Angle Of Arrival Estimation Algorithm Research Articles

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.

An Improved AoA Estimation Algorithm for BLE System in the Presence of Phase Noise

Location-based service (LBS) provides users with personalized experience. With the latest Bluetooth low energy (BLE) technology, switched antenna array and constant tone extension (CTE) are introduced enabling angle of arrival (AoA) estimation for improved positioning accuracy. However, phase noise may significantly degrade the AoA estimation performance. Such impairment and its mitigation method are not well discussed in existing literature. In this paper, we theoretically analyze the performance degradation of AoA estimation caused by phase noise in BLE system. Based on the specific antenna switching pattern, an improved multiple signal classification (MUSIC) algorithm is proposed. We further propose an expectation maximization MUSIC (EM-MUSIC) algorithm to improve the AoA estimation accuracy by estimating the phase noise with extended Kalman filter. Simulation results show that at typical SNR level, the proposed algorithm can reduce the estimation error by more than 55% compared with existing algorithms. The proposed EM-MUSIC algorithm can greatly improve the positioning accuracy and is ideal for LBS.

Cross Dipole Antennas Solution for Angle of Arrival Estimation

The Multiple Signal Classification (MUSIC) algorithm is the most popular algorithm to estimate the Angle of Arrival (AOA) of the received signals. The analysis of this algorithm (MUSIC) with typical array antenna element ( ) shows that there are two false direction indication in the planaligned with the axis of the array. In this paper a suggested modification on array system is proposed by using two perpendiculars crossed dipole array antenna in spite of one array antenna. The suggested modification does not affect the AOA estimation algorithm. The simulation and results shows that the proposed solution overcomes the MUSIC problem without any effect on the performance of the system.

Unambiguous 2-D Angle-of-Arrival Estimation Receiver Based on a 16-Port Interferometer

Multiport interferometer (MPI) techniques are getting more and more attention especially for passive receiver beyond 24 GHz. However, multiport interferometers with more than six ports are rarely been studied. And the existed MPI techniques only can achieve 2-D angle-of-arrival (AOA) estimation in a narrow range of angle, which means they have ambiguous problem. In this paper, an unambiguous 2-D AOA estimation receiver (Rx) based on a 16-port interferometer is proposed and demonstrated. It has three main components: eight antennas, a 16-port interferometer, and a signal processor. The 16-port interferometer with 8 input ports and 8 output ports is the key part and the main point of this paper. The eight input signals are superposed on eight output port signals in the interferometer. First, a 16-port interferometer and the corresponding AOA estimation algorithm are proposed. The algorithm transposes the phase differences between the antenna elements into the power relationship of the output signals. Then, the Rx error models are established to obtain the systematic errors. After that, a method for reducing angle ambiguity is introduced. The method allows for the distance between the antenna elements to be larger than λ/2, and the angle detection range is theoretically –90° to 90°. An Rx operating at 31 GHz was fabricated. The measurements demonstrate that the system operates as expected, with AOAs ranging from –40° to 40° and a mean squared error of approximately 1°.

A novel sampling methodology exploiting the least correlated-columns for angle of arrival estimation

Estimating Angle of Arrival (AOA) parameter from the measured data has gained much attention, particularly in the 5G-and-beyond wireless communication networks. With these communication networks, it is highly expected that the size and number of the data sets are huge and much larger than the rival traditional networks. To this end, the way of sampling and dealing with collected data in the array signal processing stage will have a significant impact on the signal estimation parameters. Thus, this manuscript proposes a novel sampling methodology to extract the collected information optimally by exploiting the least correlated and dependent columns within the measured Covariance Matrix (CM). The suggested method, Least Correlated Column Sampling (LCCS), is then adopted to implement a robust AOA estimation algorithm called Least Correlated Column Projection Matrix (LCCPM). A theoretical analysis is derived and presented to justify that the proposed technique minimizes correlations between adjacent columns and then compared with current sampling methodologies in terms of the position selection and corresponding correlation. The analytical results show that the LCCS methodology selects the least dependent columns among the rival methods. This, in turn, improves the AOA algorithm performance and enables it to localize closely-separated sources with high accuracy. To verify the theoretical claims and reveal the advantages of the suggested approach, a numerical example is given and then intensive Monte Carlo simulations are conducted over a wide range of scenarios where the proposed method performance is systemically compared with existing techniques. The obtained results show that the proposed algorithm is superior and outperforms all the preceding methodologies in terms of higher estimation resolution, lower Root Mean Square Error (RMSE), and higher Probability of Successful Detections (PSDs). The results also illustrate that the proposed method achieves higher noise immunity, less sensitivity to correlated signals, and detects a higher number of angles in comparison with conventional and recently-developed methods.

