一种基于迭代近端投影的被动声纳探测离网格DOA估计方法

Traditional direction of arrival(DOA)estimation methods based on sparse reconstruction commonly use convex or smooth functions to approximate non-convex and non-smooth sparse representation problems.This approach often introduces errors into the sparse representation model,necessitating the development of improved DOA estimation algorithms.Moreover,conventional DOA estimation methods typically assume that the signal coincides with a predetermined grid.However,in reality,this assumption often does not hold true.The likelihood of a signal not aligning precisely with the predefined grid is high,resulting in potential grid mismatch issues for the algorithm.To address the challenges associated with grid mismatch and errors in sparse representation models,this article proposes a novel high-performance off-grid DOA estimation approach based on iterative proximal projection(IPP).In the proposed method,we employ an alternating optimization strategy to jointly estimate sparse signals and grid offset parameters.A proximal function optimization model is utilized to address non-convex and non-smooth sparse representation problems in DOA estimation.Subsequently,we leverage the smoothly clipped absolute deviation penalty(SCAD)function to compute the proximal operator for solving the model.Simulation and sea trial experiments have validated the superiority of the proposed method in terms of higher resolution and more accurate DOA estimation performance when compared to both traditional sparse reconstruction methods and advanced off-grid techniques.

High-Precision DOA Estimation Method Based on Synthetic Aperture Technique

The existing direction-of-arrival(DOA)estimation methods only utilize the current received signals,which are susceptible to noise.In this paper,a method for DOA estimation based on a motion platform is proposed to achieve high-precision DOA estimation by utilizing past and present signals.The concept of synthetic aperture is introduced to construct a linear DOA estima-tion model.A DOA fine-tuning method based on the linear model is proposed to eliminate the lin-ear DOA variation,achieving a non-coherent accumulation of DOA estimations.Moreover,the baseband modulation and the phase modulation caused by the range history are compensated to achieve the coherent accumulation of all the DOA estimations.Simulation results show that the proposed method can significantly improve the DOA estimated accuracy at low signal-to-noise ratios(SNR).

Modified Eigendecomposition DOA EstimateAlgorithms and Field Test Studiesfor Super-Resolution

The signal direction of arrival (DOA) estimate algorithm based on the eigendecomposition of the modified covariance matrix is introduced in this paper. A field test system consisting of a 4-element linear array and a meter band radar is also presented, which is applied to the experimental studies of the algorithms in the practical performances. The results of the test indicate that when SNR is only 5.85 dB, two airplanes being 0.25 beam width apart in azimuth can be resolved clearly.

Improved Capon Estimator for High-Resolution DOA Estimation and Its Statistical Analysis

Despite some efforts and attempts have been made to improve the direction-of-arrival(DOA)estimation performance of the standard Capon beamformer(SCB)in array processing,rigorous statistical performance analyses of these modified Capon estimators are still lacking.This paper studies an improved Capon estimator(ICE)for estimating the DOAs of multiple uncorrelated narrowband signals,where the higherorder inverse(sample)array covariance matrix is used in the Capon-like cost function.By establishing the relationship between this nonparametric estimator and the parametric and classic subspace-based MUSIC(multiple signal classification),it is clarified that as long as the power order of the inverse covariance matrix is increased to reduce the influence of signal subspace components in the ICE,the estimation performance of the ICE becomes equivalent to that of the MUSIC regardless of the signal-to-noise ratio(SNR).Furthermore the statistical performance of the ICE is analyzed,and the large-sample mean-squared-error(MSE)expression of the estimated DOA is derived.Finally the effectiveness and the theoretical analysis of the ICE are substantiated through numerical examples,where the Cramer-Rao lower bound(CRB)is used to evaluate the validity of the derived asymptotic MSE expression.

