Unknown Mutual Coupling Research Articles

Reduced-Dimensional Block Sparse Angle Estimation for Bistatic MIMO Radar with Unknown Mutual Coupling

Reduced-Dimensional Block Sparse Angle Estimation for Bistatic MIMO Radar with Unknown Mutual Coupling

Antenna Array Diagnosis in the Presence of Unknown Mutual Coupling using Optimization Technique

Antenna array diagnosis is an important operation in communication systems, whenever element (s) failure in the array that worsening the projected radiation pattern occur. There are various diagnostic techniques found in literature that employ compressive sensing. Conversely, the techniques are based on easy formulation of array factor with no incorporation of mutual coupling existing between the radiators. This article shows how this deficiency lead to defective and bad diagnosis when there is presence of mutual coupling using port-level coupling matrix and average embedded antenna pattern. Furthermore, the element excitations are optimized to reduce the effect of mutual coherence of system measurement matrix, causing reduced measurements required for effective fault detection. Numerical simulation and experimental results demonstrate how the incorporation of mutual coupling generates an adequate and reliable array diagnosis, which are not found in literature. For instance, when fault number is set at 5, and SNR equals 10 dB, the smallest measurements needed for the diagnosis, which is the most effective diagnosis, are achieved when the optimized excitations are used. In conclusion, the implementation of the developed framework using measurement probe in space, shows enough results towards the practical deployment for antenna systems in wireless communication system.

Robust DOA estimation for two‐level nested arrays in unknown mutual coupling

AbstractAiming at solving the problem that the capability of direction of arrival method for estimation of nested arrays decreases sharply under unknown mutual coupling, an iterative weighted low‐rank matrix reconstruction algorithm is proposed here. First, the received data covariance matrix is extended, and a weighted low‐rank matrix recovery problem is formulated for joint estimations of the Toeplitz matrix and mutual coupling coefficients. Next, the direction of arrival from recovered Toeplitz matrix is retrieved by using the root multiple signal classification algorithm. Simulation results show that iterative weighted low‐rank matrix reconstruction algorithm can effectively avoid the influence of mutual coupling while solving the grid‐mismatch problem so that the accuracy and the resolution of estimation are improved.

A Novel Weighted Block Sparse DOA Estimation Based on Signal Subspace under Unknown Mutual Coupling

A Novel Weighted Block Sparse DOA Estimation Based on Signal Subspace under Unknown Mutual Coupling

Robust Sparse Recovery Based Vehicles Location Estimation in Intelligent Transportation System

An architecture for vehicle position estimation is introduced in this paper to tackle the issue of vehicles positioning in traffic congestion of intelligent transportation system (ITS). The introduced architecture is related to three unmanned aerial vehicles (UAVs) equipped with uniform linear array (ULA), ITS center and the terminal of position estimation, in which the UAV collect data from the ULA, the ITS center store data and the terminal is responsible for executing the corresponding direction of arrival (DOA) estimation algorithm to estimate the vehicle position. In the introduced architecture, DOA estimation is the crucial issue, hence a robust sparse recovery framework based on the optimal weighted subspace fitting is put forward for DOA estimation in the presence of direction-dependent unknown mutual coupling. Firstly, a data model can be obtained by a new transformation approach. Then, a sparse recovery algorithm based on the weighted subspace fitting is used to estimate the desired DOAs. The proposed DOA estimation algorithm can achieve superior performance in both coherent and incoherent signals in the environment of direction-dependent mutual coupling, and this environment often occurs in traffic congestion. Based on the DOA estimation results obtained from the proposed efficient regularized sparse recovery algorithm, the position of vehicles is final estimated by a weighted three-points cross-positioning method. The results of various simulation experiments fully demonstrate the robustness and superiority of the proposed architecture and algorithm.

