Three Decades of Development in DOA Estimation Technology

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.

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.

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

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.

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.

Third-Generation based Wide Band Code Division Multiple Access (W-CDMA) networks have been characterized for high data rate services accessible to a large number of users. But multipath and co-channel interference puts a limit on system capacity, throughput and new multimedia information services like wireless internet or video streaming. Smart antenna technique that includes Direction of Arrival (DOA) approach and Adaptive Beam forming algorithm is a promising means to improve the over-all system capacity and to overcome limited spectrum problem by effectively reducing the multipath and co-channel interference. In this paper, DOA estimation is done by Multiple Signal Classification (MUSIC) algorithm and Minimum Variance Distortion less Response (MVDR) algorithm is used for adaptive beam forming that continuously adjusts the weight of antenna arrays for creating a beam to track the desired user automatically, and to place null towards the interference user. Here, we improve on the MVDR method in order to assign the weightings. Instead of using a single linear equality constraint, a variable constraint has been used to obtain the optimum weights that decrease the gain in the undesired direction and desired direction comes out with more gain and without distortion. Obtained results are analyzed for the conventional method and the proposed method by observing beam forming spectrum by first varying number of antenna elements and then by varying the spacing between elements. For both the cases, the performance is better for the proposed method in terms of lower noise floor, more relative power and sharper peaks in the desired user direction than conventional method.

This paper introduces a comparative study between two algorithms for Direction-of-Arrival (DOA) estimation which are MVDR (Minimum Variance Distortionless Response) and MUSIC (Multiple Signal classification). The used algorithms are used for Direction-of-Arrival (DOA) estimation in smart antenna. The paper presents a comparative study for both algorithms in terms of the total mobile users number, effect of signal parameters, spacing between adjacent elements, and number of snapshots. It is proven that MUSIC has better performance than MVDR algorithm. MUSIC algorithm is more effective and introduces more enhancing performance.

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 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.

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.

The performance of all digital Radio Direction Finding systems in which the RF signal is digitalized with highpass sampling is seriously impaired by the Jitter effect. In this paper, the novel approach to mitigate the Jitter effect in Direction Of Arrival (DOA) estimation is proposed. The DOA of incoming signals received by an M-element Uniform Linear Antenna array is estimated by using MUSIC (Multiple Signal Classification). The simulation results for DOA estimation of RF signals with the proposed method will be shown and analyzed to verify its performance.

We note that the cost function of the MUSIC (multiple signal classification) algorithm is quadratic in an array vector and that it can be expressed in a least squares form. Based on this observation, we present a rigorous Levenberg-Marquardt (LM) formulation of the MUSIC algorithm for simultaneous estimation of an azimuth and an elevation. We show a convergence property and compare the performance of the LM-based MUSIC algorithm with that of the standard MUSIC algorithm via Monte-Carlo simulation. We also compare the performance of the MUSIC algorithm with that of the Capon algorithm both for the standard implementation and for the LM-based implementation.

The acoustic vector sensor (AVS) array is a powerful tool for direction of arrival (DOA) estimation of underwater targets. However, traditional DOA estimation algorithms generally suffer from low signal-to-noise ratio (SNR) as well as snapshot deficiency. A novel signal self-nulling based DOA estimation method is proposed. Firstly, achieve the crude estimation of the desired DOA using the traditional algorithms. Secondly, set the observation angular interval around the crudely estimated DOA. Thirdly, make the observation direction vary in the observation angular interval, and for each imaginary DOA, calculate the amplitude response of the minimum variance distortionless response (MVDR) beamformer. Finally, the pseudo spatial spectrum is achieved. Computer simulations verify that the proposed method is efficient in DOA estimation, especially in low SNR and insufficient snapshot data scenarios.

A multiple signal classification (MUSIC)-based approach known as the time-space-time MUSIC (TST-MUSIC) is proposed to jointly estimate the directions of arrival (DOAs) and the propagation delays of a wireless multiray channel. The MUSIC algorithm for the DOA estimation is referred to as the spatial-MUSIC (S-MUSIC) algorithm. On the other hand, the temporal-MUSIC (T-MUSIC), which estimates the propagation delays, is introduced as well. Making use of the space-time characteristics of the multiray channel, the proposed algorithm-in a tree structure-combines the techniques of temporal filtering and of spatial beamforming with three one-dimensional (1-D) MUSIC algorithms, i.e., one S-MUSIC and two T-MUSIC algorithms. The incoming rays are thus grouped, isolated, and estimated. At the same time, the pairing of the estimated DOAs and delays is automatically determined. Furthermore, the proposed approach can resolve the incoming rays with very close DOAs or delays, and the number of antennas required by the TST-MUSIC algorithm can be made less than that of the incoming rays.