Wideband direction-of-arrival estimation based on cubic spline function

A new direction-of-arrival （DOA） estimation algorithm for wideband sources is introduced, The new method obtains the output of the virtual arrays in the signal bandwidth using cubic spline function interpolation techniques. The narrowband high- resolution algorithm is then used to get the DOA estimation. This technique does not require any preliminary knowledge of DOA angles. Simulation results demonstrate the effectiveness of the method.

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).

Polynomial-rooting based fourth-order MUSIC for direction-of-arrival estimation of noncircular signals

A polynomial-rooting based fourth-order cumulant algorithm is presented for direction-of-arrival（DOA） estimation of second-order fully noncircular source signals, using a uniform linear array（ULA）. This algorithm inherits all merits of its spectralsearching counterpart except for the applicability to arbitrary array geometry, while reducing considerably the computation cost.Simulation results show that the proposed algorithm outperforms the previously developed closed-form second-order noncircular ESPRIT method, in terms of processing capacity and DOA estimation accuracy, especially in the presence of spatially colored noise.

Direction-of-arrival estimation based on direct data domain (D3) method

A direction-of-arrival （DOA） estimation algorithm based on direct data domain （D3） approach is presented. This method can accuracy estimate DOA using one snapshot modified data, called the temporal and spatial two-dimensional vector reconstruction （TSR） method. The key idea is to apply the D3 approach which can extract the signal of given frequency but null out other frequency signals in temporal domain. Then the spatial vector reconstruction processing is used to estimate the angle of the spatial coherent signal source based on extract signal data. Compared with the common temporal and spatial processing approach, the TSR method has a lower computational load, higher real-time performance, robustness and angular accuracy of DOA. The proposed algorithm can be directly applied to the phased array radar of coherent pulses. Simulation results demonstrate the performance of the proposed technique.

DIRECTION-OF-ARRIVAL ESTIMATION IN THE PRESENCE OF MUTUAL COUPLING BASED ON JOINT SPARSE RECOVERY

A novel Direction-Of-Arrival (DOA) estimation method is proposed in the presence of mutual coupling using the joint sparse recovery. In the proposed method, the eigenvector corresponding to the maximum eigenvalue of covariance matrix of array measurement is viewed as the signal to be represented. By exploiting the geometrical property in steering vectors and the symmetric Toeplitz structure of Mutual Coupling Matrix (MCM), the redundant dictionaries containing the DOA information are constructed. Consequently, the optimization model based on joint sparse recovery is built and then is solved through Second Order Cone Program (SOCP) and Interior Point Method (IPM). The DOA estimates are gotten according to the positions of nonzeros elements. At last, computer simulations demonstrate the excellent performance of the proposed method.

Direction-of-arrival estimation of quasi-stationary signals using two-level Khatri-Rao subspace and four-level nested array

The Khatri-Rao(KR) subspace method is a high resolution method for direction-of-arrival(DOA) estimation.Combined with 2q level nested array,the KR subspace method can detect O(N2q) sources with N sensors.However,the method cannot be applicable to Gaussian sources when q is equal to or greater than 2 since it needs to use 2q-th order cumulants.In this work,a novel approach is presented to conduct DOA estimation by constructing a fourth order difference co-array.Unlike the existing DOA estimation method based on the KR product and 2q level nested array,the proposed method only uses second order statistics,so it can be employed to Gaussian sources as well as non-Gaussian sources.By exploiting a four-level nested array with N elements,our method can also identify O(N4) sources.In order to estimate the wideband signals,the proposed method is extended to the wideband scenarios.Simulation results demonstrate that,compared to the state of the art KR subspace based methods,the new method achieves higher resolution.

DIRECTION-OF-ARRIVAL ESTIMATION BY CONTINUATION METHOD

In this paper, the subspace fitting models for direction-of-arrival (DOA) estimation is analyzed, an effective algorithmic approach is given. As the initialization value is so critical to the global convergence, the continuation theory is also used to develop a new framework which solves the initialization problem powerfully. Some numerical evidence will be given to show that the performance of the new algorithm is very promising.

