Direction Of Arrival Estimation Techniques Research Articles

Simultaneous Correlative Interferometer Technique for Direction Finding of Signal Sources.

In this paper, we propose a novel simultaneous Correlative Interferometer (CI) technique that elaborately estimates the Direction of Arrival (DOA) of multiple source signals incident on an antenna array. The basic idea of the proposed technique is that the antenna-array-based receiver compares the phase of the received signal with one of the candidates at each time sample and jointly exploits these multiple time samples to estimate the DOAs of multiple signal sources. The proposed simultaneous CI-based DOA estimation technique collectively utilizes multiple time-domain samples and can be regarded as a generalized version of the conventional CI algorithm for the case of multiple time-domain samples. We first thoroughly review the conventional CI algorithm to comprehensively explain the procedure of the direction-finding algorithm that adopts the phase information of received signals. We also discuss several technical issues of conventional CI-based DOA estimation techniques that are originally proposed for the case of a single time-domain sample. Then, we propose a simultaneous CI-based DOA estimation technique with multi-sample diversity as a novel solution for the case of multiple time-domain samples. We clearly compare the proposed simultaneous CI technique with the conventional CI technique and we compare the existing Multiple Signal Classification (MUSIC)-based DOA estimation technique with the conventional CI-based technique by using the DOA spectrum as well. To the best of our knowledge, the simultaneous CI-based DOA estimation technique that effectively utilizes the characteristics of multiple signal sources over multiple time-domain samples has not been reported in the literature. Through extensive computer simulations, we show that the proposed simultaneous CI technique significantly outperforms both the conventional CI technique in terms of DOA estimation even in harsh environments and with various antenna array structures. It is worth noting that the proposed simultaneous CI technique results in much better performance than the classical MUSIC algorithm, which is one of the most representative subspace-based DOA estimation techniques.

Deep Learning-Enabled Improved Direction-of-Arrival Estimation Technique

This paper provides a simple yet effective approach to improve direction-of-arrival (DOA) estimation performance in extreme signal-to-noise-ratio (SNR) conditions. As an example, a multiple signal classification (MUSIC) algorithm with a deep learning (DL) approach is used. First, brief research into the existing DOA estimation techniques is provided, followed by a demonstration of a simulation environment created on the MATLAB platform to generate and resolve signals from a uniform rectangular array of antenna elements. Following that is an attempt to improve the estimation accuracy of these signals by training various DL approaches, including multi-layer perceptron and one- and two-dimensional convolutional neural networks, using the generated dataset. Key findings include the cases where the developed DL approach can resolve signals and provide accurate DOA estimations that the MUSIC algorithm cannot.

Joint Model-Order and Robust DoA Estimation for Underwater Sensor Arrays

The direction-of-arrival (DoA) estimation algorithms have a fundamental role in target bearing estimation by sensor array systems. Recently, compressive sensing (CS)-based sparse reconstruction techniques have been investigated for DoA estimation due to their superior performance relative to the conventional DoA estimation methods, for a limited number of measurement snapshots. In many underwater deployment scenarios, the acoustic sensor arrays must perform DoA estimation in the presence of several practical problems such as unknown source number, faulty sensors, low values of the received signal-to-noise ratio (SNR), and access to a limited number of measurement snapshots. In the literature, CS-based DoA estimation has been investigated for the individual occurrence of some of these errors but the estimation under joint occurrence of these errors has not been studied. This work investigates the CS-based robust DoA estimation to account for the joint impact of faulty sensors and low SNR conditions experienced by a uniform linear array of underwater acoustic sensors. Most importantly, the proposed CS-based DoA estimation technique does not require a priori knowledge of the source order, which is replaced in the modified stopping criterion of the reconstruction algorithm by taking into account the faulty sensors and the received SNR. Using Monte Carlo techniques, the DoA estimation performance of the proposed method is comprehensively evaluated in relation to other techniques.