Computationally efficient MUSIC based DOA estimation algorithm for FMCW radar

This paper proposes low-cost yet high-accuracy direction of arrival(DOA)estimation for the automotive frequency-modulated continuous-wave(FMcW)radar.The existing subspace-based DOA estimation algorithms suffer fromeither high computational costs or low accuracy.We aim to solve such contradictory relation between complexity and accuracy by using randomizedmatrix approximation.Specifically,we apply an easily-interpretablerandomized low-rank approximation to the covariance matrix(CM)and R∈C^(M×M)throughthresketch maties in the fom of R≈OBQ^(H).Here the approximately compute its subspaces.That is,we first approximate matrix Q∈C^(M×z)contains the orthonormal basis for the range of the sketchmatrik C∈C^(M×z)cwe whichis etrated fom R using randomized unifom counsampling and B∈C^(z×z)is a weight-matrix reducing the approximation error.Relying on such approximation,we are able to accelerate the subspacecomputation by the orders of the magnitude without compromising estimation accuracy.Furthermore,we drive a theoretical error bound for the suggested scheme to ensure the accuracy of the approximation.As validated by the simulation results,the DOA estimation accuracy of the proposed algorithm,eficient multiple signal classification(E-MUSIC)s high,closely tracks standardMUSIC,and outperforms the well-known algorithms with tremendouslyreduced time complexity.Thus,the devised method can realize high-resolutionreal-time target detection in the emerging multiple input and multiple output(MIMO)automotive radar systems.

Exploration on 2D DOA Estimation of Linear Array Motion:Uniform Linear Motion

Generally,due to the limitation of the dimension of the array aperture,linear arrays cannot achieve two-dimensional(2D)direction of arrival(DOA)estimation.But the emergence of array motion provides a chance for that.In this paper,a generalized motion scheme and a novel method of 2D DOA estimation are proposed by exploring the linear array motion.To be specific,the linear arrays are controlled to move along an arbitrary direction at a constant velocity and snap per fixed time delay.All the received signals are processed to synthesize the comprehensive observation vector for an extended 2D virtual aperture.Subsequently,since most of 2D DOA estimation methods are not universal to our proposed motion scheme and the reduced-dimensional(RD)method fails to handle the case of the coupled parameters,a decoupled reduced-complexity multiple signals classification(DRC MUSIC)algorithm is designed specifically.Simulation results demonstrate that:a)our proposed scheme can achieve underdetermined 2D DOA estimation just by the linear arrays;b)our designed DRC MUSIC algorithm has the good properties of high accuracy and low complexity;c)our proposed motion scheme with the DRC method has better universality in the motion direction.

MDTCNet:Multi-Task Classifications Network and TCNN for Direction of Arrival Estimation

The direction-of-arrival(DoA) estimation is one of the hot research areas in signal processing. To overcome the DoA estimation challenge without the prior information about signal sources number and multipath number in millimeter wave system,the multi-task deep residual shrinkage network(MTDRSN) and transfer learning-based convolutional neural network(TCNN), namely MDTCNet, are proposed. The sampling covariance matrix based on the received signal is used as the input to the proposed network. A DRSN-based multi-task classifications model is first introduced to estimate signal sources number and multipath number simultaneously. Then, the DoAs with multi-signal and multipath are estimated by the regression model. The proposed CNN is applied for DoAs estimation with the predicted number of signal sources and paths. Furthermore, the modelbased transfer learning is also introduced into the regression model. The TCNN inherits the partial network parameters of the already formed optimization model obtained by the CNN. A series of experimental results show that the MDTCNet-based DoAs estimation method can accurately predict the signal sources number and multipath number under a range of signal-to-noise ratios. Remarkably, the proposed method achieves the lower root mean square error compared with some existing deep learning-based and traditional methods.

A Decorrelating DOA Estimation Based on the Principal Eigenvector for CDMA Frequency-Selective Fading Channels

A novel decorrelating DOA estimation algorithm of multipath signals for CDMA frequency selective fading channels based only on the principal eigenvector of its corresponding covariance matrix is proposed. The proposed algorithm has the advantages that the DOAs of the multipath signals can be estimated independently and all the other resolved multipath signal interference is eliminated. Simulation results show that this algorithm estimates the DOAs of multipath signals efficiently and accurately.

Joint DOA Estimation and Phase Calibration for Synchronous CDMA with Decorrelator

A joint direction of arrival (DOA) estimation and phase calibration for synchronous CDMA system with decorrelator are presented. Through decorrelating processing DOAs of the desired users can be estimated independently and all other resolved signal interferences are eliminated. Emphasis is directed to applications in which sensor phases may be in error. It is shown that accurate phase calibration in conjunction with their use in high resolution DOA estimation can be achieved for the decoupled signals.