Wideband Direction-of-Arrival Estimation Based on Deep Learning

The performance of traditional high-resolution direction-of-arrival(DOA)estimation methods is sensitive to the inaccurate knowledge on prior information,including the position of ar-ray elements,array gain and phase,and the mutual coupling between the array elements.Learning-based methods are data-driven and are expected to perform better than their model-based counter-parts,since they are insensitive to the array imperfections.This paper presents a learning-based method for DOA estimation of multiple wideband far-field sources.The processing procedure mainly includes two steps.First,a beamspace preprocessing structure which has the property of fre-quency invariant is applied to the array outputs to perform focusing over a wide bandwidth.In the second step,a hierarchical deep neural network is employed to achieve classification.Different from neural networks which are trained through a huge data set containing different angle combinations,our deep neural network can achieve DOA estimation of multiple sources with a small data set,since the classifiers can be trained in different small subregions.Simulation results demonstrate that the proposed method performs well both in generalization and imperfections adaptation.

Underdetermined DOA estimation and blind separation of non-disjoint sources in time-frequency domain based on sparse representation method

This paper deals with the blind separation of nonstation-ary sources and direction-of-arrival （DOA） estimation in the under-determined case, when there are more sources than sensors. We assume the sources to be time-frequency （TF） disjoint to a certain extent. In particular, the number of sources presented at any TF neighborhood is strictly less than that of sensors. We can identify the real number of active sources and achieve separation in any TF neighborhood by the sparse representation method. Compared with the subspace-based algorithm under the same sparseness assumption, which suffers from the extra noise effect since it can-not estimate the true number of active sources, the proposed algorithm can estimate the number of active sources and their cor-responding TF values in any TF neighborhood simultaneously. An-other contribution of this paper is a new estimation procedure for the DOA of sources in the underdetermined case, which combines the TF sparseness of sources and the clustering technique. Sim-ulation results demonstrate the validity and high performance of the proposed algorithm in both blind source separation （BSS） and DOA estimation.

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.

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.

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.

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.

Low-complexity method for DOA estimation based on ESPRIT

A low-complexity method for direction of arrival（DOA） estimation based on estimation signal parameters via rotational invariance technique（ESPRIT） is proposed.Instead of using the cross-correlation vectors in multistage Wiener filter（MSWF）,the orthogonal residual vectors obtained in conjugate gradient（CG） method span the signal subspace used by ESPRIT.The computational complexity of the proposed method is significantly reduced,since the signal subspace estimation mainly needs two matrixvector complex multiplications at the iteration of data level.Furthermore,the prior training data are not needed in the proposed method.To overcome performance degradation at low signal to noise ratio（SNR）,the expanded signal subspace spanned by more basis vectors is used and simultaneously renders ESPRIT yield redundant DOAs,which can be excluded by performing ESPRIT once more using the unexpanded signal subspace.Compared with the traditional ESPRIT methods by MSWF and eigenvalue decomposition（EVD）,numerical results demonstrate the satisfactory performance of the proposed method.

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.

Multiple Emitters Localization by UAV with Nested Linear Array:System Scheme and 2D-DOA Estimation Algorithm

Unmanned Aerial Vehicle(UAV)equipped with uniform linear array has been applied to multiple emitters localization.Meanwhile,nested linear array enables to enhance localization resolution and achieve under-determined Direction of Arrival(DOA)estimation.In this paper,we propose a new system structure for emitters localization that combines the UAV with nested linear array,which is capable of significantly increasing the positioning accuracy of interested targets.Specifically,a localization scheme is designed to obtain the paired two-dimensional DOA(2D-DOA,i.e.azimuth and elevation angles)estimates of emitters by nested linear array with UAV.Furthermore,we propose an improved DOA estimation algorithm for emitters localization that utilizes Discrete Fourier Transform(DFT)method to obtain coarse DOA estimates,subsequently,achieve the fine DOA estimates by sparse representation.The proposed algorithm has lower computational complexity because the coarse DOA estimates enable to shrink the range of over-complete dictionary of sparse representation.In addition,compared to traditional uniform linear array,improved 2D-DOA estimation performance of emitters can be obtained with a nested linear array.Extensive simulation results testify the effectiveness 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.

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.

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.