Direction-of-Arrival (DoA) Estimation Performance for Satellite Applications in a Multipath Environment with Rician Fading and Spatial Correlation

Direction-of-arrival (DoA) estimation methods are highly versatile and find extensive applications in satellite communication. DoA methods are employed across a range of orbits, from low Earth orbits (LEO) to geostationary Earth orbits (GEO). They serve multiple applications, including altitude determination, geolocation and estimation accuracy, target localization, and relative and collaborative positioning. This paper provides a framework for modeling the DoA angle in satellite communications with respect to the elevation angle. The proposed approach employs a closed-form expression that incorporates various factors, such as the antenna boresight angle, satellite and Earth station positions, and the altitude parameters of the satellite stations. By leveraging this formulation, the work accurately calculates the Earth station’s elevation angle and effectively models the DoA angle. To the authors’ knowledge, this contribution is unique and has not been previously addressed in the available literature. Furthermore, this paper studies the impact of spatial correlation in the channel on well-known DoA estimation techniques. As a significant part of this contribution, the authors introduce a signal model incorporating correlation in satellite communication. Although selected studies have presented spatial signal correlation models in satellite communications to analyze the performance metrics, such as the bit error or symbol error probability, outage probability, and ergodic capacity, this work stands out by presenting and adapting a correlation model in the signal specifically for studying DoA estimations. Accordingly, this paper evaluates DoA estimation performance using root mean square error (RMSE) measurements for different satellite communication link conditions (uplink and downlink) through extensive Monte Carlo simulations. The simulation’s performance is evaluated by comparing it with the Cramer–Rao lower bound (CRLB) performance metric under additive white Gaussian noise (AWGN) conditions, i.e., thermal noise. The simulation results demonstrate that incorporating a spatial signal correlation model for DoA estimations significantly improves RMSE performance in satellite systems.

Robust DOA Estimation Based on an Exponential Hyperbolic Cosine Adaptive Algorithm

Direction-of-arrival (DOA) estimation methods based on conventional adaptive signal processing are not robust to outliers. A robust DOA estimation technique based on an exponential hyperbolic cosine function (EHCF) under such situations is proposed in this letter. To further enhance the performance of the proposed approach, a variable scale parameter strategy has been incorporated into the proposed scheme. The new methods have been shown to offer improved and robust DOA estimation performance.

Adaptive Diagonal Loading Technique to Improve Direction of Arrival Estimation Accuracy for Linear Antenna Array Sensors

Diagonal loading is one of the most widely used and effective methods to improve the robustness of both adaptive beamformers and Direction of Arrival (DOA) estimation due to the involvement of the sensor received covariance matrix. In addition, subspace-based DOA estimation techniques rely on multiple snapshots to achieve high estimation accuracies. This paper presents the study of a modified diagonally loaded sample covariance matrix for accurate DOA estimation in adverse scenarios. The proposed and novel technique deciphers poor DOA estimation in a low SNR environment by computationally changing the received sample covariance matrix. Our method is computationally simple as it does not require peak searching and does not depend on the coherency of the signal. The efficacy of the proposed method is examined via computer simulation for various sensor array sizes and the number of snapshot samples. Based on our numerical simulation results, our proposed method generally outperforms most state-of-the-art DOA estimators. In a finite number of snapshots and a single signal source, our proposed method performs 9.5% better than the state-of-the-art DOA estimation technique, 2.8% in multiple signal sources, and 8.5% in a single snapshot, single signal source environment of gained DOA estimation performance.

A Covariance Matrix Reconstruction Approach for Single Snapshot Direction of Arrival Estimation.

Achieving accurate single snapshot direction of arrival (DOA) information significantly improves communication performance. This paper investigates an accurate and high-resolution DOA estimation technique by enabling single snapshot data collection and enhancing DOA estimation results compared to multiple snapshot methods. This is carried out by manipulating the incoming signal covariance matrix while suppressing undesired additive white Gaussian noise (AWGN) by actively updating and estimating the antenna array manifold vector. We demonstrated the estimation performance in simulation that our proposed technique supersedes the estimation performance of existing state-of-the-art techniques in various signal-to-noise ratio (SNR) scenarios and single snapshot sampling environments. Our proposed covariance-based single snapshot (CbSS) technique yields the lowest root-mean-squared error (RMSE) against the true DOA compared to root-MUSIC and the partial relaxation (PR) approach for multiple snapshots and a single signal source environment. In addition, our proposed technique presents the lowest DOA estimation performance degradation in a multiple uncorrelated and coherent signal source environment by up to 25.5% with nearly negligible bias. Lastly, our proposed CbSS technique presents the best DOA estimation results for a single snapshot and single-source scenario with an RMSE of against the true DOA compared to root-MUSIC and the PR approach with nearly negligible bias as well. A potential application for CbSS would be in a scenario where accurate DOA estimation with a small antenna array form factor is a limitation, such as in the intelligent transportation system industry and wireless communication.