Triad-displaced ULAs configuration for non-circular sources with larger continuous virtual aperture and enhanced degrees of freedom

Non-uniform linear array(NULA)configurations are well renowned due to their structural ability for providing increased degrees of freedom(DOF)and wider array aperture than uniform linear arrays(ULAs).These characteristics play a significant role in improving the direction-of-arrival(DOA)estimation accuracy.However,most of the existing NULA geometries are primarily applicable to circular sources(CSs),while they limitedly improve the DOF and continuous virtual aperture for noncircular sources(NCSs).Toward this purpose,we present a triaddisplaced ULAs(Tdis-ULAs)configuration for NCS.The TdisULAs structure generally consists of three ULAs,which are appropriately placed.The proposed antenna array approach fully exploits the non-circular characteristics of the sources.Given the same number of elements,the Tdis-ULAs design achieves more DOF and larger hole-free co-array aperture than its sparse array competitors.Advantageously,the number of uniform DOF,optimal distribution of elements among the ULAs,and precise element positions are uniquely determined by the closed-form expressions.Moreover,the proposed array also produces a filled resulting co-array.Numerical simulations are conducted to show the performance advantages of the proposed Tdis-ULAs configuration over its counterpart designs.

DOA Estimation Algorithm Based on Adaptive Filtering in Spatial Domain

In this paper, a novel DOA estimation methodology based upon the technology of adaptive nulling antenna is proposed. Initially, the nulling antenna obtains the weight vector by LMS algorithm and power inversion criterion.Afterwards, reciprocal of the antenna pattern is defined as the spatial spectrum and the extracted peak values are corresponded to the estimated DOA. Through observation of the spectrum and data analysis of variable steps and SNRs, the simulation results demonstrate that the proposed method can estimate DOA above board. Furthermore, the estimation error of the proposed technique is directly proportional to step size and is inversely proportional to SNR. Unlike the existing MUSIC algorithm, the proposed algorithm has less computational complexity as it eliminates the need of estimating the number of signals and the eigenvalue decomposition of covariance matrix. Also it outperforms MUSIC algorithm, the recently proposed MUSIC-Like algorithm and classical methods by achieving better resolution with narrow width of peaks.

A Modified Root-MUSIC Algorithm for Signal DOA Estimation

This paper presents a modified Root-MUSIC algorithm by which the signal DOA estimation performance can be improved when the snapshot number is limited. The operation principlesof this algorithm are described in detail. It is also pointed out theoretically that this is equivalentto have increased the snapshot number and can make the DOA estimation better. Finally, somesimulating results to verify the theoretical analyses are presented.

Novel algorithm on DOA estimation for correlated sources under complex symmetric Toeplitz noise

To cope with the scenario where both uncorrelated sources and coherent sources coexist, a novel algorithm to direction of arrival （DOA） estimation for symmetric uniform linear array is presented. Under the condition of stationary colored noise field, the algorithm employs a spatial differencing method to eliminate the noise covariance matrix and uncorrelated sources, then a Toeplitz matrix is constructed for the remained coherent sources. After preprocessing, a propagator method （PM） is employed to find the DOAs without any eigendecomposition. The number of sources resolved by this approach can exceed the number of array elements at a lower computational complexity. Simulation results demonstrate the effectiveness and efficiency of the proposed method.

Blind identification and DOA estimation for array sources in presence of scattering

A novel identification method for point source,coherently distributed（CD） source and incoherently distributed（ICD） source is proposed.The differences among the point source,CD source and ICD source are studied.According to the different characters of covariance matrix and general steering vector of the array received source,a second order blind identification method is used to separate the sources,the mixing matrix could be obtained.From the mixing matrix,the type of the source is identified by using an amplitude criterion.And the direction of arrival for the array received source is estimated by using the matching pursuit algorithm from the vectors of the mixing matrix.Computer simulations validate the efficiency of the method.

DOA estimation of coexisted noncoherent and coherent signals via sparse representation of cleaned array covariance matrix

A new direction finding method is presented to deal with coexisted noncoherent and co- herent signals without smoothing operation. First the direction-of-arrival （DOA） estimation task is herein reformulated as a sparse reconstruction problem of the cleaned array covariance matrix, which is processed to eliminate the affection of the noise. Then by using the block of matrices, the information of DOAs which we pursuit are implied in the sparse coefficient matrix. Finally, the sparse reconstruction problem is solved by the improved M-FOCUSS method, which is applied to the situation of block of matrices. This method outperforms its data domain counterpart in terms of noise suppression, and has a better performance in DOA estimation than the customary spatial smoothing technique. Simulation results verify the efficacy of the proposed method.