Joint Range and Angle Estimation by FDA-MIMO Radar With Unknown Mutual Coupling

Frequency diverse array-multiple-input multiple-output (FDA-MIMO) radar is characterized by a range-angle-dependent transmit antenna beampattern, where both range and angle of the target can be jointly estimated. However, the performance of existing estimation algorithms degrades in the presence of mutual coupling effect. To overcome this drawback, this paper deals with the problem of joint range and angle estimation by FDA-MIMO radar with unknown mutual coupling. At the design stage, the mutual coupling matrices (MCM) are constructed in both transmit and receive arrays, exploiting the banded symmetric Toeplitz structure, where the mutual coupling effect is eliminated by transforming the MCM into a diagonal matrix with a selection matrix. Subsequently, the range ambiguity is resolved by setting two linear coprime frequency increments, which locates the true target at the overlap of the peaks corresponding to the two spatial spectra. Furthermore, to reduce the computational cost resulted from the two-dimensional (2-D) grid search, an iterative procedure based on the coordinate descent (CD) algorithm is considered by alternating between the optimization over one variable keeping the other fixed. At the analysis stage, the closed-form solutions of the mutual coupling coefficients are given by utilizing the estimated range and angle. Moreover, a comparison in terms of estimation performance in different coupling scenarios is carried out, where the Cramér-Rao lower bound is provided as the benchmark. Numerical results verify the effectiveness of the considered estimation strategy in multiple simulated scenarios.

DOA Estimation Based on Gridless Fuzzy Active Learning Under Unknown Mutual Coupling and Nonuniform Noise: Experimental Verification

In this paper, we proposed an active learning method for the DOA estimation based on uniform linear array (ULA) antennas with a very low computational complexity. In particular, an adaptive Fuzzy sampling strategy was used to make a model and refine it according to the snapshot data collected by multiple antennas to estimate the DOA angles. The proposed gridless Fuzzy pipeline sequential design zero-forcing (PLSD-ZF) sampling can reduce complexity by dividing the angle space into subsets and using the measurements of the appropriate subsets to obtain more accurate DOA estimates. Our simulation and experiment results demonstrate that the proposed fuzzy PLSD-ZF scheme outperforms other well-known ones in the presence of unknown mutual coupling, non-uniform noise, and data with a mixture distribution.

Positioning and Contour Extraction of Autonomous Vehicles Based on Enhanced DOA Estimation by Large-Scale Arrays

As an important branch of internet of vehicle (IoV) systems, autonomous vehicle (AV) positioning based on direction-of-arrival (DOA) estimation has received extensive attention in recent years. In this paper, an AV positioning method under unknown mutual coupling is proposed within the framework of large-dimensional asymptotic theory (LAT). Firstly, enhanced and closed-form DOA estimation is achieved by jointly exploiting large-scale uniform linear arrays (ULAs), Toeplitz rectification and the phase transformation result associated with the sample covariance matrix; secondly, a more reliable subset/set of DOAs is constructed according to the signal-to-noise at receivers; finally, robust AV positioning is achieved with the reliable subset/set. Motivated by satisfactory DOA estimation performance, an AV contour extraction scheme is developed with the aid of two antennas installed on an AV. The proposed method shows several salient advantages compared with existing methods, including improved resolution and accuracy, reduced computational complexity, robustness to mutual coupling and unreasonable DOA estimates, as well as the ability to effectively extract AV contour information.

Robust Block Sparse Beamforming for Strictly Non-Circular Signals and Unknown Array Mutual Coupling

Widely linear beamformers (WLBs) can greatly outperform the traditional adaptive beamformers. However, the performance of WLBs is extremely susceptible to the unknown mutual coupling (MC) effects caused by the imperfectly calibrated antennas array. In this brief, we propose a block sparse representation scheme and design a robust WLB for strictly non-circular (SNC) signals to mitigate the unknown MC mismatch. We use the structural characteristics of the steering vector under MC, and derive a new augmented formulation for the observed signals which takes into account the MC mismatches. We solve a block sparse optimization problem in order to estimate the directions of illuminating signals allowing us to reconstruct the augmented interference-plus-noise covariance matrix (AINCM). We then propose to estimate the augmented desired signal steering vector using the augmented subspace projection method. Our proposed robust WLB not only avoids estimating the MC coefficients, but also prevents the signal cancellation phenomenon from reconstructing the AINCM. Our simulation results demonstrate that the proposed WLB is effective for the SNC signals, outperforms the existing methods, and is robust against unknown MC and look direction mismatches.

Mixed source localization considering mutual coupling and unknown nonuniform noise under exact spatial geometry

A mixed far-field (FF) and near-field (NF) source localization method in the presence of unknown mutual coupling and nonuniform noise is proposed in this paper, where the exact spatial geometry considering propagation attenuation is adopted. First, the nonuniform noise covariance and FF direction of arrival (DOA) are estimated by the iterative least-squares subspace (ILSS) technique and the principle of rank reduction (RARE), respectively. Then, the NF source components are extracted by applying the oblique projection technique. Finally, by choosing three relative sensor positions and constructing four special cumulant matrices properly, NF DOA and range information are obtained in closed-form expressions, where a scheme to avoid ambiguity is exploited. Simulation result show that the proposed method can yield an improved performance in comparison with existing state-of-the-art methods.

