Angle Of Arrival Research Articles

Channel state information estimation for reconfigurable intelligent surfaces based on received signal analysis

<p>Recently, reconfigurable intelligent surfaces have an increasing role to enhance the coverage and quality of mobile networks especially when the received signal level is very weak because of obstacles and random fluctuation. This motivates the researchers to add more contributions to the fields of reconfigurable intelligent surfaces (RIS) in wireless communications. A substantial issue in reconfigurable intelligent surfaces is the huge overhead for channel state information estimation which limits the system’s performance, oppressively. In this work, a newly proposed method is to estimate the angle of arrival and path loss at the RIS side and then send short information to the base station rather than huge overhead as in previous research. The estimated channel state information is used to beamform the downlink waveform toward users accurately. The simulation results indicate that the proposed algorithm calculated the angle of arrival of users, admirably especially at a high signal-to-noise ratio. Moreover, a considerable spectral efficiency enhancement is obtained as compared to the traditional methods.</p>

Closed-Form Rectilinear Emitters Localization With Multiple Sensor Arrays: Phase Alignment and Optimal Weighted Least-Square Method

Position determination algorithms of rectilinear emitters (also called strictly second-order noncircular emitters) with multiple sensor arrays (SAs) allow for higher localization accuracy and more achievable degrees of freedom compared to the conventional version with circular emitters. However, it also brings additional computational complexity and noise. In this article, we develop a position determination algorithm of rectilinear emitters in closed-form on the basis of phase alignment and optimal weighted least square (OWLS), which is termed the PAOWLS method. First, we utilize the polynomial root-finding method to obtain the angle of arrival (AOA) and estimate rectilinear phases to align AOA. Then, we construct a least-square (LS) constraint to estimate the positions of emitters directly (termed the PALS method). Finally, we construct a weighted LS constraint and deduce the optimal weight to compensate for the position estimation bias. Extensive numerical results verify the outstanding performance of the proposed algorithm in terms of localization accuracy, resolution capability, and computational complexity.

Physical-layer key generation based on multipath channel diversity using dynamic metasurface antennas

Physical layer key generation (PKG) technology leverages the reciprocal channel randomness to generate the shared secret keys. The low secret key capacity of the existing PKG schemes is due to the reduction in degree-of-freedom from multipath fading channels to multipath combined channels. To improve the wireless key generation rate, we propose a multipath channel diversity-based PKG scheme. Assisted by dynamic metasurface antennas (DMA), a two-stage multipath channel parameter estimation algorithm is proposed to efficiently realize super-resolution multi-path parameter estimation. The proposed algorithm first estimates the angle of arrival (AOA) based on the reconfigurable radiation pattern of DMA, and then utilizes the results to design the training beamforming and receive beamforming to improve the estimation accuracy of the path gain. After multipath separation and parameter estimation, multi-dimensional independent path gains are utilized for generating secret keys. Finally, we analyze the security and complexity of the proposed scheme and give an upper bound on the secret key capacity in the high signal-to-noise ratio (SNR) region. The simulation results demonstrate that the proposed scheme can greatly improve the secret key capacity compared with the existing schemes.

High-Performance Channel Estimation for mmWave Wideband Systems With Hybrid Structures

In this paper, a channel estimation problem for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems with hybrid structures is studied. Firstly, a beamspace multiple signal classification (MUSIC) algorithm for mmWave wideband channels is proposed to simultaneously estimate the angles of arrival (AOAs), angles of departure (AODs) and transmission delays. Since the traditional spectral peak search method has high complexity, a multi-spectral peak search method is skillfully designed to search for multiple spectral peaks on the MUSIC spatial spectrum more quickly and accurately. Then, the proposed channel estimator is extended to more actual systems equipped with uniform planar arrays (UPAs). Finally, the Cramér–Rao bound (CRB) results of these channel parameters are derived for evaluating the performance of the proposed channel parameter estimator. Simulation results demonstrate that the proposed channel estimator has greatly high channel estimation accuracy.

