Resolution Direction Of Arrival Estimation Research Articles

Deep learning-based DOA estimation for hybrid massive MIMO receive array with overlapped subarrays

As massive MIMO is a key technology in the future sixth generation (6G), the large-scale antenna arrays are widely considered in direction-of-arrival (DOA) estimation for they can provide larger aperture and higher estimation resolution. However, the conventional fully digital architecture requires one radio-frequency (RF) chain per antenna, and this is challenging for the high hardware costs and much more power consumption caused by the large number of RF chains. Therefore, an overlapped subarray (OSA) architecture-based hybrid massive MIMO array is proposed to reduce the hardware costs, and it can also have better DOA estimation accuracy compared to non-overlapped subarray (NOSA) architecture. The simulation results also show that the accuracy of the proposed OSA architecture has 6^{circ } advantage over the NOSA architecture with signal-to-noise ratio (SNR) at 10 dB. In addition, to improve the DOA estimation resolution, a deep learning (DL)-based estimator is proposed by combining convolution denoise autoencoder (CDAE) and deep neural network (DNN), where CDAE can remove the approximation error of sample covariance matrix (SCM) and DNN is used to perform high-resolution DOA estimation. From the simulation results, CDAE-DNN can achieve the accuracy lower bound at textrm{SNR}=-8 dB and the number of snapshots N=100, this means it has better performance in poor communication situation and can save more software resources compared to conventional estimators.

Deconvolved Fractional Fourier Domain Beamforming for Linear Frequency Modulation Signals

To estimate the direction of arrival (DOA) of a linear frequency modulation (LFM) signal in a low signal-to-noise ratio (SNR) hydroacoustic environment by a small aperture array, a novel deconvolved beamforming method based on fractional Fourier domain delay-and-sum beamforming (FrFB) was proposed. Fractional Fourier transform (FrFT) was used to convert the received signal into the fractional Fourier domain, and delay-and-sum beamforming was subsequently performed. Noise resistance was acquired by focusing the energy of the LFM signal distributed in the time-frequency domain. Then, according to the convolution structure of the FrFB complex output, the influence of the fractional Fourier domain complex beam pattern was removed by deconvolution, and the target spatial distribution was restored. Therefore, an improved spatial resolution of DOA estimation was obtained without increasing the array aperture. The simulation and experimental results show that, with a small aperture array at low SNR, the proposed method possesses higher spatial resolution than FrFB and frequency-domain deconvolved conventional beamforming.

Novel Design of Grey Wolf Optimization Heuristics for High Resolution Direction of Arrival Estimation in Acoustic Plane Waves

Novel Design of Grey Wolf Optimization Heuristics for High Resolution Direction of Arrival Estimation in Acoustic Plane Waves

A Fast Multi-Source Sound DOA Estimator Considering Colored Noise in Circular Array

Sound direction of arrival (DOA) estimation keeps finding new applications. For instance, real-time sound DOA estimation enabled by acoustic sensors on low-cost and limited-resource devices can enhance or create many consumer use cases. Indeed, tracking multiple moving sound sources in noisy or reverberated condition with a cost-effective platform is highly desirable. In this paper, we develop a computation-aware DOA estimator that uses weighting in frequency domain. It relies on single source zones (SSZs) and circular integrated cross spectrum (CICS) techniques and peak search on DOA histogram. The existing histogram-based multi-source DOA estimation is fundamentally limited in angular resolution, and the CICS process is very time consuming. To tackle these problems, we propose a weighting method that exploits the unevenness of intermediate DOA estimation errors over the frequencies of interest, and a simple yet effective strategy to reduce search range. Two underlying mechanisms, the impact of the colored noise of microphones and the frequency-dependent behavior (statistic model) of DOA estimation errors, are unveiled. We have developed an experimental platform using commercially off-the-shelf devices to verify design objectives, including feasibility and real-time requirement. The proposed techniques are validated on the developed platform and the system performance is evaluated experimentally in a real-world environment. It is observed the DOA estimation resolution is improved roughly from 30° to 15° and the overall computation is reduced by 68%.

DOA Estimation Method in Multipath Environment for Passive Bistatic Radar

Direction-of-arrival (DOA) estimation in multipath environment is an important issue for passive bistatic radar (PBR) using frequency agile phased array VHF radar as illuminator of opportunity. Under such scenario, the main focus of this paper is to cope with the closely spaced uncorrelated and coherent signals in low signal-to-noise ratio and limited snapshots. Making full use of the characteristics of moduli of eigenvalues, the DOAs of the uncorrelated signals are firstly estimated. Afterwards, their contributions are eliminated by means of spatial difference technique. Finally, in order to improve resolution and accuracy DOA estimation of remaining coherent signals while avoiding the cross-terms effect, a new beamforming solution based iterative adaptive approach (IAA) is proposed to deal with a reconstructed covariance matrix. The proposed method combines the advantages of both spatial difference method and the IAA algorithm while avoiding their shortcomings. Simulation results validate its effectiveness; meanwhile, the good performances of the proposed method in terms of resolution probability, detection probability, and estimation accuracy are demonstrated by comparison with the existing methods.

