Direction Of Arrival Parameters Research Articles

Joint parameter estimation algorithm for polarization-sensitive channel compression arrays based on atomic norm minimization

To reduce the complexity of direction-of-arrival (DOA) algorithms in polarization-sensitive arrays and maintain high estimation accuracy while decreasing runtime, this paper proposes a channel compression model based on orthogonal dipole arrays. In addition, a DOA estimation algorithm using atomic norm minimization (ANM) is introduced for this model. This algorithm performs eigenvalue decomposition on the covariance matrix of the data received from the polarization-sensitive channel compressed array. A new observation vector is constructed for the ANM problem under the channel compression model using the obtained eigenvalues and eigenvectors. Subsequently, a Toeplitz matrix is constructed from the optimal solution of the semi-positive definite programming (SDP) problem. The Vandermonde decomposition of the Toeplitz matrix provides estimates of the signal DOA parameters. By combining the vectorization result of the covariance matrix and the least-squares calculation, the polarization auxiliary angle and polarization phase angle information of the incident source are also obtained. Simulation experiments compare the root-mean-squared error (RMSE) of each algorithm at different angular intervals and signal-to-noise ratios (SNR) to confirm the validity of estimating DOA and polarization parameters.

Robust Tensor-Based DOA and Polarization Estimation in Conformal Polarization Sensitive Array with Bad Data.

Partially impaired sensor arrays pose a significant challenge in accurately estimating signal parameters. The occurrence of bad data is highly probable, resulting in random loss of source information and substantial performance degradation in parameter estimation. In this paper, a tensor variational sparse Bayesian learning (TVSBL) method is proposed for the estimate of direction of arrival (DOA) and polarization parameters jointly based on a conformal polarization sensitive array (CPSA), taking into account scenarios with the partially impaired sensor array. First, a sparse tensor-based received data model is developed for CPSAs that incorporates bad data. Then, a column vector detection method is proposed to diagnose the positions of the impaired sensors. In scenarios involving partially impaired sensor arrays, a low-rank matrix completion method is employed to recover the random loss of signal information. Finally, variational sparse Bayesian learning (VSBL) and minimum eigenvector methods are utilized sequentially to obtain the DOA and polarization parameters estimation, successively. Furthermore, the Cramér-Rao bound is given for the proposed method. Simulation results validated the effectiveness of the proposed method.

Geometric Algebra based 2D-DOA Estimation for Non-circular Signals with an Electromagnetic Vector Array

This paper presents two novel methods based on geometric algebra (GA) to estimate two-dimensional (2D) direction-of-arrival (DOA) of non-circular (NC) signals for uniform rectangular array (URA). Traditional methods treat the received NC signals as a long vector which will inevitably lose orthogonality inside each electromagnetic vector sensor (EMVS) and thus miss some information of second-order statistical properties. Furthermore, the computational complexity will also increase. By contrast, the GA-based estimating signal parameter via rotational invariance techniques (ESPRIT) and propagation method (PM) algorithms are proposed to estimation DOA of NC signals. Taking advantage of GA, the relationship among multidimensional signals can be maintained. First, the six components of the EMVS are represented as a multivector in GA space. Then, we construct the GA-based extended covariance matrix to utilize the signal information more completely. The DOA parameters can be estimated through the ESPRIT and PM principle. The proposed GA-based estimation of signal parameter via rotational invariance techniques for NC signals processing (GANC-ESPRIT) can estimate DOA with high estimation accuracy. The proposed GA-based propagation method for NC signals estimation (GANC-PM) uses linear transformation to calculate angle parameters. Our model has much lower memory requirements and less computational burden compared with long vector models. Simulation results demonstrate the robustness and superiority of the proposed GANC-ESPRIT algorithm in terms of angular resolution. Complexity analysis shows that the proposed GANC-PM algorithm performances better with less computations.

DOA estimation and signal sorting methods of multi-baseline polarized interferometer

The density of the signal flow in electronic reconnaissance has developed from tens of thousands of pulses per second earlier to millions of pulses per second currently. To address the problem of rapidly identifying, analyzing, and locating all threat signal sources in the electronic reconnaissance environment. Firstly, the thought of direction of arrival (DOA) estimation followed by signal sorting is investigated, which contributes to achieving the parameter estimation and analysis of all signals in complex electromagnetic environments. Additionally, the DOA estimation method utilizes the signal power received by the array to correctly decouple the spatial phase difference from the polarization phase difference. By exploiting the thought of subspace decomposition, the robustness of the decoupling is improved. Then, the estimation formulas of the DOA and polarization parameters are found. Finally, polarization parameters are used to participate in the signal sorting, which significantly reduces the density of the signal flow faced by the signal sorting method, and improves the real-time and accuracy of signal sorting. The proposed methods achieve microsecond-level DOA estimation and signal sorting for broadband high-density signal flows. Numerical simulation results demonstrate the superiority of the proposed method.