DOA Estimation of Linear Dipole Array With Known Mutual Coupling Based on ESPRIT and MUSIC

AbstractIn this paper, multiple signal classification and estimation of signal parameters via rotational invariance technique (ESPRIT) algorithms are proposed to estimate the direction of arrival (DOA) of multiple incident signals under known mutual coupling among elements of the antenna array. These algorithms use the same subspace based techniques to achieve the separation of the eigenvectors into two subspaces: the signal space and the noise space. However, ESPRIT only uses the signal space. The performance of subspace based DOA estimation algorithm is evaluated using uniform linear arrays of dipole elements. The simulation results show the exact number of samples and elements, signal‐to‐noise ratio and spacing between the antennas elements used are the most important parameters in the algorithms, to sustain the accuracy of the DOA of the incident signals. The problem of calculating mutual coupling coefficient of the array elements is formed into a matrix has been considered. It is shown from the simulation results that the performance of the DOA estimation techniques considering the mutual coupling effect can be improved by the proposed compensation method. The different results obtained are in good agreement with those of the literature.

Machine–learning-enabled metasurface for direction of arrival estimation

Abstract Metasurfaces, interacted with artificial intelligence, have now been motivating many contemporary research studies to revisit established fields, e.g., direction of arrival (DOA) estimation. Conventional DOA estimation techniques typically necessitate bulky-sized beam-scanning equipment for signal acquisition or complicated reconstruction algorithms for data postprocessing, making them ineffective for in-situ detection. In this article, we propose a machine-learning-enabled metasurface for DOA estimation. For certain incident signals, a tunable metasurface is controlled in sequence, generating a series of field intensities at the single receiving probe. The perceived data are subsequently processed by a pretrained random forest model to access the incident angle. As an illustrative example, we experimentally demonstrate a high-accuracy intelligent DOA estimation approach for a wide range of incident angles and achieve more than 95% accuracy with an error of less than 0.5 ° $0.5{}^{\circ}$ . The reported strategy opens a feasible route for intelligent DOA detection in full space and wide band. Moreover, it will provide breakthrough inspiration for traditional applications incorporating time-saving and equipment-simplified majorization.

A Review of the State of the Art and Future Challenges of Deep Learning-Based Beamforming

The key objective of this paper is to explore the recent state-of-the-art artificial intelligence (AI) applications on the broad field of beamforming. Hence, a multitude of AI-oriented beamforming studies are thoroughly investigated in order to correctly comprehend and profitably interpret the AI contribution in the beamforming performance. Starting from a brief overview of beamforming, including adaptive beamforming algorithms and direction of arrival (DOA) estimation methods, our analysis probes further into the main machine learning (ML) classes, the basic neural network (NN) topologies, and the most efficient deep learning (DL) schemes. Subsequently, and based on the prior aspects, the paper explores several concepts regarding the optimal use of ML and NNs either as standalone beamforming and DOA estimation techniques or in combination with other implementations, such as ultrasound imaging, massive multiple-input multiple-output structures, and intelligent reflecting surfaces. Finally, particular attention is drawn on the realization of beamforming or DOA estimation setups via DL topologies. The survey closes with various important conclusions along with an interesting discussion on potential future aspects and promising research challenges.

Multi-Level Time-Frequency Bins Selection for Direction of Arrival Estimation Using a Single Acoustic Vector Sensor

In the context of multi-source direction of arrival (DOA) estimation in an enclosed environment, the challenges include reverberation and overlapping of multiple simultaneous active sources. To address these interferences, the identification of time-frequency (TF) bins dominated by the sources signals is essential. In this work, we propose an intensity vector (IV) based TF bins selection technique for DOA estimation using a single acoustic vector sensor (AVS). The proposed technique involves multi-level inliers selection and outliers removal (MLISOR), which is implemented in three steps. In the first step, we derive the distribution of IVs and then select IVs using a norm metric. In the second step, the regions with the highest local IV density in each time frame are identified. In the third step, we cluster the IVs according to their directions and remove the outliers based on the member-to-centroid angle metric. Simulation results show that both the accuracy and the robustness of the proposed technique outperform the existing techniques. The indoor experimental results also verify that the proposed technique is effective and robust in practical situations.

