A Multidimensional Matrix Completion Method for 2-D DOA Estimation with L-Shaped Array

A modified propagator method based on L-shaped array for 2-Dimensional (2-D) direction of arrival (DOA) estimation in monostatic MIMO radar is proposed. A cross-correlation matrix, which can eliminate the influence of noise, is constructed by the received data from the two orthogonal uniform linear arrays (ULAs) at x-axis and z-axis. Then the matrix can be utilized to estimate signal subspace of 2-D DOA through propagator method. At last, the elevation and azimuth angles of the 2-D DOA is automatically paired by the complex eigenvalues of a low-order complex matrix. The 2-D DOA estimation performance of the proposed method is better than conventional propagator method and ESPRIT algorithm. Simulation results verify the effectiveness of the proposed method.

In the two-dimensional Direction Of Arrival (DOA) estimation of coherently distributed noncircular sources, the problem of large complexity is caused by dimension expansion after exploiting noncircular property, meanwhile the existing low-complexity algorithms all require additional parameter pairing procedure. To solve these problems, a rapid DOA estimation algorithm with automatic pairing is proposed for coherently distributed noncircular sources based on cross-correlation propagator. The L-shaped array is considered. Firstly, the extended array manifold model is established by exploiting the noncircularity of the signal, and then it is proved that there are approximate rotational invariance relationships in the Generalized Steering Vectors (GSVs) of two subarrays of the L array. At the same time, the extra noise can be eliminated by the cross-correlation matrix of the array output signals. Finally, on the basis of the approximate rotational invariance relationships of the sub-arrays, the center azimuth and elevation DOAs can be obtained by propagator method. Theoretical analysis and simulation experiments show that without the spectrum searching and eigenvalue decomposition of the sample covariance matrix, the proposed algorithm has low computational complexity. Moreover, it can automatically pair the estimated central azimuth and central elevation DOAs. In addition, compared with the existing propagation method for coherently distributed noncircular sources, the proposed algorithm can achieve higher estimation accuracy with the small complexity cost.

According to the heavy computation and high cost of two-dimensional (2D) multiple signal classification (MUSIC) to achieve 2D direction of arrival (DOA) estimation in various complex arrays, this paper proposes a reduced-dimensional (RD) estimation algorithm based on L-shaped uniform array without the need of 2D spectral peak search and secondary optimization. This algorithm makes full use of the structural characteristics of L-shaped array, decomposes the L-shaped uniform array into two uniform linear arrays, and estimates the angle between the source and the X-axis and Y-axis by one-dimensional (1D) search respectively, then obtains the 2D-DOA estimation according to the geometric relationship and uses the maximum likelihood method for angle matching. In this algorithm, the time-consuming 2D search is transformed into 1D search, which greatly reduces the computational complexity. In order to further reduce the complexity and improve the estimation accuracy, the root-finding method can be used instead of one-dimensional search. The simulation results show that the proposed algorithm has higher DOA estimation performance as well as faster operation speed.

In this paper, we investigate the problem of two-dimensional (2-D) direction of arrival (DOA) estimation in a distributed L-shaped array (LSA). The resolution of 2-D DOA estimation in distributed uniform L-shaped array is low, the number of available sources is limited by array elements and the estimation accuracy is susceptible to SNR. In order to promote the 2-D DOA estimation performance under distributed L-shaped array, this paper proposes a new L-shaped array geometry for 2-D DOA estimation based on the conjugate augmented property of signal second-order statistics. The proposed array structure is distributed augmented nested L-shaped array which every subarray is one-dimensional (1-D) distributed augmented nested array placed along y-axis and z-axis. By using Khatri-Rao product processing, the newly formed array possesses higher degree-of-freedom and number of virtual array apertures are more extended. Then, utilize spatial information and temporal information to construct a conjugate augmented spatial–temporal auto-correlation matrix for each orthogonal distributed subarray. With the redundancy removal and spatial smoothing, the rank of each covariance matrix is restored. Through analysis, each portion of the new formed virtual array can be used separately for 1-D azimuth and elevation angle estimation. Additionally, virtual cross-correlation matrix of two subarrays can accomplish angle-pairing procedure. Simulation results verify the effectivity of the proposed method in 2D-DOA estimation.