A Linear Near-Field Interference Cancellation Method Based on Deconvolved Conventional Beamformer Using Fresnel Approximation

In underwater acoustic detection using towed arrays, the in-band radiation noise of the tow-ship generates significant interference. In this article, a high-resolution spatial filter based on the deconvolved conventional beamformer (dCv) is proposed to mitigate the impact of this noise on the detection on the beam of interest. The tow-ship interference is near field, thus, the dCv cannot be applied directly as the point spread function is shift variant. In this work, a solution to this problem involving the Fresnel approximation is developed. It is demonstrated analytically that the Fresnel approximation is not sensitive to the accuracy of the estimation of the interference range, and its applicability depends on the arrival angle of the interference. Experimental results show that the linearity of the dCv spatial filter ensures that the amplitude and phase of the array signal can be retrieved with no distortion, which is critical for acoustic detection.

Spatial Multi-Source Information Fusion Localization Algorithm in Non-Line-of-Sight Environments

In this paper, a high-accuracy localization problem under a complex non-line-of-sight (NLOS) condition is addressed by a new method that utilizes multiple NLOS paths to improve localization accuracy, as opposed to the traditional method of suppressing them. The spatial multi-path array fusion localization model is presented and analyzed, followed by an angle-of-arrival (AOA) and time-of-arrival (TOA) algorithm based on spatial multi-information fusion that seeks to improve localization accuracy. Multi-path of spatial signals, measurement of the multi-element antenna, and geographic environment information are integrated into the proposed method for localization optimization. Simulation experiments were carried out, and the results revealed that the proposed algorithm is capable of making full use of spatial multi-location information for localization, thus improving the accuracy of localization in a the NLOS environment effectively and increasing the locatable probability of complex environment localization applications.

A Contactless Authentication System Based on WiFi CSI

The ubiquitous and fine-grained features of WiFi signals make it promising for realizing contactless authentication. Existing methods, though yielding reasonably good performance in certain cases, are suffering from two major drawbacks: sensitivity to environmental dynamics and over-dependence on certain activities. Thus, the challenge of solving such issues is how to validate human identities under different environments, even with different activities. Toward this goal, in this article, we develop WiTL, a transfer learning–based contactless authentication system, which works by simultaneously detecting unique human features and removing the environment dynamics contained in the signal data under different environments. To correctly detect human features (i.e., human heights used in this article), we design a Height EStimation (HES) algorithm based on Angle of Arrival (AoA). Furthermore, a transfer learning technology combined with the Residual Network (ResNet) and the adversarial network is devised to extract activity features and learn environmental independent representations. Finally, experiments through multi-activities and under multi-scenes are conducted to validate the performance of WiTL. Compared with the state-of-the-art contactless authentication systems, WiTL achieves a great accuracy over 93% and 97% in multi-scenes and multi-activities identity recognition, respectively.

Efficient cooperative localization method with node selection based on position error bound

Cooperative Localization (CL) has become a promising solution that circumvents the needs for high-density anchor deployment, and offers additional localization accuracy in Wireless Sensor Networks (WSNs). However, it suffers from high computational complexity and increased network traffic problem. To alleviate this problem, we propose an iterative greedy node selection approach based on Position Error Bound (PEB) for Angle-of-arrival (AOA) based CL. We introduce a Boolean vector to formulate the node selection optimization problem that maximizes the Decrease of PEB (DPEB) from agents’ cooperation. Since the problem is NP-hard and difficult to solve, the iterative greedy algorithm is proposed to approach the sub-optimal solutions. Then, in order to improve the accuracy of localization phase, we derive the efficient closed-form solution of AOA CL problem and propose an iterative constrained Least Squares (LS) method. Moreover, the theoretical analysis shows that the localization estimator is very close to the Cramer Rao Lower Bound (CRLB) performance. Finally, simulation results show that the proposed node selection approach can greatly decrease the number of agents participating in the CL without significant degradation in localization accuracy. Besides, the proposed CL method outperforms the existing ones in terms of localization accuracy.