Accurate AoA Estimation for RFID Tag Array With Mutual Coupling

Angle-of-Arrival (AoA) estimation is an important problem in passive radio-frequency identification (RFID) systems. Affixing an RFID tag array to an object enables to acquire its orientation information. However, the electromagnetic interaction between the tags can induce mutual coupling interference, distorting the RFID fingerprint measurements used for AoA estimation. Moreover, RFID reader modes with radio-frequency (RF) noise-tolerant Miller encoding can induce <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula> -radians phase jump. In this article, we propose a scheme called RF-Mirror that can resolve the mutual coupling and phase jump problems and achieve accurate AoA estimation for an array with two or more tags. First, we characterize the impact of mutual coupling on a tag’s signal fingerprint and develop novel RSSI/phase-distance models. We then develop new experimental methods and signal processing techniques to verify the effectiveness of the proposed models. Based on the validated models, we develop new AoA estimation algorithms for tag arrays that deal with the mutual coupling effect explicitly. We provide extensive experimental results, which demonstrate that RF-Mirror can achieve significantly improved performance compared to baseline schemes, with median AoA estimation errors of 11.65° and 6.29° for two- and four-tag arrays, respectively.

Time of Arrival and Angle of Arrival Estimation Algorithm in Dense Multipath

Accurate position determination of a wireless device by time-of-arrival (TOA) and angle-of-arrival (AOA) estimation of the line-of-sight (LOS) path with an array of collocated antennas, is important in various applications. In an indoor environment, there can be many multipath reflections, from different angles of arrivals; and, when the signal bandwidth is relatively narrow, the received signal from the LOS path is often obscured by the overlapping received multipath signals. In this case, accurate estimation of the LOS TOA and AOA is challenging. In this paper, we present a novel estimator for the LOS TOA and AOA in multipath conditions, by obtaining an approximate distribution for the received signals of all the antennas. We approximate as Gaussian the distribution of the received signals given the LOS TOA and AOA, as well as additional clusters’ mean AOAs; and subsequently derive a maximum likelihood estimator for the LOS TOA and AOA. We also further derive an estimation algorithm with reduced complexity. The performance of the LOS TOA and AOA estimation algorithm in challenging multipath conditions is evaluated. The results show, that the proposed estimator achieves a performance advantage compared to reference methods for sufficiently high SNR.

Aircraft position estimation using angle of arrival of received radar signals

With increasing demand of air traffic, there is a need to optimize the use of available airspace. Effective utilization of airspace relies on quality of aircraft surveillance. Active research is carried out for enhancements in surveillance techniques and various methods are evaluated for future use. This paper evaluates the use of multiple signal classification (MUSIC) based angle of arrival (AOA) estimation along with multiangulation for locating aircrafts from their electromagnetic wave emission. The performance evaluation of the system is presented by evaluating the AOA estimation errors and position estimation (PE) errors. The errors are evaluated by comparing the estimated value to the actual value. An analysis on the system parameters, AOA error and PE error are presented in the end. AOA errors are affected by the AOA value (emitter bearing), number of array elements, SNR and resolution of AOA estimation algorithm. Errors in AOA estimation lead to PE errors. The simulation results show small errors for short ranges. The system performance can be improved at the expense of computational time by using higher MUSIC resolution and larger antenna arrays

Cascade AOA Estimation Algorithm Based on Flexible Massive Antenna Array.

The Angle-of-Arrival (AOA) has a variety of applications in civilian and military wireless communication fields. Due to the rapid development of the location-based service (LBS) industry, the importance of the AOA estimation technique has increased. Although a large antenna array is necessary to estimate accurate AOA information of many signals, the computational complexity of conventional AOA estimation algorithms, such as Multiple Signal Classification (MUSIC), is dramatically increased. In this paper, we propose a cascade AOA estimation algorithm employing CAPON and Beamspace MUSIC, based on a flexible (on/off) antenna array. First, this approach roughly finds AOA groups, including several signal AOAs using CAPON, by applying some of the antenna elements. Then, it estimates each signal AOA in the estimated AOA groups using Beamspace MUSIC by applying the full size of the antenna array. In addition to extremely low computational complexity, the proposed algorithm also has similar estimation performance to that of MUSIC. In particular, the proposed cascade AOA estimation algorithm is highly efficient when employing a massive antenna array. Representative computer simulation examples are provided to illustrate the AOA estimation performance of the proposed technique.

