas have been used to provide a coverage of 15 regions of radiation patterns at different angles. The salient feature of this RPR-based AOA estimation is the use of Fuzzy Inferences System (FIS) to further enhance the number of estimation points. The introduction of a modified FIS membership function (MF) based on Gaussian function resulted in an improved 85% FIS aggregation percentage between the fuzzy input and output. This later resulted in a low AOA error (of less than 5%) and root-mean-square error (of less than 8°).