Beam tracking in phased array antenna based on the trajectory classification

AbstractOne of the major new concepts in 5G cellular communications is a shift in the way services are delivered to users. In the new 5G protocol, individual users are recognized and tracked through beams, which are targeted and specific to individual users. The millimeter‐wave (mmWave) communications impose a directionality which results in a significant challenge in serving mobile terminals and unmanned aerial vehicles. This challenge can be relieved in mmWave systems using analog beamforming. Based on the identification of the patterns in the reference signal received power (RSRP) measurements, some classifications are employed. Some trajectories are defined for different users in Hallways. Therefore, the angle of arrival (AoA) would be the same for the users following the same trajectory. The users based on the received RSRP values are clustered by K‐means and I‐Kmeans algorithms. To this end, our algorithms to find the beamforming coefficient are employed for only one user in each cluster and the complexity of the algorithms can be lower. The proposed algorithms are spatial frequency‐based beam tracking and angular domain beam tracking algorithms. Simulation results show that tracking the sine function of AoA achieves better performance compared to tracking the AoA. Moreover, the optimal number of clusters is obtained by using the Elbow method.

Nonuniform-Array-Based Integrated MIMO Communication and Positioning in Wireless Local Area Networks

The angle of arrival (AoA) is a vital parameter for geometry-based 3-D indoor positioning. Considering a wireless local area network (WLAN) as an example, a conventional antenna array designed for multiple-input–multiple-output (MIMO) communication is not suitable for AoA estimation due to the angle ambiguity introduced by the large interelement spacing (IES). Consequently, the integration of positioning and communication functions using the same radio frequency (RF) frontend and the array would be highly valuable. A nonuniform array (NUA) is proposed to solve this challenging problem. The NUA is optimized to achieve unambiguous angle estimation while maintaining high-capacity WLAN MIMO communication. Moreover, based on the estimated MIMO channel state information (CSI), an improved trilinear parallel factor (TPF) decomposition method is proposed to accurately estimate AoAs with low complexity. Simulated and experimental results show that the proposed NUA-based scheme can achieve decimeter-level 3-D positioning accuracy.

Intelligent Positioning Algorithm Based on CSI Channel Mode

Using wearable devices to realize the mining and application of human behavior patterns has become a hotspot in the field of intelligent positioning. Wearable devices provide an analyzable data foundation for indoor spatial distribution and human behavior pattern prediction. The development of the intelligent positioning system based on RSSI has encountered a bottleneck that it is difficult to improve the positioning accuracy. Therefore, some research works started emphasizing location technology based on channel state information (CSI). In this paper, the principle used by Wi-Fi channel state information to realize intelligent positioning is described, the characteristics of CSI are analyzed, and an intelligent positioning algorithm based on CSI is proposed. Specifically, the algorithm first estimates the angle of arrival (AoA) based on the MUSIC algorithm, separates the reflected paths in the multipath components, and accurately estimates the AoA of each path. Second, phase estimation with channel state information is achieved by forming different antenna subarray measurements under the consideration of a subset of antennas and subcarriers. Then, the phase response linear fitting of the data packet CSI is eliminated using the ToF purification algorithm to obtain the corrected phase response and realize the elimination of the STO noise of the channel state information. Finally, the target position is calculated by effectively filtering the reflection path through the likelihood value, and the accurate target positioning function is achieved. The experimental results demonstrate that the intelligent positioning algorithm proposed in this paper can achieve decimeter-level positioning accuracy under the condition of a fixed number of APs, and the average error is better than that of deep learning-based and SVM-based positioning algorithms. In other words, the accuracy of intelligent positioning is improved.

Smart Gateway for Healthcare Networks Based on Beam Steering Technology

To ensure high-reliability communication in healthcare networks, this paper presents a smart gateway system that includes an angle-of-arrival (AOA) estimation and a beam steering function for a small circular antenna array. To form a beam toward healthcare sensors, the proposed antenna estimates the direction of the sensors utilizing the radio-frequency-based interferometric monopulse technique. The fabricated antenna was assessed based on the measurements of complex directivity and the over-the-air (OTA) testing in Rice propagation environments using a two-dimensional fading emulator. The measurement results reveal that the accuracy of the AOA estimation agrees well with that of the analytical data obtained through the Monte Carlo simulation. This antenna is embedded with a beam steering function employing phased array technology, which can form a beam spaced at 45° intervals. The ability of full-azimuth beam steering with regard to the proposed antenna was evaluated by beam propagation experiments using a human phantom in an indoor environment. The received signal of the proposed antenna with beam steering increases more than that of a conventional dipole antenna, confirming that the developed antenna has great potential of achieving high-reliability communication in a healthcare network.