For polarimetric multi-input multi-output (MIMO) radar with spatially spread crossed-dipole, this article studies the problem of joint direction of arrival (DOA) and polarisation parameter estimation based on block-orthogonal matching pursuit (BOMP) algorithm. First, the signal model of polarimetric MIMO radar with spatially spread crossed-dipole is established, and then the covariance matrix of the received data is calculated. Using the relationship between polarisation parameter and DOA in the crossed-dipole, sparse dictionary matrix is constructed within only DOA parameter and it will be translated into a block sparse problem. Then, fast BOMP algorithm is used to estimate their support positions and their amplitudes. Last, DOA estimation is calculated by support positions and polarisation parameter is estimated by the amplitudes of the support positions. The proposed algorithm has three advantages. One is that overcomplete dictionary is constructed within only the DOA, which has a small computational complexity. Another one is that the problem of strong mutual coupling among collocated crossed-dipole is solved by using the spatially spread crossed-dipole. The last one is that the DOA and polarisation estimations can pair automatically without any additional processing. Computer simulation results demonstrate the effectiveness of the proposed algorithm.

The Direction of Arrival (DOA) parameter is a key parameter in directional channel modeling for GNSS systems and multipath suppression. However, achieving high-precision, low-complexity DOA estimation of multiple signal sources without requiring a known source number is still a challenge. This paper introduces a satellite navigation DOA parameter estimation method based on deconvolution beamforming. By exploiting the translational invariance property of the uniform linear array pattern, the deconvolution process is applied to the de-spread array pattern of satellite navigation signals, achieving high-precision estimation of DOA parameters. This method can achieve high-precision blind DOA estimation of multiple signal sources while significantly reducing the estimation complexity. Compared with traditional methods, precise DOA estimation can be achieved even in low-signal-to-noise-ratio conditions and with a small number of elements in the array. The theoretical analysis and simulation results verify the effectiveness of the proposed algorithm.

In recent years, high-resolution Direction of Arrival (DOA) estimation with a sensor array has become indispensable for various applications. In actual measurement, however, DOA estimation accuracy is deteriorated by many error factors. For a uniform linear array (ULA), there exist algorithms for self-calibration for single-dimensional (1-D) DOA estimation. In this paper, we develop a simple self-calibration method for two-dimensional (2-D) DOA estimation with an L-shaped array.

We propose a new method for direction of arrival (DOA) estimation in the presence of multipath propagation and mutual coupling for a frequency hopping (FH) system. With the use of pilot symbols and assuming perfect time-frequency synchronization for a linear array, we take mutual coupling and multipath propagation into account, and derive a maximum likelihood (ML) estimator for both the mutual coupling matrix and DOA estimation. We then formulate an iterative alternating minimization (AM) algorithm for finding the mutual coupling and DOA parameters in an alternate manner. Simulation results illustrating the performance of the algorithm and comparison with the Cramer-Rao bound (CRB) are also presented.

Aiming at the underdetermined two-dimensional(2-D) direction-of-arrival(DOA) and polarization parameter estimation of L-shaped array, this paper established a data receiving model of three-quadrature dipole compression coprime array. In order to separate DOA and polarization parameter estimation, the self-covariance matrix without polarization parameter is constructed, and the virtual array without holes is obtained by vectorization operation, which greatly improves the continuous freedom of the array. At the same time, the 2-D DOA estimation is reduced to one dimensional(1-D) space, and two 1-D DOA are obtained by sparse representation method. An angle matching method based on Khatri-Rao product is proposed under the condition of degree of freedom(DOF) extension of polarization sensitive array. On this basis, the polarization parameter of the signal are obtained by the least square method. The simulation results show that the proposed L-shaped coprime array can achieve the array DOF extension. And the joint estimation of DOA and polarization parameters has higher direction finding accuracy and angle resolution than LV-MUSIC algorithm under the condition of low SNR.