Multiemitter Two-Dimensional Angle-of-Arrival Estimator via Compressive Sensing

This article introduces a multiemitter 2-D angle-of-arrival (AoA) estimation scheme. It consists of an AoA estimation algorithm based on impinging signal spatial sparsity, Dantzig selector (an l1-norm minimization solution), and a six-element nonuniformly random-spaced 2-D array. The new scheme can identify more signals than the number of sensors without requiring a priori knowledge of signals and in a low signal-to-noise ratio (SNR) environment. It is demonstrated from 2 to 18 GHz with ten simultaneous incoming signals within 500 MHz instantaneous bandwidth. The number of signals, bandwidth, and frequency range are determined by the performance of digital receivers and are not limited by the compressive sensing (CS)-based 2D-AoA estimation scheme. Note that the random selection of element locations plays a vital role in ensuring that the new scheme achieves a highly successful estimation rate (SER). This is because random sensing is one of the key factors in CS. The only constraint of element locations is the spacing between elements, which has to be bigger than the diameter of cavity-backed-spiral-antenna. Simulation results show that the 2D-AoA estimator has more than 69%, 82%, and 90% SER if the measurement error is less than or equal to 1°, 2°, and 4°, respectively, when SNR is between -10 and 10 dB.

Accurate Indoor Localization Assisted With Optimizing Array Orientations and Receiver Positions

Owing to the multiple antennas equipped at modern Wi-Fi infrastructures, the angle-of-arrival (AoA) based indoor localization systems have successfully achieved the accuracy of tens of centimeters. However, the high accuracy is acquired at the cost of employing the additional resources in the domains of frequency, space or time, which requires complex processing and hinders the practical application. In this paper, we present the design and implementation of RcLoc, which takes full advantages of the flexible array orientations and receiver positions, based on limited resources. Particularly, RcLoc devises a receiver configuration scheme for guiding the system deployment. Optimized array orientation could effectively improve the AoA estimation accuracy and well-designed receiver positions contribute to the Cramer-Rao lower bound of localization error. In the stage of system realization, we further devise an array calibration method to calibrate the actual array and develop an improved AoA estimation algorithm, which make RcLoc robust to the array arrangement. We prototype RcLoc on commodity Wi-Fi devices without manual intervention. Comprehensive experiments in a multipath-rich indoor environment show that RcLoc achieves a median localization accuracy of 0.4 m, which provides useful insights for receiver deployment.

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.

Cooperative Dynamic Angle of Arrival Estimation Considering Space–Time Correlations for Terahertz Communications

Angle of arrival (AoA) estimation is required by adaptive directive antennas in order to realize a high antenna gain, which is necessary for future indoor terahertz communications due to its extremely high path loss. This paper proposes an AoA estimation algorithm using belief propagation in a dynamic scenario, where the user equipment (UE) is moving during the data transmission, based on the space-time correlations of AoA change. The temporal correlation of AoA change is due to the limited moving speed and statistical movement pattern. Furthermore, if we have distributed antennas or hybrid massive multiple-input-multiple-output (MIMO) array, the AoA changes of different antennas reveal spatial correlation because all the AoA changes are caused by the same spatial displacement of UE. This spatial correlation is utilized in this paper to further improve the estimation accuracy by passing messages between antennas and combining the intrinsic estimate by each antenna and extrinsic information from other antennas. In order to demonstrate the algorithm performance, a distributed antenna system and a hybrid massive MIMO array (an array of multiple directive antenna arrays) are considered as application scenarios. The simulation results show that the cooperative estimation brings significant advantage in both scenarios in respect of mean effective antenna gain and level crossing rate.

Accurate 2-D AOA Estimation and Ambiguity Resolution for a Single Source under Fixed Uniform Circular Arrays

This paper presents an analytic algorithm for accurate two-dimensional (2-D) angle of arrival (AOA) estimation of a single source under fixed uniform circular arrays (UCAs). Algebraic and explicit formulations for 2-D AOA estimation are first developed in the Fourier domain. It is shown that three is the minimum number of antennas for 2-D AOA estimation based on phase measurement. Then a signal model for phase extraction is established with equivalent phase noises through observations of signal samples corrupted by additive Gaussian white noise. Under fixed UCAs, 2-D AOA estimation of a single source would suffer from phase ambiguity, and hence, ambiguity resolution is also addressed in the Fourier domain by integer search. Numerical examples are provided to verify the effectiveness and appealing performance of the proposed 2-D AOA estimation algorithm.

Sparse-reconstruction-based 2-D angle of arrival estimation with L-shaped array

In this paper, a novel two-dimensional (2-D) angle of arrival estimation algorithm based on sparse signal reconstruction is addressed with an L-shaped array. By constructing a cross-correlation vector of the received signals, the 2-D angles estimation are obtained by ℓ1-norm minimization and least square procedure, respectively. Without pair matching process, the proposed algorithm can provide improved resolution and estimation accuracy. The simulation results validate the effectiveness of the proposed algorithm.