Photonic system for Doppler-frequency-shift and Angle-of-arrival simultaneous measurement using dual-parallel Mach–Zehnder modulator

A simple photonic system is proposed to measure the Doppler frequency shift (DFS) and Angle of arrival (AOA) of microwave signals simultaneously. The echo signals received by the two antennas are fed into two sub-MZM of a dual-parallel Mach–Zehnder modulator (DPMZM) respectively, and the reference signals are sent into one of the sub-MZM. The introduction of reference signals makes it easier to determine the direction of DFS. After the reference signal and echo signal pass through the low-speed PD, the beating signal is obtained. The value and direction of the DFS can be derived from the frequency of the down-converted signal. And the measurement of AOA can be calculated from the power of the output signal. The experimental results show that the echo signal measurement errors of DFS are less than ±0.5 Hz in the operating frequency range of 20 GHz, and the power sensitivity is less than -40 dBm. AOA has measurement errors of less than ±1.5° in the range of 0 to 90°. In addition, by adding only one test branch, the system can achieve the unambiguous measurement of AOA in the range of ±90°.

A survey of GNSS interference monitoring technologies

With the increasing economic and strategic significance of the global navigation satellite systems (GNSS), interference events also occur frequently. Interference monitoring technologies aim to monitor the interference that may affect the regular operation of the GNSS. Interference monitoring technologies can be divided into three parts: interference detection and recognition, interference source direction finding, and interference source location and tracking. Interference detection aims to determine whether interference exists. This paper introduces the classification of interference and the corresponding detection methods. The purpose of interference recognition is to recognize and classify interference. It is often combined with pattern recognition and machine learning algorithms. Interference source direction finding aims to estimate the direction of the interference signal. There are three kinds of methods: amplitude, phase, and spatial spectrum estimation. Interference source location aims to estimate the position of the interference signal. It is usually based on the received signal strength (RSS), time difference of arrival (TDOA), frequency difference of arrival (FDOA), angle of arrival (AOA) or direction of arrival (DOA). Interference source tracking aims to track moving interference sources, and it is generally based on Kalman filter theory. This paper summarizes the interference monitoring technologies and their latest progress. Finally, prospects for interference monitoring technologies are offered.

A method for resolving the phase difference ambiguity in interferometric monopulse angle of arrival estimation

This paper presents a method to eliminate the phase difference ambiguity in angle of arrival (AOA) estimation using the radio-frequency-based interferometric monopulse technique. In order to uniquely determine the AOA, we utilize four directivities which are formed by using selected elements with the same weight function as the AOA estimation. We investigate the AOA estimation accuracy for the direction of the incoming wave from a high elevation angle and in multi-path propagation environments using a three-dimensional fading emulator. The results show that the proposed method is valid for complex propagation environments.

A self-learning mean optimization filter to improve bluetooth 5.1 AoA indoor positioning accuracy for ship environments.

As COVID-19 is still spreading globally, the narrow ship space makes COVID-19 easier for the virus to infect ship passengers. Tracking close contacts remains an effective way to reduce the risk of virus transmission. Therefore, indoor positioning technology should be developed for ship environments. Today, almost all smart devices are equipped with Bluetooth. The Angle of Arrival (AoA) using Bluetooth 5.1 indoor positioning technology is well suited for ship environments. But the narrow ship space and steel walls make the multipath effect more pronounced in ship environments. This also means that more noises are included in the signal. In the Uniform Rectangular Array (URA) type receiving antenna array, elevation and azimuth angles are two important parameters for the AoA indoor positioning technology. Elevation and azimuth angles are unstable because of the influence of noise. In this paper, a Self-Learning Mean Optimization Filter (SLMOF) is proposed. The goal of SLMOF is to find the optimal elevation and azimuth angles as a way to improve the Bluetooth 5.1 AoA indoor positioning accuracy. The experimental results show that the Root Mean Square Error (RMSE) of SLMOF is 0.44m, which improves the accuracy by 72% compared to Kalman Filter (KF). This method can be applied to find the optimal average in every dataset.