Direction of arrival (DOA) estimation of the spatial speech source is a key technique in the audition system of the service robot. This paper investigates a robust high resolution speaker DOA estimation based on acoustic vector sensor (AVS) and spatial sparsity representation (SSR) theory of source. The approximate model of the inter-sensor data ratio (ISDR) of AVS in the time-frequency (TF) domain is derived with reverberation and noise, which determines the relationship between the AVS manifold vector and the ISDR. To obtain a robust speaker DOA estimation, the paper gets reliable high local signal-to-noise ratio (HLSNR) TF points by extracting the pitch of speech signal and fitting the curve. Then the SSR model of DOA estimation is formulated and the high DOA estimation accuracy is achieved. The experimental results under different reverberation and additive noise conditions show that the proposed DOA estimation method is able to achieve RMSE of below 0.5° when the SNR is from 5dB to 30dB. Moreover, the method is independent of the source frequencies and not sensitive to reverberation. Since AVS has a small size and few sensors, this DOA estimation approach will probably provide solutions for the speaker source DOA estimation of service robots in the natural home environment.

Direction of arrival (DOA) estimation using coprime array is discussed, and a fast method using a sensor-saving coprime array with enlarged inter-element spacing is proposed. Original coprime array combines the results obtained from two coprime subarrays to uniquely determine the DOA estimation, and it is shown in this paper that one subarray actually only requires half of its original sensor number and can achieve enlarged inter-element spacing based on the array compensation from the cross correlation matrix. Thereafter, a fast DOA estimation method, which extracts the noise subspace without eigenvalue decomposition, is proposed to obtain coprime DOA estimations based on one dimensional root finding technique. Final unique DOA is estimated from the coincide results of the coprime estimations. Simulation results verify that the proposed method can achieve almost the same estimation performance as conventional methods while requiring less sensors.

We use one vector and two pressure sensors to form a sparse large aperture L-shape array for high performance two-dimensional (2D) direction of arrival (DOA) and frequency estimation. Because the number of sensors is small and there is only one vector sensor in the presented array, thus, the installation of sensors in the array is simpler and installation error is smaller, than the conventional array. Meanwhile, a high performance 2D DOA and frequency estimation method is presented. Firstly, utilizing single vector sensor and based on the ESPRIT, a group coarse 2D DOA and frequency parameters are obtained. Secondly, to restrain space noise or interference, a matrix filter is utilized to process the covariance matrix which comes from sensor array, so as to form a new covariance matrix which possesses high signal to noise ratio. Thirdly, utilizing the new covariance matrix and based on the ESPRIT again, accurate but ambiguity angles estimates are obtained. Fourthly, one signal power estimator and one optimization method are presented to solve the angle ambiguity and frequency ambiguity problems, respectively. The proposed method gains a high performance 2D DOA and frequency estimation results. Numerical simulations are performed to verify the feasibility of the proposed method.

DOA (Direction of Arrival) estimation is a major problem in array signal processing applications. Recently, compressive sensing algorithms, including convex relaxation algorithms and greedy algorithms, have been recognized as a kind of novel DOA estimation algorithm. However, the success of these algorithms is limited by the RIP (Restricted Isometry Property) condition or the mutual coherence of measurement matrix. In the DOA estimation problem, the columns of measurement matrix are steering vectors corresponding to different DOAs. Thus, it violates the mutual coherence condition. The situation gets worse when there are two sources from two adjacent DOAs. In this paper, an algorithm based on OMP (Orthogonal Matching Pursuit), called ILS-OMP (Iterative Local Searching-Orthogonal Matching Pursuit), is proposed to improve DOA resolution by Iterative Local Searching. Firstly, the conventional OMP algorithm is used to obtain initial estimated DOAs. Then, in each iteration, a local searching process for every estimated DOA is utilized to find a new DOA in a given DOA set to further decrease the residual. Additionally, the estimated DOAs are updated by substituting the initial DOA with the new one. The simulation results demonstrate the advantages of the proposed algorithm.

The paper focuses on the stochastic Cramer-Rao bound (CRB) of direction of arrival (DOA) estimates for binary phase-shift keying (BPSK) and quaternary phase-shift keying (QPSK) modulated signals corrupted by additive circular complex Gaussian noise. Explicit expressions of the CRB for the DOA parameter alone in the case of a single signal waveform are given. Finally, these results are extended to the case of two independent BPSK distributed sources where an explicit expression of the DOA parameters alone is given for large SNR.

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