Performance Analysis of Angle of Arrival Estimation Algorithms for Smart Antenna in Wireless Sensor Networks: An Optimization Approach

Wireless Sensor Network) has energy constraints. Main energy consumption is at the (Tx) transmitter/ receiver (Rx) side, which is proportional to data/command packets /frames. Failure at the receiving end asks for re-transmission leading to more power consumption. WSN follows structured layers like medium access control (MAC), physical layer (PHY), link layer etc. PHY deals with RF transmission including antenna. In this paper the focus is given on smart antenna as, WSN deals with systems which need to be adaptive, especially in unknown time varying scenarios. With data transmission; location positioning (localization) and efficient routing are the factors to be considered. Localization of node is done using range measurements which include received signal strength, time of arrival, time difference of arrival (TDOA) and angle-of-arrival (AoA) measurement. By improvising antenna performance i.e. small beam width, we can achieve less erroneous data reception, leading to less energy consumption. In this work, we propose a method based on Non-dominated Sorting Genetic Algorithm (NSGA-II) for localization of nodes using smart antennas. Comparative analysis of its performance is done with different algorithms like LMS (Least Mean Square), LMS and PSO (Particle Swarm Algorithm) for varying number of elements and spacing between elements. We demonstrated that the proposed method achieves very good accuracy and precision in angle measurements as compared with existing approaches. Keywords-II, LMS, PSO, WSN, AoA, PHY, MAC, TDOA,

Evaluation of performance enhancement on CRLB of CAF under multiple emitters

A method of enhancing performance is described on the Cramer–Rao lower bound (CRLB) of time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements under multiple emitters. The TDOA and FDOA measurements between two receivers can be simultaneously estimated by using the cross ambiguity function (CAF). However, in the case of multiple emitters, these measurements are contaminated by the inter-symbol interferences (ISIs) which deteriorate the estimation quality. Therefore, it is required to reduce the effect of ISIs for enhancing the performance prior to calculating CAF. For separating the interference signals from the desired signal, we select the angle of arrival estimation algorithm with multiple array antennas. The performance of the estimator can be expressed as CRLB and the separation probability is calculated by employing the locally most powerful test. From the numerical evaluation results, it was observed that the performance could be significantly improved especially for the situation under heavy ISIs.

High-Resolution AoA Estimation for Hybrid Antenna Arrays

Communication with unlicenced 60-GHz band is attractive for wireless networks since a free-of-charge bandwidth of 7 GHz is available. The antenna array, conducting transmitter/receiver beamforming, is often used to enhance the transmission range. However, to conduct beamforming, the transceivers have to know the angles of signal arrival. Many angle-of-arrival (AoA) estimation algorithms are well known. Unfortunately, most of them are developed for digital arrays, requiring an analog-to-digital-converter (ADC) and a digital-to-analog converter (DAC) for each antenna element. For an array with a large number of antennas, the implementation cost will be very high. Recently, hybrid antenna array, in which multiple antennas share an ADC/DAC, was proposed as a low-cost solution. However, the existing AoA estimation algorithms for hybrid arrays can only support one AoA; their applications are then limited in multipath indoor environments. In this paper, we propose new AoA estimation algorithms to solve the problem. Using an augmented beam-space approach, we extend the use of the well-known AoA estimation algorithms, MUSIC and ESPRIT developed for digital arrays, to the scenario of hybrid antenna arrays. Simulations show that the proposed algorithms, although developed for hybrid arrays, can have similar performance as MUSIC and ESPRIT in digital arrays.

Application of the alternating projection strategy to the Capon beamforming and the MUSIC algorithm for azimuth/elevation AOA estimation

In this paper, the alternating projection (AP) scheme is applied to get the initial and the final estimates of the Capon beamforming algorithm and the MUSIC algorithm. In a previous paper, the AP scheme has been proposed as a computationally efficient method for the initialization and optimization of the cost function for the ML-based angle of arrival (AOA) estimation problem. We call the AP scheme in the previous paper the brute-force AP strategy. To improve the brute-force AP, we suggest the Newton AP which consists of the successive one-dimensional Newton search. The brute-force AP can be used for the initialization and the optimization of the cost function of AOA estimation, and the Newton AP can be used for the optimization of the cost function for the AOA problem. We compare the performance of the AP-based approach with the conventional implementation of the AOA estimation algorithm using two-dimensional brute-force search.