Underdetermined DOA estimation via multiple time-delay covariance matrices and deep residual network

Higher-order statistics based approaches and signal sparseness based approaches have emerged in recent decades to resolve the underdetermined direction-of-arrival(DOA)estimation problem.These model-based methods face great challenges in practical applications due to high computational complexity and dependence on ideal assumptions.This paper presents an effective DOA estimation approach based on a deep residual network(DRN)for the underdetermined case.We first extract an input feature from a new matrix calculated by stacking several covariance matrices corresponding to different time delays.We then provide the input feature to the trained DRN to construct the super resolution spectrum.The DRN learns the mapping relationship between the input feature and the spatial spectrum by training.The proposed approach is superior to existing model-based estimation methods in terms of calculation efficiency,independence of source sparseness and adaptive capacity to non-ideal conditions(e.g.,low signal to noise ratio,short bit sequence).Simulations demonstrate the validity and strong performance of the proposed algorithm on both overdetermined and underdetermined cases.

2D DOA Estimation Algorithm with Increased Degrees of Freedom for Two Parallel Linear Arrays

In this paper,a two-dimensional(2 D)direction-of-arrival(DOA)estimation algorithm with increased degrees of freedom for two parallel linear arrays is presented.Being different from the conventional two-parallel linear array,the proposed two-parallel linear array consists of two uniform linear arrays with non-equal inter-element spacing.Propagator method(PM)is used to obtain a special matrix which can be utilized to increase the virtual elements of one of uniform linear arrays.Then,the PM algorithm is used again to obtain automatically paired elevation and azimuth angles.The simulation results and complexity analysis show that the proposed method can increase the number of distinguishable signals and improve the estimation precision without increasing the computational complexity.

Estimation of DOA and Doppler Frequency on Nonideal UCA

The problem of estimating direction of arrivals (DOA) and Doppler frequency for many sources is considered in the presence of general array errors (such as amplitude and phase error of sensors, setting position error of sensors). Adopting direct array manifold in a uniform circular array (UCA), the estimation of Doppler frequency can be obtained by DOA matrix. Based on analyzing the statistic characters of general array errors, the estimation of DOA can be obtained by Weight Total Least Squares. Numerical results illustrate that the estimator is robust to general array errors and show the capabilities of the estimator.

Computationally Efficient 2DDOA Estimation for Uniform Planar Arrays:RDROOTMUSIC Algorithm

The problem of two-dimensional direction of arrival(2D-DOA)estimation for uniform planar arrays(UPAs)is investigated by employing the reduced-dimensional(RD)polynomial root finding technique and 2D multiple signal classification(2D-MUSIC)algorithm.Specifically,based on the relationship between the noise subspace and steering vectors,we first construct 2D root polynomial for 2D-DOA estimates and then prove that the 2D polynomial function has infinitely many solutions.In particular,we propose a computationally efficient algorithm,termed RD-ROOT-MUSIC algorithm,to obtain the true solutions corresponding to targets by RD technique,where the 2D root-finding problem is substituted by two one-dimensional(1D)root-finding operations.Finally,accurate 2DDOA estimates can be obtained by a sample pairing approach.In addition,numerical simulation results are given to corroborate the advantages of the proposed algorithm.

Low complexity DOA estimation for massive UCA with single snapshot

In this paper, a low complexity direction of arrival(DOA) estimation method for massive uniform circular array(UCA) with single snapshot is proposed.Firstly, the coarse DOAs are estimated by finding the peaks from the circular convolution between a fixed coefficient vector and the received data vector.Thereafter, in order to refine coarse DOA estimates, we reconstruct the direction matrix based on the coarse DOA estimations and take the first order Taylor expansion with DOA estimation offsets into account.Finally, the refined estimations are obtained by compensating the offsets, which are obtained via least squares(LS) without any complex searches.In addition, the refinement can be iteratively implemented to enhance the estimation results.Compared to the offset search method, the proposed method achieves a better estimation performance while requiring lower complexity.Numerical simulations are presented to demonstrate the effectiveness of the proposed method.