Presence Of Unknown Mutual Coupling Research Articles

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.

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.

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

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

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.

Source Localization of EM Waves in the Near-Field of Spherical Antenna Array in the Presence of Unknown Mutual Coupling

To obtain an antenna array with isotropic radiation, spherical antenna array (SAA) is the right array configuration. The challenges of locating signals transmitted within the proximity of antenna array have been investigated considerably in the literature. However, near-field (NF) source localization of signals has hitherto not been investigated effectively using SAA in the presence of mutual coupling (MC). MC is another critical problem in antenna arrays. This paper presents an NF range and direction-of-arrival (DoA) estimation technique via the direction-independent and signal invariant spherical harmonics (SH) characteristics in the presence of mutual coupling. The energy of electromagnetic (EM) signal on the surface of SAA is captured successfully using a proposed pressure interpolation approach. The DoA estimation within the NF region is then calculated via the distribution of pressure. The direction-independent and signal invariant characteristics, which are SH features, are obtained using the DoA estimates in the NF region. We equally proposed a learning scheme that uses the source activity detection and convolutional neural network (CNN) to estimate the range of the NF source via the direction-independent and signal invariant features. Considering the MC problem and using the DoA estimates, an accurate spectrum peak in the multipath situation in conjunction with MC and a sharper spectrum peak from a unique MC structure and smoothing algorithms are obtained. For ground truth performance evaluation of the SH features within the context of NF localization, a numerical experiment is conducted and measured data were used for analysis to incorporate the MC and consequently computed the root mean square error (RMSE) of the source range and NF DoA estimate. The results obtained from numerical experiments and measured data indicate the validity and effectiveness of the proposed approach. In addition, these results are motivating enough for the deployment of the proposed method in practical applications.

Block Newtonised orthogonal matching pursuit for off‐grid DOA estimation in the presence of unknown mutual coupling

Mutual coupling (MC) between array elements can lead to severe accuracy degradation in direction-of-arrival (DOA) estimation. In this study, an off-grid compressed sensing (CS) approach for DOA estimation in the presence of unknown MC is proposed using block Newtonised orthogonal matching pursuit (NOMP). First, the model of DOA estimation with MC is block sparsely represented to fully utilise the whole array aperture. Then, a block NOMP algorithm with multiple measurement vectors (MMV) is developed to achieve the estimates of DOA, in which the Newton refinement procedure is presented by deriving the first- and second-order derivatives of the block steering matrix with respect to the angle grid. Compared with the subspace-based and sparse recovery-based methods with MC, the proposed block NOMP approach has robustness in a small number of snapshots and owns no grid-mismatch effect. Meanwhile, in comparison to several existing off-grid DOA estimation methods with MC, it can achieve higher estimation accuracy and similarly low computational complexity. The superior performance of the proposed approach is shown via numerical simulations.

Novel robust adaptive beamforming against unknown mutual coupling

Mutual coupling effects among the array elements can degrade the performance of the beamformer seriously. In this paper, we propose a novel robust adaptive beamforming method, which is robust against unknown mutual coupling. The main idea is to reconstruct the interference-plus-noise covariance matrix (IPNCM) with the estimated eigenvector and eigenvalue corresponding to the interference. The eigenvector lies in the intersection of two special subspaces. The first subspace is the signal-plus-interference (SPI) subspace got from the sample covariance matrix (SCM). The second subspace, which is interference subspace, can be obtained by integrating the conduction matrix in each interference angular sector. According to the correlation between the estimated eigenvector and SPI subspace, the eigenvalues corresponding to each interference are determined. Therefore, the IPNCM is reconstructed. The desired signal steering vector (SV) is estimated by a similar method. Simulation results show that the proposed beamformer outperforms other methods in the presence of unknown mutual coupling.

STAP Based on Toeplitz Covariance Matrix Reconstruction for Airborne Radar

We consider the problem of clutter covariance matrix (CCM) estimation for space-time adaptive processing (STAP) radar in the small sample. In this paper, a fast efficient algorithm for CCM reconstruction is proposed to overcome this shortcoming for the linear structure. Particularly, we present a low-rank matrix recovery (LRMR) question about CCM estimation based on the Toeplitz structure of CCM and the prior knowledge of the noise. The closed-form solution is obtained by relaxing the nonconvex LRMR problem that the trace norm replaces the rank norm. The target can then be efficiently detected by using the recovered CCM according to the STAP theorem. We also analyze the algorithm model under the linear structure in the presence of unknown mutual coupling. It is shown that our method can obtain accurate CCM in the small sample, with even higher accuracy than the traditional algorithms in the same number of samples. It also can reduce the coupling effect and obtain more degrees of freedom (DOF) with limited sensors and pulses by utilizing sparse linear structure (SLS) and improve angle and Doppler resolutions. Finally, numerical simulations have verified the effectiveness of the proposed method in comparison with some of the existing methods.

