DFT and PSD for estimating DOA with an active acoustic array

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

Based on the extended noise subspace, the case of weak signal direction of arrival (DOA) estimation in presence of strong jamming is studied, and a novel approach is given in this paper. Under the condition knowing the number of strong interferences, first, the method forms the extended noise subspace, then, uses the conventional multiple signal classification (MUSIC) algorithm to constrain the strong interferences effectively and estimates weak signals' DOA information accurately based on the extended noise subspace. The method does not require the angle information of strong interferences and it has the same computation burden as conventional MUSIC. Theoretic analysis and computer simulation demonstrate the method is effective and feasible.

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.

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.

This paper presents a brief overview of narrowband direction of arrival (DOA) estimation algorithms and techniques. A comprehensive study is carried out in this paper to investigate and evaluate the performance of variety of algorithms for DOA estimation. Two categories of DOA estimation algorithms are considered for discussion which are Classical methods and Subspace based techniques. Classical methods include Sum-and-Delay method and Capon’s Minimum Variance Distortionless Response (MVDR) while Subspace based techniques are multiple signal classification (MUSIC) and The Minimum Norm Technique. Also ESPIRIT technique is evaluated. Inefficiencies are pointed out and solutions are suggested to overcome these shortfalls. Simulation results shows that the MUSIC algorithm is able to better represent the DOAs of signals with more prominent peaks. The Min-Norm algorithm also identifies the DOAs of signals similar to the MUSIC algorithm, but produces spurious peaks at other locations. The MVDR method identifies the DOAs of signals, but the locations are not represented by sharp peaks, due to spectral leakage. The classical beamformer also produces several spurious peaks. MUSIC show higher accuracy and resolution than the other algorithms. It should be noted that MUSIC is more applicable because it can be used for different array geometries.

A single vector hydrophone can complete the estimation of the direction of arrival (DOA) of underwater target. However, the single vector hydrophone can only estimate limited number sources and its performance is degraded when the signal is non-stationary, such as LFM signal. Besides, the DOA estimation performance of wideband LFM can be further improved as the time-frequency characteristics of the wideband LFM signals are not fully utilized in the existing methods. In this paper, a DOA estimation of wideband LFM signal using single vector hydrophone based on LVD-MUSIC method is proposed. Firstly, the Lv Distribution (LVD) algorithm is used to estimate the signal parameters, which has the advantage of high time-frequency concentration and the ability of anti-noise. Then, considering that the single vector hydrophone has the characteristics of the array manifold, the subspace decomposition theory in array signal processing is applied to the array manifold. At the same time, a covariance matrix is constructed based on the LVD, and is introduced into the Multiple Signal Classification (MUSIC) algorithm to estimate DOA. Finally, the simulation results prove the effectiveness of the proposed method, and verify the improvement of the DOA estimation accuracy.

We address the problem of direction of arrival (DOA) estimation in an array-fed parabolic reflector antenna. We employ the estimated DOA and an adaptive nulling scheme for pattern nulling of a jamming signal. The multiple signal classification (MUSIC) algorithm is used to estimate the DOA. Adaptive nulling is obtained using the linearly constrained minimum variance (LCMV) beam former. The performance of the method is illustrated by specific examples.

In this paper we propose for comparative analysis of wideband direction of arrival (DOA) estimation methods with nested array. Previously developed methods, Incoherent method, Coherent signal subspace processing (CSSM) and Weighted average of signal subspace (WAVES) could not perform satisfactory DOA estimation due to the single dimensionality and associated with lesser degree of freedom. In nested array, DOA estimation is done on the same source that is Gaussian source with additive white Gaussian noise (AWGN). While, nested array performed DOA estimation by decomposing uniform linear array (ULA) into multidimensional array, by which algorithm gets improved performance compare to the conventional methods. In nested array, power spectrum is measured by using multiple signal classification (MUSIC) and performance index is evaluated using Monte Carlo simulation.