Block Sparse Recovery Approach for DOA Estimation in Nested Array with Unknown Mutual Coupling

Block Sparse Recovery Approach for DOA Estimation in Nested Array with Unknown Mutual Coupling

Spherical Atomic Norm-Inspired Approach for Direction-of-Arrival Estimation of EM Waves Impinging on Spherical Antenna Array with Undefined Mutual Coupling

A spherical antenna array (SAA) is an array-designed arrangement capable of scanning in almost all the radiation sphere with constant directivity. It finds recent applications in aerospace, spacecraft, vehicular and satellite communications. Therefore, estimation of the direction-of-arrival (DoA) of electromagnetic (EM) waves that impinge on an SAA with unknown mutual coupling called for research attention. This paper proposed a spherical harmonic atomic norm minimization (SHANM) for DoA estimation using an SAA configuration. The gridless sparse signal recovery problem is considered in the spherical harmonic (SH) domain in conjunction with the atomic norm minimization (ANM). Because of the unavailability of the Vandermonde structure in the SH domain, the theorem of Vandermonde decomposition that is the mathematical basis of the traditional ANM methods finds no application in SH. Addressing this challenge, a low-dimensional semidefinite programming (SDP) approach implementing the SHANM method is developed. This approach is independent of Vandermonde decomposition, and directly recovers the atomic decomposition in SH. The numerical experimental results show the superior performance of the proposed method against the previous methods. In addition, accounting for the impacts of mutual coupling, an experimental measured data, which is the generally accepted ground of testing any method, is employed to illustrate the efficacy and robustness of the proposed methods. Finally, for achieving DoA estimation with sufficient localization accuracy using a SAA, the proposed SHANM-based method is a better option.

BSBL-Based Auxiliary Vehicle Position Analysis in Smart City Using Distributed MEC and UAV-Deployed IoT

Smart city enters the new 3.0 era, and Internet of Things (IoT) perform as the urban neural network in smart city. In the industrial areas of smart city, the IoT focuses on industrial applications, such as logistics and environmental monitoring. In this work, an auxiliary position analysis framework composed of IoT and distributed mobile-edge computing (MEC) is proposed to analyze the position of vehicles in the industrial areas of smart city. In the proposed framework, IoT are deployed by multiple unmanned aerial vehicles (UAVs) equipped with uniform linear array (ULA), which receives the signal emitted by vehicles for obtaining the direction of arrival (DOA), and the distributed MEC provides computing and synchronization services to auxiliary positioning. The DOA estimation is a key issue for auxiliary positioning in the proposed framework. To realize DOA estimation with unknown mutual coupling (MC) existing in IoT nodes, a novel block sparse Bayesian learning (SBL) algorithm is developed. In the developed algorithm, the unknown MC existing between sensors in each IoT nodes is first fused with the signal by parameterizing steering vector. Then, a block SBL (BSBL) procedure is presented to perform DOA estimation by using the inherent block sparse structure in the equivalent signal obtained after fusion. Benefiting from the fusion of MC and signal, the developed DOA estimation algorithm does not require a separate estimation of the unknown MC and also does not cause the loss of the array aperture. Based on the DOA estimation information, the position of vehicles in the industrial environment is effectively analyzed through weighted multiple cross-locations. Synthetic data set simulation is carried out to verify that the vehicle positions in smart city can be efficiently analyzed and estimated based on the presented framework and algorithm.

Adaptive Detectors for FDA-MIMO Radar With Unknown Mutual Coupling

Adaptive Detectors for FDA-MIMO Radar With Unknown Mutual Coupling

Real‐Valued Weighted Subspace Fitting Algorithm for DOA Estimation with Block Sparse Recovery

In this paper, the problem of direction‐of‐arrival (DOA) estimation for strictly noncircular sources under the condition of unknown mutual coupling is concerned, and then a robust real‐valued weighted subspace fitting (WSF) algorithm is proposed via block sparse recovery. Inspired by noncircularity, the real‐valued coupled extended array output with double array aperture is first structured via exploiting the real‐valued conversion. Then, an efficient real‐valued block extended sparse recovery model is constructed by performing the parameterized decoupling operation to avoid the unknown mutual coupling and noncircular phase effects. Thereafter, the WSF framework is investigated to recover the real‐valued block sparse matrix, where the spectrum of real‐valued NC MUSIC‐like is utilized to design a weighted matrix for strengthening the solutions sparsity. Eventually, DOA estimation is achieved based on the support set of the reconstructed block sparse matrix. Owing to the combination of noncircularity, parametrized decoupling thought, and reweighted strategy, the proposed method not only effectively achieves high‐precision estimation, but also efficiently reduces the computational complexity. Plenty of simulation results demonstrate the effectiveness and efficiency of the proposed method.