An Improved Two-Stage Spherical Harmonic ESPRIT-Type Algorithm

Sensor arrays are gradually becoming a current research hotspot due to their flexible beam control, high signal gain, robustness against extreme interference, and high spatial resolution. Among them, spherical microphone arrays with complex rotational symmetry can capture more sound field information than planar arrays and can convert the collected multiple speech signals into the spherical harmonic domain for processing through spherical modal decomposition. The subspace class direction of arrival (DOA) estimation algorithm is sensitive to noise and reverberation, and its performance can be improved by introducing relative sound pressure and frequency-smoothing techniques. The introduction of the relative sound pressure can increase the difference between the eigenvalues corresponding to the signal subspace and the noise subspace, which is helpful to estimate the number of active sound sources. The eigenbeam estimation of signal parameters via the rotational invariance technique (EB-ESPRIT) is a well-known subspace-based algorithm for a spherical microphone array. The EB-ESPRIT cannot estimate the DOA when the elevation angle approaches 90°. Huang et al. proposed a two-step ESPRIT (TS-ESPRIT) algorithm to solve this problem. The TS-ESPRIT algorithm estimates the elevation and azimuth angles of the signal independently, so there is a problem with DOA parameter pairing. In this paper, the DOA parameter pairing problem of the TS-ESPRIT algorithm is solved by introducing generalized eigenvalue decomposition without increasing the computation of the algorithm. At the same time, the estimation of the elevation angle is given by the arctan function, which increases the estimation accuracy of the elevation angle of the algorithm. The robustness of the algorithm in a noisy environment is also enhanced by introducing the relative sound pressure into the algorithm. Finally, the simulation and field-testing results show that the proposed method not only solves the problem of DOA parameter pairing, but also outperforms the traditional methods in DOA estimation accuracy.

Blind Adaptive Beamforming for a Global Navigation Satellite System Array Receiver Based on Direction Lock Loop

The adaptive beamforming algorithm can realize interference suppression and navigation signal enhancement, and has been widely used. However, achieving high-precision real-time estimation of the direction of arrival (DOA) parameters of navigation signals in strong-interference scenarios with low complexity is still a challenge. In this paper, a blind adaptive beamforming algorithm for a Global Navigation Satellite System (GNSS) array receiver based on direction lock loop is proposed without using the prior information of the DOA parameter. The direction lock loop is used for DOA tracking and estimation after interference suppression, which uses the spatial correlation of the array beam pattern to construct a closed direction-tracking loop. The DOA estimation value is adjusted in real time based on the loop errors. A blind beamformer is constructed using the DOA estimation results to provide gain by forming a beam in the satellite direction. This method improves the accuracy and dynamic adaptability of DOA estimation while significantly reducing the computational complexity. The theoretical analysis and simulation results verify the effectiveness of the proposed algorithm.

Polynomial rooting-based parameter estimation for polarimetric monostatic MIMO radar

Compared with conventional multiple-input multiple-output (MIMO) radar, polarimetric MIMO radar has been proven to hold the additional capability to operate data in the joint spatial and polarization domains. However, practical transceivers may involve antenna arrays with classical non-ideal factors (e.g., gain/phase/position errors) and polarimetric non-ideal factors (e.g., polarimetric antenna misorientation), and many current approaches cannot handle such non-ideal factors in real applications. In this study, polynomial rooting-based direction-of-arrival (DOA) and polarization parameter estimation is investigated for polarimetric monostatic MIMO radar with non-ideal transceivers. By incorporating the polarimetric manifold separation (PMS) technique, a joint DOA and polarization parameter estimation method, termed PMS2, is proposed with a closed-form solution. The Cramér-Rao bound for the multi-dimensional parameters involved in the estimation problem is also given. Simulation results demonstrate the performance advantages of the proposed PMS2 approach, which is attributed to the compact structure of polarimetric transceivers and the ability of the PMS2 approach to handle the polarimetric properties of signals.

Small Sample Coherent DOA Estimation Method Based on S2S Neural Network Meta Reinforcement Learning.