DOA Estimation for Transmit Beamspace MIMO Radar via Tensor Decomposition With Vandermonde Factor Matrix

We address the problem of tensor decomposition in application to direction-of-arrival (DOA) estimation for transmit beamspace (TB) multiple-input multiple-output (MIMO) radar. A general 4-order tensor model that enables computationally efficient DOA estimation is designed. Whereas other tensor decomposition-based methods treat all factor matrices as arbitrary, the essence of the proposed DOA estimation method is to fully exploit the Vandermonde structure of the factor matrices to take advantage of the shift-invariance between and within different subarrays. Specifically, the received signal of TB MIMO radar is expressed as a 4-order tensor. Depending on the target Doppler shifts, the constructed tensor is reshaped into two distinct 3-order tensors. A computationally efficient tensor decomposition method is proposed to decompose the Vandermonde factor matrices. The generators of the Vandermonde factor matrices are computed to estimate the phase rotations between subarrays, which can be utilized as a look-up table for finding target DOA. It is further shown that our proposed method can be used in a more general scenario where the subarray structures can be arbitrary but identical. The proposed DOA estimation method requires no prior information about the tensor rank and is guaranteed to achieve precise decomposition result. Simulation results illustrate the performance improvement of the proposed DOA estimation method as compared to conventional DOA estimation techniques for TB MIMO Radar.

Performance of SVM technique for DoA Estimation in 5G mm-Wave band

Applying Machine Learning algorithms in wireless communication has shown increasing interest due to the increase of demand on capacity, the increase of the number of users, and equipment sharing the limited frequency spectrum resources. Also, the need for a reduction in power consumption at base stations and the optimization of radio coverage make ML an attractive and promising technique. In this paper, we investigate the usage of Support Vector Machine (SVM) technique for Direction of Arrival (DoA) estimation in the millimeter-wave band. The objective is to predict the location of a user in a given area by analyzing the received signals at an array of antennas, using an SVM-based model. The first phase of this technique consists of the training phase that aims to identify the characteristics of each class, and that is based on a set of training samples. The second phase consists of testing the trained model using a set of samples/users. We have carried out a set of simulations based on the developed model. The results are promising in terms of the accuracy of determining the DoA, taking into consideration a channel with noise and multipath.

Towards Low Latency and Resource-Efficient FPGA Implementations of the MUSIC Algorithm for Direction of Arrival Estimation

The estimation of the Direction of Arrival (DoA) is one of the most critical parameters for target recognition, identification and classification. MUltiple SIgnal Classification (MUSIC) is a powerful technique for DoA estimation. The algorithm requires complex mathematical operations like the computation of the covariance matrix for the input signals, eigenvalue decomposition and signal peak search. All these signal processing operations make real-time and resource-efficient implementation of the MUSIC algorithm on Field Programmable Gate Arrays (FPGAs) a challenge. In this paper, a novel design approach is proposed for the FPGA-implementation of the MUSIC algorithm. This approach enables a significant reduction in both FPGA resources and latency. In more detail, the proposed design enables the estimation of DoA in real-time scenarios in 2μsec with 30% to 50% fewer resources as compared to existing techniques.

Low-Complexity Aggregation Techniques for DOA Estimation over Wide-RF Bandwidths

Accurate direction of arrival (DOA) estimation of wideband, low-power nonstationary signals is important in many radio frequency (RF) applications. This article analyses the performance of two incoherent aggregation techniques for the DOA estimation of high chirp-rate linear frequency modulated (LFM) signals used in modern radar and electronic warfare (EW) applications. The aim is to determine suitable aggregation techniques for blind DOA estimation for real-time implementation with a frequency channelised signal. The first technique calculates a single pseudospectrum by directly combining the spatial covariance matrices from each of the frequency bins. The second technique first calculates the spatial pseudospectra from the spatial covariance matrix (SCM) from each frequency bin and then combines the spatial pseudospectra into one single estimate. Firstly, for single and multiple signal emitters, we compare the DOA estimation performance of incoherent SCM-based aggregation with that of the incoherent spatial pseudospectra-based aggregation using the root mean-squared error (RMSE). Secondly, we determine the types of signals and conditions for which these incoherent aggregation techniques are more suited. We demonstrate that the low-complexity SCM-based aggregation technique can achieve relatively good estimation performance compared to the pseudospectra-based aggregation technique for multiple narrowband signal detection. However, pseudospectra aggregation is better suited for single wideband emitter detection. Both the incoherent aggregation techniques presented in this article offer a computational advantage over the coherent processing techniques and hence are better suited for real-time implementation.