Adaptive Radar Detection and Bearing Estimation in the Presence of Unknown Mutual Coupling

This paper deals with joint adaptive radar detection and target bearing estimation in the presence of mutual coupling among the array elements. First of all, a suitable model of the signal received by the multichannel radar is developed via a linearization procedure of the Uniform Linear Array (ULA) manifold around the nominal array looking direction together with the use of symmetric Toeplitz structured matrices to represent the mutual coupling effects. Hence, the Generalized Likelihood Ratio Test (GLRT) detector is evaluated under the assumption of homogeneous radar environment. Its computation leverages a specific Minorization-Maximization (MM) framework, with proven convergence properties, to optimize the concentrated likelihood function under the target presence hypothesis. Besides, when the number of active mutual coupling coefficients is unknown, a Multifamily Likelihood Ratio Test (MFLRT) approach is invoked. During the analysis phase, the performance of the new detectors is compared with benchmarks as well as with counterparts available in the open literature which neglect the mutual coupling phenomenon. The results indicate that it is necessary to consider judiciously the coupling effect since the design phase, to guarantee performance levels close to the benchmark.

Adaptive Target Detection and DOA Estimation With Uniform Rectangular Arrays in the Presence of Unknown Mutual Coupling

Adaptive Target Detection and DOA Estimation With Uniform Rectangular Arrays in the Presence of Unknown Mutual Coupling

DOA Estimation for Massive MIMO Systems with Unknown Mutual Coupling Based on Block Sparse Bayesian Learning.

Obtaining accurate angle parameters using direction-of-arrival (DOA) estimation algorithms is crucial for acquiring channel state information (CSI) in massive multiple-input multiple-output (MIMO) systems. However, the performance of the existing algorithms deteriorates severely due to mutual coupling between antenna elements in practical engineering. Therefore, for solving the array mutual coupling, the array output signal vector is modeled by mutual coupling coefficients and the DOA estimation problem is transformed into block sparse signal reconstruction and parameter optimization in this paper. Then, a novel sparse Bayesian learning (SBL)-based algorithm is proposed, in which the expectation-maximum (EM) algorithm is used to estimate the unknown parameters iteratively, and the convergence speed of the algorithm is enhanced by utilizing the approximate approximation. Moreover, considering the off-grid error caused by discretization processes, the grid refinement is carried out using the polynomial roots to realize the dynamic update of the grid points, so as to improve the DOA estimation accuracy. Simulation results show that compared with the existing algorithms, the proposed algorithm is more robust to mutual coupling and off-grid error and can obtain better estimation performance.

Subspace Pseudointensity Vectors Approach for DoA Estimation Using Spherical Antenna Array in the Presence of Unknown Mutual Coupling

Spherical antenna array (SAA) exhibits the ability to receive electromagnetic (EM) waves with the same signal strength, regardless of the direction-of-arrival (DoA), angle-of-arrival, and polarization. Hence, estimating the DoA of EM signals that impinge on SAA in the presence of mutual coupling requires research consideration. In this paper, a subspace pseudointensity vectors technique is proposed for DoA estimation using SAA with unknown mutual coupling. DoA estimation using an intensity vector technique is appealing due to its computational efficiency, particularly for SAAs. Two intensity vector-based techniques that operate with spherical harmonic decomposition (SHD) of an EM wave obtained from SAA are presented. The first technique employed pseudointensity vectors (PV) and operates quite well under EM conditions when one source is in operation each time, while the second technique employed subspace pseudointensity vectors (SPV) and operates under EM conditions when multi-sources and multiple reflection cause more challenging problems. The degree of correctness in the estimation of the DoA via the PVs and SPVs is measured using baseline methods in the literature via simulations, adding noise to stationary, single-source and multi-source methods. In addition, incorporating mutual coupling effects, data from experiments, which are the generally acceptable ground truth when examining any procedure, are further used to illustrate the robustness and efficiency of the proposed techniques. The results are sufficiently inspiring for the practical deployment of the proposed techniques.

Joint DOD and DOA Estimation for Bistatic MIMO Radar in the Presence of Unknown Mutual Coupling

Joint DOD and DOA Estimation for Bistatic MIMO Radar in the Presence of Unknown Mutual Coupling