Aiming at the existing Direction of Arrival (DOA) methods based on neural network, a large number of samples are required to achieve signal-scene adaptation and accurate angle estimation. In the coherent signal environment, the problems of a larger amount of training sample data are required. In this paper, the DOA of coherent signal is converted into the DOA parameter estimation of the angle interval of incident signal. The accurate estimation of coherent DOA under the condition of small samples based on meta-reinforcement learning (MRL) is realized. The meta-reinforcement learning method in this paper models the process of angle interval estimation of coherent signals as a Markov decision process. In the inner loop layer, the sequence to sequence (S2S) neural network is used to express the angular interval feature sequence of the incident signal DOA. The strategy learning of the existence of angle interval under small samples is realized through making full use of the context relevance of spatial spectral sequence through S2S neural network. Thus, according to the optimal strategy, the output sequence is sequentially determined to give the angle interval of the incident signal. Finally, DOA is obtained through one-dimensional spectral peak search according to the angle interval obtained. The experiment shows that the meta-reinforcement learning algorithm based on S2S neural network can quickly converge to the optimal state by only updating the gradient of S2S neural network parameters with a small sample set when a new signal environment appears.

Direction-of-Arrival Estimation for Nested Acoustic Vector-Sensor Arrays Using Quaternions

There is an increasing interest in direction-of-arrival (DOA) estimation using nested arrays composed of vector sensors. Considering acoustic vector sensors (AVSs) commonly used in underwater applications, this paper proposes a novel algorithm, called augmented nested quaternion-MUSIC (ANQ-MUSIC), to perform DOA estimation. By judiciously arranging the multi-component outputs of AVSs, we model the received signals from the entire nested AVS array as a quaternion observation vector in a compact way to reduce the computational complexity. Next, we formulate a quaternion-based difference co-array (QDCA) model via vectorizing the quaternion covariance matrix (QCM). Based on the obtained insights from the QDCA model, we derive a suitable QCM, which is constructed by applying the spatial smoothing technique. Finally, classical quaternion-MUSIC is logically introduced to estimate the DOA parameters. In simulations, we take into account non-uniform received noise and inter-component correlated noise, which may occur in practical underwater environments. The results demonstrate that the proposed method shows superiority in angular resolution and achieves a desirable trade-off between estimate accuracy and computational burden, besides showing robust performance in the above test scenarios.

Conjugate Augmented Decoupled 3-D Parameters Estimation Method for Near-Field Sources

A near-field source localization method is proposed for 2-D direction-of-arrival (DOA) and range estimation based on a symmetrical cross array. It first employs the conjugate symmetry property of signal autocorrelation for different time delays to construct a conjugate augmented spatial–temporal cross-correlation matrix, then the extended steering vector is decoupled to avoid the usual multidimensional search based on the properties of the Khatri–Rao product, and finally three 1-D MUSIC-type searches are employed to obtain the results. The proposed method can realize automatic pairing of multiple parameters associated with each source and it also works in the underdetermined case. Furthermore, the stochastic Cramer–Rao lower bound with different time delays is derived. Compared with two existing methods, simulation results demonstrate that the proposed method provides satisfactory estimation performance for both the DOA and range parameters at low signal-to-noise ratio and with a small number of snapshots.

High resolution DOA estimation of acoustic plane waves: An innovative comparison among Cuckoo search heuristics and subspace based algorithms

SONAR signal processing plays an indispensable role when it comes to parameter estimation of Direction of Arrival (DOA) of acoustic plane waves for closely spaced target exclusively under severe noisy environments. Resolution performance of classical MUSIC and ESPRIT algorithms and other subspace-based algorithms decreases under scenarios like low SNR, smaller number of snapshots and closely spaced targets. In this study, optimization strength of swarm intelligence of Cuckoo Search Algorithm (CSA) is accomplished for viable DOA estimation in different scenarios of underwater environment using a Uniform Linear Array (ULA). Higher resolution for closely spaced targets is achieved using smaller number of snapshots viably with CSA by investigating global minima of the highly nonlinear cost function of ULA. Performance analysis of CSA for different number of targets employing estimation accuracy, higher resolution, variance analysis, frequency distribution of RMSE over the monte Carlo runs and robustness against noise in the presence of additive-white Gaussian measurement noise is achieved. Comparative studies of CSA with Root MUSIC and ESPRIT along with Crammer Rao Bound analysis witnesses better results for estimating DOA parameters which are further endorsed from the results of Monte Carlo simulations.