Robust Sparse Representation for DOA Estimation With Unknown Mutual Coupling Under Impulsive Noise

A variety of approaches have been exploited for DOA estimation with unknown mutual coupling in recent years. However, they usually suffer performance degradation under impulsive noise. To overcome this problem, an improved method for DOA estimation in the presence of unknown mutual coupling is proposed. Firstly, a sparse representation of the received signals is created by the aid of the structure of mutual coupling matrix (MCM). Subsequently, a Cauchy-score based cost function is utilized to reconstruct the sparse model and realize DOA estimation. Numerous experiments are carried out to evaluate the algorithms and simulation results confirm that our method shows superior performance than existing methods.

Robust DOA Estimation Against Mutual Coupling With Nested Array

In this letter, we focus on the problem of direction-of-arrival (DOA) estimation with a nested array in the presence of unknown mutual coupling. Firstly, the measurement model of the nested array with mutual coupling is established, and the properties of the corresponding array covariance matrix are derived. Based on the obtained properties, the coarray output signal is then reconstructed with reduced mutual coupling. Finally, joint sparse recovery technique is employed to extract the DOAs. Our algorithm can achieve underdetermined DOA estimation with satisfactory performance under unknown mutual coupling. Numerical results demonstrate the effectiveness of the proposed algorithm.

A Novel Block Sparse Reconstruction Method for DOA Estimation With Unknown Mutual Coupling

In this letter, we consider the direction-of-arrival (DOA) estimation in the presence of unknown mutual coupling in application to uniform linear arrays (ULAs). A novel method is proposed that treats the DOA estimation as a block sparse signal reconstruction problem with a modified array manifold matrix which utilizes the information of entire array output. A block smoothed $\ell _{0}$ norm approximation technique is introduced to solve the problem. Finally, an iterative proximal algorithm is proposed. Simulation results are presented that show the superiority of the proposed method against other known techniques.

DOA Estimation Under Mutual Coupling of Uniform Linear Arrays Using Sparse Reconstruction

A novel sparse reconstruction method is developed for direction of arrival (DOA) estimation in the presence of unknown mutual coupling of uniform linear arrays. In the proposed method, a sparse representation for single measurement vector (SMV) is first derived. Then, it is shown that the problem size can be reduced by a linear transformation to eliminate the redundant components in the SMV. Finally, by taking advantage of the banded symmetric Toeplitz structure of the mutual coupling matrix, a reweighted ${\mathcal {\ell }_{1}}$ -norm minimization subject to an error-constrained ${\mathcal {\ell }_{2}}$ -norm is introduced to determine the DOA estimates without mutual coupling compensation, further enhancing the sparsity and providing a robustness against the noise. Simulation results demonstrate the superiority of the proposed method over its state-of-the-art counterparts.

Block rank sparsity-aware DOA estimation with large-scale arrays in the presence of unknown mutual coupling

With the development of massive multiple-input mutiple-output (MIMO) technique, high-resolution direction-of-arrival (DOA) estimation has attracted great attention. A novel sparse signal reconstruction method based on the inherent block rank sparsity of the sub-matrix is proposed for high resolution DOA estimation with large-scale arrays under the condition of unknown mutual coupling. In the proposed method, by taking advantage of the banded symmetric Toeplitz structure of the mutual coupling matrix (MCM), a novel block representation model is firstly formulated by parameterizing the steering vector. Then, exploiting the inherent block sparsity characteristics of the sub-matrix, a reweighted nuclear norm minimization algorithm is proposed to reconstruct the sparse matrix, in which the weighted matrix is designed by using the spectrum of MUSIC-Like algorithm. Finally, the DOAs are achieved by searching the non-zeros blocks of the recovered matrix. The proposed method not only makes full use of the block rank sparsity characteristics of the sub-matrix and weighted matrix for enhancing the sparse solution, but also avoids the array aperture loss. Thus, the proposed method has superior estimation performance than the state-of-the-art algorithms under the condition of unknown mutual coupling. Especially, in the case of large-scale antennas, the advantage of the proposed method is more obvious. Some computer simulation results are performed to verify the advantage of our proposed method.