Accurate Phase Angle Measurement of Backscatter Signal under Noisy Environment

Backscatter communication is an emerging wireless access technology to realize ultra-low power terminals exploiting the modulated reflection of incident radio wave. This paper proposes a method to measure the phase angle of backscatter link using principal component analysis (PCA). The phase angle measurement of backscatter link at the receiver is essential to maximize the signal quality for subsequent demodulation and to measure the distance and the angle of arrival. The drawback of popular phase angle measurement with naive phase averaging and linear regression analysis is to produce erroneous phase angle, where the phase angle is close to $\pm\frac{\pi}{2}$ radian and the signal quality is poor. The advantage of the proposal is quantified with a computer simulation, a conducted experiment and radio propagation experiments.

Sensing-Aided Uplink Channel Estimation for Joint Communication and Sensing

The joint communication and sensing (JCAS) technique has drawn great attention due to its high spectrum efficiency by using the same transmit signal for both communication and sensing. Exploiting the correlation between the uplink (UL) channel and the sensing results, we propose a sensing-aided Kalman filter (SAKF)-based channel state information (CSI) estimation method for UL JCAS, which exploits the angle-of-arrival (AoA) estimation to improve the CSI estimation accuracy. A Kalman filter (KF)-based CSI enhancement method is proposed to refine the least-square CSI estimation by exploiting the estimated AoA as the prior information. Simulation results show that the bit error rates (BER) of UL communication using the proposed SAKF-based CSI estimation method approach those using the minimum mean square error (MMSE) method, while at significantly reduced complexity.

Multi-user transmit beamforming for broadband acoustic systems

Multi-user transmit beamforming for acoustic systems based on orthogonal frequency division multiplexing (OFDM) modulation is studied and demonstrated. Transmit beamforming consists of assigning weight coefficients to multiple transmit elements, such that the signals transmitted from the array add constructively at the users' receive sides. In an optimal setting, the transmitter assigns weights that are proportional to the channel transfer functions to the users. However, the assumption of complete channel knowledge is not entirely accurate, as the transmitter obtains the channels via feedback from the users. Thus, the transmitter has partial knowledge of the channels, as their estimates are not only noisy, but can be outdated if the channels have changed during the time it took to close the feedback loop. These adversities are notably pronounced in time-varying acoustic channels, as the feedback delay is large as compared to the channel coherence time due to the low speed of sound propagation. To counteract this problem, a technique that targets only those features of the channels that can withstand the feedback delay is proposed. Such features are the users' angles of arrival of the principal propagation paths. The principal paths are stable paths that do not experience rapid fluctuations and are thus varying sufficiently slowly that they can tolerate long feedback delays. The design concepts are demonstrated using experimental over-the-air transmissions in an indoor environment of an acoustic communications testbed.

Source separation and arrival angle estimation using a single acoustic vector sensor

Extracting individual time signatures from two angularly-separated sources overlapping in time and bandwidth typically requires coherent array processing on multiple hydrophones, a process whose performance is degraded by limited aperture and spatial aliasing. Vector sensors, which measure both acoustic pressure and particle velocity, can measure an acoustic field’s directionality in a single, compact sensor package. Standard beamforming techniques can be applied to the outputs of a vector sensor to suppress one source’s signal, but its efficacy is limited by the angular insensitivity of the resulting cardioid beam pattern. Here we use fundamental physical principles to demonstrate that measurements on a 2-D vector sensor can completely determine the azimuths, amplitudes, and relative phases of two horizontal plane waves of the same frequency, achieving source separation and direction-of-arrival estimates for both. Interference between the waves creates a reactive intensity whose orientation is perpendicular to the bisector of the two wavenumber vectors. This observation leads to closed-form inversion formulas. Data examples of the technique are illustrated using overlapping humpback whale songs off Hawaii, and on weak airgun and bowhead whale signals embedded within intense directional noise from a nearby drilling site in the Beaufort Sea. [Work sponsored by ONR TFO.]