Monopulse based DOA and polarization estimation with polarization sensitive arrays

In this paper, the vector sensor array (VSA) and the scalar sensor array (SSA) with polarization sensitivity (PS) are placed under the same framework of polarization sensitive arrays (PSAs), whose direction-dependent PS factor is larger than zero. This commonality of their PS factors enables both VSA and SSA to perform polarization estimation effectively. Based on this new PSA model, a generalized monopulse algorithm is proposed to jointly estimate direction-of-arrival (DOA) and polarization parameters, by expanding the existing monopulse angle estimation in spatial domain to both spatial and polarization domains. Both the DOA and polarization parameters can be effectively and efficiently estimated by comparing the outputs of the sum and spatial/polarized difference beams with only one snapshot. Then, closed-form expressions for joint DOA and polarization estimation error variances are derived and the polarization estimation variance is proved to decrease monotonically with the PS factor. Simulations are carried out on an X-band 5×5 planar array composed of microstrip patch antennas to demonstrate the effectiveness of the proposed algorithm.

Optimal Array Design and Directive Sensors for Guided Waves DoA Estimation.

The estimation of Direction of Arrival (DoA) of guided ultrasonic waves is an important task in many Structural Health Monitoring (SHM) applications. The aim is to locate sources of elastic waves which can be generated by impacts or defects in the inspected structures. In this paper, the array geometry and the shape of the piezo-sensors are designed to optimize the DoA estimation on a pre-defined angular sector, from acquisitions affected by noise and interference. In the proposed approach, the DoA of a wave generated by a single source is considered as a random variable that is uniformly distributed in a given range. The wave velocity is assumed to be unknown and the DoA estimation is performed by measuring the Differences in Time of Arrival (DToAs) of wavefronts impinging on the sensors. The optimization procedure of sensors positioning is based on the computation of the DoA and wave velocity parameters Cramér-Rao Matrix Bound (CRMB) with a Bayesian approach. An efficient DoA estimator is found based on the DToAs Gauss-Markov estimator for a three sensors array. Moreover, a novel directive sensor for guided waves is introduced to cancel out undesired Acoustic Sources impinging from DoAs out of the given angles range. Numerical results show the capability to filter directional interference of the novel sensor and a considerably improved DoA estimation performance provided by the optimized sensor cluster in the pre-defined angular sector, as compared to conventional approaches.

Generalized Sparse Polarization Array for DOA Estimation Using Compressive Measurements

The compressive array method, where a compression matrix is designed to reduce the dimension of the received signal vector, is an effective solution to obtain high estimation performance with low system complexity. While sparse arrays are often used to obtain higher degrees of freedom (DOFs), in this paper, an orthogonal dipole sparse array structure exploiting compressive measurements is proposed to estimate the direction of arrival (DOA) and polarization signal parameters jointly. Based on the proposed structure, we also propose an estimation algorithm using the compressed sensing (CS) method, where the DOAs are accurately estimated by the CS algorithm and the polarization parameters are obtained via the least-square method exploiting the previously estimated DOAs. Furthermore, the performance of the estimation of DOA and polarization parameters is explicitly discussed through the Cramér-Rao bound (CRB). The CRB expression for elevation angle and auxiliary polarization angle is derived to reveal the limit of estimation performance mathematically. The difference between the results given in this paper and the CRB results of other polarized reception structures is mainly due to the use of the compression matrix. Simulation results verify that, compared with the uncompressed structure, the proposed structure can achieve higher estimated performance with a given number of channels.

DOA and Polarization Estimation for Non-Circular Signals in 3-D Millimeter Wave Polarized Massive MIMO Systems

In this article, a multiple signal classification (MUSIC) based algorithm is proposed for two-dimensional (2-D) direction-of-arrival (DOA) and polarization estimation of non-circular signals in three-dimensional (3-D) millimeter wave polarized massive multiple-input-multiple-output (MIMO) systems. The traditional MUSIC-based algorithms can estimate either the DOA and polarization for circular signals or the DOA for non-circular signals by using spectrum search. By contrast, based on the quaternion theory, a novel algorithm named quaternion non-circular MUSIC (QNC-MUSIC) is proposed for parameter estimation of non-circular signals with high estimation accuracy. Moreover, only the DOA estimation needs spectrum search, and the polarization estimation has a closed-form expression. First, the DOA estimation can be achieved based on the derivation principle. Then the closed-form expression of the polarization estimation can be obtained based on the chain rule of the derivation w.r.t. the polarization parameters. In addition, the computational complexity analysis shows that compared with the conventional DOA and polarization estimation algorithms, our proposed QNC-MUSIC has much lower computational complexity, especially when the source number is large. The stochastic Cramér-Rao Bound (CRB) for the estimation of the 2-D DOA and polarization parameters of the non-circular signals is derived as well. Finally, numerical examples are provided to demonstrate that the proposed algorithms can improve the parameter estimation performance when large-scale/massive MIMO systems are employed.

