DOA estimation via shift-invariant matrix completion

In this paper, we consider the problem of direction of arrival (DOA) estimation on a large sensor array, in the case of missing data resulting from chain failure or employed sub-sampling schemes. Since the missing measurements impair the performance of DOA estimation, we propose to recover the missing entries from the available ones, by applying matrix completion (MC) techniques. We use three well-known MC algorithms SVT, LMaFit, and OptSpace for imputation of the missing entries and then employing MUSIC algorithm to estimate the angles of impinging signals. In addition, we improve the performance of MC techniques, by employing the information theoretic based source enumeration methods, such as MDL and AIC, instead of the heuristic and imprecise rank estimator in current methods. The mean-square error (MSE) of DOA estimation is taken as an assessment criterion for comparing the performance of the aforementioned MC algorithms. Simulation results are conducted to show that in addition to performance improvement achieved by imputation of the missing measurements, the LMaFit algorithm with the proposed rank estimator has the lowest runtime and DOA MSE comparing to the other well-known MC algorithms.

Uniform linear array (ULA) geometry does not perform well for direction of arrival (DOA) estimation at directions close to the array endfires. Shirvani and Akbari solved this problem by displacing two elements from both ends of the ULA to the top and/or bottom of the array axis. Shirvani-Akbari array (SAA) presents a considerable improvement in the DOA estimation of narrowband sources arriving at endfire directions in terms of DOA estimation accuracy and angular resolution. In this paper, all new proposed SAA configurations are modelled and also examined, numerically. In this paper, two well-known DOA estimation algorithms, multiple signal classification (MUSIC) and minimum variance distortionless response (MVDR), are used to evaluate the effectiveness of proposed arrays using total root mean square error (RMSE) criterion. In addition, two new scenarios are proposed which divide angular search to two parts, directions close to array endfires as well as middle angles. For middle angles, which belong to (−70∘≤𝜃≤70∘), ULA is considered, and for endfire angles, the angles which belong to (−90∘≤𝜃≤−70∘) and (70∘≤𝜃≤90∘), SAA is considered. Simulation results of new proposed scenarios for DOA estimation of narrowband signals show the better performance with lower computational load.

Iterative reweighted proximal projection based DOA estimation algorithm for monostatic MIMO radar

An algorithm to improve direction of arrival (DOA) estimation accuracy with an extended sensor array in the presence of multiple coherent signal sources is proposed. The algorithm uses virtual element theory to extend the sensor array, estimates virtual element information via linear prediction and expands the array aperture in practical sense; the sparsity of the target orientation in angle space is exploited to establish an over-complete dictionary and a reception model for the array signal in sparse space; the received array data is preprocessed using singular value decomposition (SVD) method and target DOA estimation is realized by calculating the best atoms. The algorithm improves DOA estimation accuracy by extended array and uses SVD to control computational complexity effectively, which ensures the accuracy and efficiency. Computer simulation shows that the proposed algorithm is able to accurately estimate the DOA for both single-target and closely spaced multi-target cases in low signal-to-noise ratio environments, and also has excellent DOA estimation performance in the presence of multiple coherent signal sources.

The advanced radar detection method which combines Tx beamforming and Rx array processing with highresolution direction of arrival (DOA) estimation is proposed. The proposed method associates DOA estimation direction with Tx beamforming direction (TxD-DOA) for a high accuracy detection for multiple targets in millimeter-wave radar system. Computer simulation and experiment evaluation verify the improvement effects of the TxD-DOA method. We have confirmed that the suppression of multiple radar reflection waves by the transmission beam scanning brings the better DOA estimation accuracy and higher angular resolution of the TxD-DOA for multiple human targets.

The problem of wideband noncircularity exploitation in the frequency domain is considered in the paper for direction of arrival (DOA) estimation of multiple signals. Two closed-form data focusing based subspace methods are proposed, designed for a uniform linear array (ULA) and a conjugate minimum redundancy linear array (CMRLA), respectively. The first method is the wideband extension of Charge's noncircular root multiple signal classification method (NC-root-MUSIC). The second method is based on virtual aperture synthesis. Both methods can handle more signals than the number of sensors. Simulation results are included to illustrate the performances of the proposed methods in terms of processing capacity and DOA estimation accuracy.

In this paper, we elaborate a reference antenna in terms of direction of arrival (DOA) estimation accuracy to verify its reliability under realistic propagation conditions. Estimation accuracy decreases for the reference antenna due to environmental influences. Our main contribution is modeling such non-anechoic environment in the simulation by considering proximity to earth with surface roughness, antenna cover against climatic influences and height of the stacked-up system over earth in a real-world deployment. We developed a ring extension for the antenna, such environmental influences are minimized and the DOA estimation accuracy is improved again. The overall localization performance of a system with several DOA estimation sensor nodes for different antenna configurations is proven in a simulation environment. The results show an improvement of the localization accuracy for the optimized antenna.

This paper proposes a method to enhance direction of arrival (DoA) estimation accuracy and resolve range ambiguity in interleaved subcarrier-based multiple-input multiple-output (MIMO) single carrier-frequency division multiple access (SC-FDMA) systems for radar applications. Using the virtual array in the MIMO configuration, a large effective aperture can be synthesized using a relatively small number of transmitting and receiving antenna elements, enabling high angular resolution. To realize this virtual array, orthogonality among multiple transmitters must be ensured. For instance, the interleaved subcarrier allocation scheme enables signals from multiple transmitting antenna elements to be orthogonal. However, this allocation introduces range-dependent phase shifts between transmitting antenna elements, which degrade DoA estimation accuracy, and reduces the maximum unambiguous detection range. To mitigate these problems, we propose a method to compensate for the phase shift in each virtual array, along with a range ambiguity resolution method based on phase linearity analysis. The proposed methods are validated through simulations. With the proposed method, the average DoA estimation error is reduced from 5.63° to 0.2°. Furthermore, the proposed method successfully resolves range ambiguity, reducing the estimation error to 0 m for a target at 1500 m, which lies beyond the system’s maximum unambiguous range of 384 m. The results confirm that the proposed approach improves DoA estimation accuracy and resolves range ambiguity in interleaved MIMO SC-FDMA systems.