Novel DOA Estimation of Coherent Signal based on Virtual Array

Conventional direction-of-arrival (DOA) estimations perform poorly in coherent signals due to the rank loss of spatial covariance matrix, and most of the existing methods solve the problem at the expense of Degree of Freedom (DOF) or accuracy. In this paper, a novel DOA estimation technique for coherent signal is presented based on virtual array. The method comprises three steps: Firstly, the covariance matrix is generated according to the output signals of the array. Then, the first row and first column are selected from the matrix according to the data model of each element of the covariance matrix, which can be regarded as a uniform and continuous single snapshot signal of virtual array. Finally, the forward and backward spatial smoothing is used to achieve decorrelation of the single snapshot signal of the virtual array element. The proposed method has no loss of DOF and maintains high accuracy for low signal-to-noise ratio (SNR) conditions. The feasibility of the proposed method is proved by theoretical derivation, and its effectiveness is also verified by numerical results.

Direction-of-Arrival Estimation With Virtual Antenna Array: Observability Analysis, Local Oscillator Frequency Offset Compensation, and Experimental Results

Estimating the direction-of-arrival (DoA) of radio frequency sources is essential in many wireless applications. Traditional DoA estimation based on bearing measurements requires multiantenna arrays, which is not suitable for portable electronics for its large form factor. In this article, we improved a novelty single-antenna-based DoA estimation technique, called virtual multiantenna arrays. While the device is moving and continuously receiving signals, the DoA can be estimated by measuring the intercepted signals at several positions along the receiver's trajectory. Previous virtual array technology left two unsolved problems. Some receiver movements are not able to estimate the DoA, without a theoretical basis to screen feasible trajectories; the virtual array requires precise, relative coordinates of the receiver, which is challenging in reality. This article investigates the feasibility of the virtual array and improves its robustness by addressing these two unsolved problems. A theoretical foundation for feasible receiver trajectory determination is provided; the receiver has to move with accelerations to make the DoA observable. Also, we prove the nature that the DoA could be estimated by measuring the accelerations only, where precise receiver position is not mandatory. These results indicate that the virtual array is feasible with simple receiver movements and low-cost commercial equipment, thus significantly reducing system complexity and cost. Simulations are conducted to validate our theoretical predictions. The proof of concept implemented on a software-defined radio testbed also proves the validity and suitability of the improved DoA estimation technology in applications with form-factor constraints.

Array manifold matching algorithm based on fourth-order cumulant for 2D DOA estimation with two parallel nested arrays

In this paper, a two-dimensional (2D) direction-of-arrival (DOA) estimation algorithm with two parallel nested arrays is developed. Firstly, a constructor method for fourth-order cumulant (FOC) matrices is given according to the distribution of sensors. Then a pre-existing DOA estimation technique is firstly used to estimate the elevation angles and an improved unilateral array manifold matching (AMM) algorithm is used to estimate the azimuth angles. Compared with some classical 2D DOA estimation algorithms, the proposed algorithm has much better estimation performance, particularly in the case of low SNR environment. Compared with some traditional FOC-based algorithms, the proposed algorithm has higher estimation precision. Simulation results can illustrate the validity of proposed algorithm.

DOA Elevation and Azimuth Angles Estimation of GPS Jamming Signals Using Fast Orthogonal Search

This article introduces a new two-dimensional direction of arrival (DOA) elevation and azimuth angles estimation technique for global positioning system (GPS) jamming signals in challenging environments based on the fast orthogonal search (FOS) method. FOS-DOA estimation is accommodated to process array structure optimized for interference rejection. Performance of FOS-DOA is compared to the predominant multiple signal classification (MUSIC) DOA estimation method. Results showed significant improvement in jamming detection of the multiple sources of interference with slight variations in their amplitude at jamming to signal ratio (JSR) 15 and 45 dB. The improvement introduced by the proposed DOA estimation technique is mainly in the accuracy of detecting the number of jammers and their DOA.

A Novel Extended Cyclic MUSIC algorithm using Wavelet Decomposition Technique

The study and analysis of direction of arrival(DOA) estimation has been a very challenging aspect in wireless communication systems, radar and sonar in the presence of a fading channel. Extended cyclic MUSIC subspace based method is reviewed for the DOA when noise is present. An extension of the Cyclic MUSIC algorithm is the Extended mode of the cyclic MUSIC which uses an extended version of the data array vector. Based upon the spectral correlation of the inbound signals these algorithms can estimate the signals’ direction of arrival. The spatially correlated noises, present degrade the performance of the Cyclic MUSIC algorithms and also fail to make an estimate of the signals' direction of arrival when they are closely spaced. The paper proposes an improved DOA estimation technique of Wavelet Decomposition when spatially correlated noises are present. The signals present at the receiver side are denoised and the required estimates of the transmitted signals are obtained by using the Extended Cyclic MUSIC algorithm to the denoised data. Simulations prove that the output SNR has been enhanced considerably.