In this paper, the issue of direction of arrival (DOA) estimation is discussed, and a partial angular sparse representation (SR)-based method using a sparse separate nested acoustic vector sensor (SSN-AVS) array is developed. Traditional AVS array is improved by separating the pressure sensor array and velocity sensor array into two different sparse array geometries with nested relationship. This improved array geometry can achieve large degrees of freedom (DOF) after the extended vectorization of the cross-covariance matrix, and only partial SR of the angle is required by exploiting the cyclic phase ambiguity caused by the large inter-element spacing of the virtual array. Joint sparse recovery is developed to amend the grid offset and unitary transformation is utilized to transform the complex atoms into real-valued ones. After sparse recovery, the sparse vector can simultaneously provide high-resolution but ambiguous angle estimation and unambiguous reference angle estimation embedded in the AVS array, and they are combined to obtain unique and high-resolution DOA estimation. Compared to other state-of-the-art DOA estimation methods using the AVS array, the proposed algorithm can provide better DOA estimation performance while requiring lower complexity. Multiple simulation results verify the effectiveness of the approach.

In signal processing applications smart antenna arrays are used to locate sources of various signals and for beamforming. Various algorithms have been developed to determine or estimate the Direction Of Arrival (DOA). Direction Of Arrival estimation is utilized in a number of applications, including radar, navigation, communication, etc. Beamforming is a technique used for directive signal transmission and reception. In a smart antenna system antenna arrays adjusts the main lobe towards the desire user and generates null in the direction of interfering users to provide suppression. Objective of this paper is to analyze the performance of Multiple Signal Classification (MUSIC) algorithm a subspace method to estimate DOA and compare with classical Bartlett method of DOA. Beamforming algorithms like LMS and NVSSLMS are studied,analyzed and its performance parameters compared.

In this paper, we propose a new method to estimate more directions of arrival (DOA) while having fewer antennas in the array. Indeed, we decompose the original array into two subarrays. Next, we make a Hadamard product between both steering vectors of subarrays to get the Hadamard steering vector. The lasts elements of the Hadamard steering vector are vertically concatenate with original steering vector. After that we apply MUltiple SIgnal Classification (MUSIC) algorithm for estimation of DOA of source signals. We used MatLab software to make the simulations and we get very good results.

Currently, the widely used methods for direction of arrival (DOA) estimation were constructed based on the subspace, such as Multiple Signal Classification (MUSIC) and Estimating Signal Parameter via Rotational Invariance Techniques (ESPRIT), which needed to know the number of sources in advance. In this paper, a new model based on the Generalized Reference Curve Model (GRCM) for the DOA estimation was proposed, which do not need to know the sources number in advance. And the comparison of the performance between the proposed model and the MUSIC model was given to demonstrate the effectiveness of our method. The algorithm of Multi-target Intermittent Particle Swarm Optimization (MIPSO) was adopted to solve the model proposed in this paper, and the performance of the MIPSO was analyzed through a simulation. The result shown that:(1) the GRCM was an effective model to solve the DOA estimation without prior knowledge of the sources number; (2) the MIPSO was an efficient algorithm to solve the DOA estimation with much shorter operation time and high precision.

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.

This paper proposes a low complexity direction of arrival (DOA) estimation method for two-dimensional (2D) massive multiple-input multiple-output (MIMO) systems. The conventional Multiple SIgnal Classification (MUSIC) algorithm of an example of 2D DOA estimation has a great DOA estimation performance, although there is a problem that the calculation amount becomes enormous due to 2D spectral search, especially in massive MIMO systems. By using a part of row or column antenna elements, it is possible to separate search domains azimuth and elevation, respectively. Observation dimension can be degenerated to 1D and subset of row/column-wise antenna elements are sufficient for DOA estimation. The proposed approach can reduce computation complexity while achieving an improved DOA estimation accuracy. Simulation results validate its effectiveness and superiority. The obtained findings will be useful for designing practical massive MIMO systems in future mobile communications.

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.