Sparse Bayesian Learning Based Space-Time Adaptive Processing Against Unknown Mutual Coupling for Airborne Radar Using Middle Subarray

Sparse-recovery-based space–time adaptive processing (STAP) methods can exhibit superior clutter suppression performance with limited training data. However, the clutter suppression performance seriously degrades when the mutual coupling is present in the STAP array elements. In this paper, a sparse Bayesian learning (SBL)-based STAP method against the mutual coupling by using the middle subarray is proposed. Specifically, the mutual coupling matrix (MCM) of the STAP uniform linear array is approximately described as a banded symmetric Toeplitz matrix since the effect of mutual coupling between two array elements is inversely related to their distance and can be negligible when their distance is larger than a few wavelengths. Utilizing this specific structure of the MCM, a mutual coupling mitigation method is developed for STAP by rearranging the received snapshots with the designed spatial–temporal selection matrix. Then, to efficiently recover the clutter angle-Doppler profile, the modified fast converging SBL (FCSBL) named adaptive FCSBL algorithm and its multiple measurement vector case adaptive MFCSBL algorithm are developed with only partial hyper-parameters being updated in a single iteration. The proposed method not only achieves the superior clutter suppression performance in the presence of unknown mutual coupling, but also has low computational load. The simulation results are implemented to validate the effectiveness of the proposed methods.

An Efficient 2-D DOA Estimation for a Cylindrical Conformal Array with Unknown Mutual Coupling

The limited space of a conformal array may lead to a serious mutual coupling effect, which will significantly affect the performance of direction of arrival (DOA) estimation algorithms. In this paper, an efficient 2-D direction finding method is developed in the presence of unknown mutual coupling for the uniform cylindrical conformal array (CCA). To avoid the time-consuming two-dimensional spectral peak searching, the 2-D DOA estimation is decoupled and divided into two 1-D DOA estimations. Elevation is first estimated based on a subarray estimation of signal parameters via rotation invariant technique (ESPRIT), and then azimuth is estimated based on the rank reduction (RARE) method by using the elevation estimation result. Consequently, the mutual coupling coefficients can be estimated after getting the DOA estimates. The proposed method can well calibrate the mutual coupling effect of a cylindrical array with a low computational complexity. The final simulation results corroborate our analysis.

The Critical Patients Localization Algorithm Using Sparse Representation for Mixed Signals in Emergency Healthcare System

In this paper, a new architecture for the emergency healthcare system based on mobile cloud computation (MCC) and fifth-generation (5G) wireless link is proposed. Based on the processing speed of MCC and the communication rate of 5G scheme, this system can monitor and locate patients in real time. Multiple base stations of the massive multiple input multiple output cooperate with each other to locate the patients using cross-location method. Then, a new direction-of-arrival (DOA) estimation algorithm is proposed in the presence of unknown mutual coupling. The uncorrelated signals are first estimated using the estimation of signal parameters via rotational invariance technique algorithm. Then, these estimators construct the manifold matrix of uncorrelated signals to estimate the mutual coupling coefficients. The information about uncorrelated signals and mutual coupling is eliminated based on oblique projection technique in order to estimate the DOAs of coherent signals using the original array. Finally, the DOAs of coherent signals are achieved based on sparse representation theory. Simulation results demonstrate the effectiveness and performance of the proposed algorithm.

An optimal robust adaptive beamforming in the presence of unknown mutual coupling

An optimal robust adaptive beamformer in the presence of unknown mutual coupling is proposed. In this proposed beamformer, envelopes of the received signals in the presence of unknown mutual coupling and their corresponding powers can be estimated by utilizing the Toeplitz characteristics of the mutual coupling matrix. Both of them are used to reconstruct the interference-plus-noise covariance matrix in a novel expression. A subspace orthogonal to the interference space can be obtained by performing the eigenvalue decomposition on this reconstructed matrix. Hence, the desired signal and the noise are retained by projecting the observed data to this orthogonal space. Finally, the optimal weight vector is obtained by passing the desired signal with the maximum output power criterion. The proposed method maintains excellent performance in the presence of unknown mutual coupling and the simulation results are consistent with the theoretical analysis.

Robust Adaptive Beamforming Against Mutual Coupling Based on Mutual Coupling Coefficients Estimation

In an adaptive beamforming system, the mutual coupling effects among the array elements can seriously degrade the system performance. In this paper, we propose a robust adaptive beamforming algorithm using a uniform linear array (ULA) to mitigate the mutual coupling effects. The proposed algorithm is based on the fact that the mutual coupling matrix (MCM) of a ULA can be approximated as a banded symmetric Toeplitz matrix as the mutual coupling between two sensors is inversely related to their separation and is negligible for a few wavelengths away. By exploiting the structural characteristics of the MCM, a subspace-based method is used to estimate the mutual coupling coefficients of the ULA that yield a closed-form solution, and the MCM is further constructed. Then, the constructed MCM and array received data are used to reconstruct the interference-plus-noise covariance (INC) matrix. Finally, the robust adaptive beamformer is created through using the Capon principle and the reconstructed INC matrix. Unlike most of the existing algorithms, the proposed algorithm only requires prior knowledge of the array geometry. Simulation results indicate that our approach outperforms the compared algorithms in the presence of unknown mutual coupling and can achieve a performance close to the theoretical optimal value.