Subspace-based high-resolution direction of arrival (DOA) estimation significantly deteriorates under array manifold perturbation and rank deficiency of the covariance matrix due to mutual coupling and multipath propagation, respectively. In this correspondence, the unknown mutual coupling can be circumvented by the proposed method without any passive or active calibration process, and the DOA of the coherent signals can be accurately estimated accordingly. With a newly constructed matrix, the deficient rank can be restored, and the effective array aperture can be extended compared with conventional spatial smoothing. The proposed method achieves a good robustness and DOA estimation accuracy with unknown mutual coupling. The simulation results demonstrate the validity and efficiency of the proposed method.

The problem of multiple incident signals’ direction of arrival (DOA) estimation for spatially separated polarisation sensitive array (SS‐PSA) is studied. The proposed SS‐PSA is a sparse uniform array with orthogonally oriented and spatially separated dipole‐triads. The proposed array can reduce the array mutual coupling by using the spatially separated polarised antenna instead of conventional spatially collocated polarised antenna. Moreover, the DOA estimation accuracy can be improved greatly due to the extended aperture offered by sparse array. Computer simulation results verify the effectiveness of the proposed algorithm.

In this proposed work, a subspace based method of Direction of Arrival (DOA) estimation with Mutual Coupling (MC) compensation and wavelet denoising for Uniform Linear Array (ULA) is addressed. A preprocessing technique of denoising by Wavelet Transform (WT) is accomplished and followed by Mutual Coupling compensation algorithm to improve DOA estimation. A complex symmetric Toeplitz matrix is modeled and based on this matrix, mutual coupling between sensors is compensated. By denoising and mutual coupling compensation, this method accomplishes a satisfactory performance for weak signals in DOA estimation without array calibration. The effectiveness of the proposed method is studied through simulations.

gold-MUSIC based DOA estimation using ULA antenna of DS-CDMA sources with propagation delay diversity

In this work, the grid mismatch problem for direction of arrival (DOA) estimation of array is studied. To solve the grid mismatch impact on the DOA estimation accuracy, a novel off-grid model for DOA estimation using sinc interpolation is proposed in this paper. Specifically, the off-grid error is represented as parameter to be estimated in the observation model. Under the framework of sparse Bayesian learning (SBL), the off-grid error is estimated via the variational Bayesian expectation maximization (VBEM) and eliminated by updating the grids with sinc interpolation. The simulation results demonstrate superior performance of the proposed method over state-of-the-art methods are reported, as well.

Distributed subarray antennas (DSAs), which are sparse arrays consisting of two or more far separated subarrays, have many advantages over uniform linear array, especially for direction of arrival (DOA) estimation. However, they are subject to manifold ambiguity, which has significant influence on DOA estimation. In order to solve the manifold ambiguity of uncorrelated sources for DSA, a novel method based on estimation of signal parameters via rotational invariance technique by utilizing optimal subarray partition is proposed in this paper. In the proposed method, an optimized reference estimation is obtained by the rotational invariance between the new subarrays with optimal partition of DSA. The high accuracy and unambiguous DOA estimations are then disambiguated easily according to the optimized reference estimation. In this way, the performance of disambiguated DOA estimation can be enhanced in cases of the low signal-to-noise ratio and the large spacing between the subarrays. Moreover, the ambiguity threshold effect of DOA estimation for DSA is analyzed by means of the maximum a posteriori estimator. Computer simulation results show good performance of the proposed method and the effectiveness of the ambiguity threshold for DSA.

In automotive millimeter-wave (MMW) radar systems, achieving high-precision direction of arrival (DOA) estimation with a limited number of array elements is a crucial research focus. Compressive sensing (CS) techniques have been demonstrated to offer superior performance in DOA estimation compared to spectral estimation methods. However, traditional CS methods suffer from an off-grid effect, which causes their reconstruction results to deviate from the actual positions of the signal sources, thereby reducing the accuracy. Currently, as a gridless method, atomic norm minimization (ANM) has shown effectiveness in DOA estimation for uniform linear arrays (ULAs). However, the performance of ANM is suboptimal in non-uniform linear arrays (NULAs), and their computational efficiency is not satisfactory. In this paper, we propose a novel algorithm for DOA estimation in NULA, drawing inspiration from the alternating descent conditional gradient algorithm framework. First, we construct an atomic set based on the observation scene and select the atoms with the highest correlation to the residuals as potential signal sources for global estimation. Then, we construct a mapping function for the signal sources in the continuous domain and perform conditional gradient descent in the neighborhood of each signal source, addressing the bias introduced by the off-grid effect. We compared the proposed algorithm with ANM, Iterative Shrinkage Thresholding (IST), and Multiple Signal Classification (MUSIC) algorithms. Simulation experiments validate that the proposed algorithm effectively addresses the off-grid effect and is applicable to DOA estimation in coprime and random arrays. Furthermore, real data experiments confirm the effectiveness of the proposed algorithm.