Vehicle Location in Edge Computing Enabling IoTs Based on Bistatic FDA-MIMO Radar

Edge computing has a wide range of applications in the Internet of Things (IoT), especially suitable for low latency, high bandwidth, and high reliability. Edge computing enabling IoT can locate the vehicles rapidly with the help of edge computing. The sensors of IoTs can construct the Frequency diverse array (FDA) radar system, which has been widely concerned by scholars in recent years. The monostatic FDA and multiple-input-multiple-output (FDA-MIMO) is a research hotspot in parameter estimation field, but the research based on bistatic FDA-MIMO radar is insufficient. In this paper, we propose a tensor-based target location method in bistatic FDA-MIMO radar, which implements joint direction of arrival (DOA), direction of departure (DOD) and range parameters estimation. First of all, subarrays with different transmission frequency increment are used to construct the transmitting array to overcome the coupling between DOD and range. Then the tensor-based third-order signal model is established, which saves the multidimensional structure characteristics of received signal. And the signal subspace of each subarray is estimated by high-order-singular value decomposition (HOSVD). Furthermore, the phase period ambiguity is eliminated by limiting the range of the target, and the method for DOA, DOD and range parameters matching is provided. Theoretical analysis and numerical simulations demonstrate the effectiveness and superiority of the proposed method.

An accurate and easy deployment array gain-phase error calibration method for DoA estimation in Wi-Fi network

DoA estimation using channel state information (CSI) is an important technology for localization in Wi-Fi network. Existing direction of arrival (DoA) estimation algorithms such as multiple signal classification (MUSIC) algorithm may provide feasible DoA estimation accuracy, if the array gain-phase error can be well calibrated in commodity Wi-Fi devices. However, for multipath propagation environment, classical subspace-based calibration methods cannot be directly deployed, due to the coupling effect between gain-phase error parameters and DoA parameters. Therefore, most works deploy wired calibration methods with specialized devices, which also require professional and trivial deployments. This paper offers an iterative pairwise antenna gain-phase error calibration method, which is easy deployment and low cost. Furthermore, we combine spatial smoothing technique with pairwise antenna calibration schemes to guarantee calibration accuracy and parameters decoupling, respectively. Experimental results in realistic indoor environments to verify the effectiveness of the proposed calibration method.

Estimating DOA and PPS parameters of signal received by ULA in heavy noise environment

Abstract Estimation of the direction-of-arrival (DOA) and parameters of polynomial phase signal (PPS) impinging on the uniform linear array (ULA) of sensors in heavy-tailed noise environments is considered in this paper. To estimate signal parameters, a recently proposed quasi maximum-likelihood (QML) estimator is adopted. The proposed algorithm consists of two successive steps: (1) noise influence mitigation by using the proposed normalization strategy and (2) signal parameters estimation using the DOA-QML approach. The algorithm performance is evaluated for both monocomponent and multicomponent signals.

Joint estimation of DOA and polarisation for radar signals based on dual‐polarised antenna array

To apply polarisation information of radar signal in radar reconnaissance equipment, a joint estimation algorithm of the radar signal direction of arrival (DOA) and polarisation parameters based on dual-polarisation antenna array has been researched in this study. The mathematical model of dual-polarised circular array has been given, and the joint estimation method of DOA and polarisation parameters for radar signal based on multiple signal classification (MUSIC) algorithm has been analysed. To overcome the shortcomings such as high calculation cost for the MUSIC algorithm, the DOA-matrix algorithm has been presented. DOA-matrix algorithm can effectively distinguish the radar signals with different polarisation states in the same direction by use of eigen-decomposition of the DOA-matrix. Through matrix correlation calculation, spectral peak search has been avoided, and the computational cost and algorithm complexity are greatly reduced. Finally, the feasibility of the DOA-matrix algorithm has been simulated and analysed. The simulation results show that the DOA-matrix algorithm can effectively complete the joint estimation of DOA and polarisation parameters for radar signal. The running time reaches millisecond level, which has a strong application value in engineering.

Joint DOA and Polarization Estimation With Crossed-Dipole and Tripole Sensor Arrays

Electromagnetic vector sensor arrays can track both the polarization and direction of arrival (DOA) of the impinging signals. For linear crossed-dipole arrays, as shown by our analysis, due to inherent limitation of the structure, it can only track one DOA parameter and two polarization parameters. For full 4-D (two DOA, and two polarization parameters) estimation, we could extend the linear crossed-dipole array to the planar case. In this article, instead of extending the array geometry, we replace the crossed-dipoles by tripoles and construct a linear tripole array. Detailed proof shows that such a structure can estimate the 2-D DOA and 2-D polarization information effectively in general. A brief comparison between the planar crossed-dipole array and the linear tripole array is performed at last, showing that although the planar structure has a better performance, it is achieved at the cost of increased physical size.