A Novel DFT-Based DOA Estimation by a Virtual Array Extension Using Simple Multiplications for FMCW Radar.

We propose a novel discrete Fourier transform (DFT)-based direction of arrival (DOA) estimation by a virtual array extension using simple multiplications for frequency modulated continuous wave (FMCW) radar. DFT-based DOA estimation is usually employed in radar systems because it provides the advantage of low complexity for real-time signal processing. In order to enhance the resolution of DOA estimation or to decrease the missing detection probability, it is essential to have a considerable number of channel signals. However, due to constraints of space and cost, it is not easy to increase the number of channel signals. In order to address this issue, we increase the number of effective channel signals by generating virtual channel signals using simple multiplications of the given channel signals. The increase in channel signals allows the proposed scheme to detect DOA more accurately than the conventional scheme while using the same number of channel signals. Simulation results show that the proposed scheme achieves improved DOA estimation compared to the conventional DFT-based method. Furthermore, the effectiveness of the proposed scheme in a practical environment is verified through the experiment.

High Resolution Direction of Arrival Estimation Based on Covariance Fitting Estimation of Signal Parameters by Rotational Invariance Technique Algorithm

High Resolution Direction of Arrival Estimation Based on Covariance Fitting Estimation of Signal Parameters by Rotational Invariance Technique Algorithm

Mℓ 1,2 ‐MUSIC algorithm for DOA estimation of coherent sources

Recasting the direction of arrival (DOA) estimation problem into sparse model is a subject of many researches, which has been carried out by different methods. Using the compressive sensing (CS) multiple measurement vector recovery algorithms have improved the resolution of DOA estimation in comparison with conventional methods such as multiple signal classification (MUSIC). In addition, a recently introduced hybrid method using CS and array processing named as l 1,2 -MUSIC exhibits high resolution as well as capability of resolving coherent sources. However, with recent advances in technology and employing higher order polynomial sources due to non-linear frequency characteristic, the performance of conventional methods is poor when the size of the array is small or the noise level is high. A novel hybrid method is presented based on matching pursuit (MP) signal decomposition and the l 1,2 -MUSIC algorithm. The new approach named Ml 1,2 -MUSIC takes MP decomposition coefficients of the signal into account and applies the l 1,2 -MUSIC to achieve DOA estimation. Along with improving the resolvability of closely located non-stationary sources (high ordered polynomial phase) with small-sized sensor array, the main advantage of the proposed method is robustness to source coherency.

Precision of direction of arrival (DOA) estimation using novel three dimensional array geometries

Numerous methods for direction of arrival (DOA) estimation, used in smart antennas have been already reported in previous studies. The precision of DOA estimation depends on the choice of the algorithm and the geometrical configuration of the antenna array. In this work, the performance of new geometrical configurations, i.e. 2D with equal area and 3D with equal volume including circular, square, triangular, hexagonal and star geometries, with equal number of antenna elements, are examined and compared to each other to find the most proper geometry. Monte-Carlo simulations are performed to evaluate the DOA precision of the proposed arrays using the MUSIC algorithm. It is shown that in three cases of comparison including 2D geometries, 3D geometries and 3D rotated geometries, with the star and triangular configurations one achieves better resolution in DOA estimation. It is also revealed that the rotated configurations show lower estimation error compared to normal configurations.

Block-sparse beamforming for spatially extended sources in a Bayesian formulation.

Direction-of-arrival (DOA) estimation refers to the localization of sound sources on an angular grid from noisy measurements of the associated wavefield with an array of sensors. For accurate localization, the number of angular look-directions is much larger than the number of sensors, hence, the problem is underdetermined and requires regularization. Traditional methods use an ℓ2-norm regularizer, which promotes minimum-power (smooth) solutions, while regularizing with ℓ1-norm promotes sparsity. Sparse signal reconstruction improves the resolution in DOA estimation in the presence of a few point sources, but cannot capture spatially extended sources. The DOA estimation problem is formulated in a Bayesian framework where regularization is imposed through prior information on the source spatial distribution which is then reconstructed as the maximum a posteriori estimate. A composite prior is introduced, which simultaneously promotes a piecewise constant profile and sparsity in the solution. Simulations and experimental measurements show that this choice of regularization provides high-resolution DOA estimation in a general framework, i.e., in the presence of spatially extended sources.

English

Source Direction of arrival (DOA) estimation plays an important role in array signal processing, and has a wide range of application. Array signal processing is an important branch in the field of signal processing. In recent years, it has developed dramatically. It can be applied in fields such as radar, communication, sonar, earthquake, exploration, astronomy and biomedicine. Over the past few years, all kinds of algorithms which can be used in DOA estimation have made great achievements, the most classic algorithms among which are Multiple Signal Classification (MUSIC) and Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT).In this paper, we will give an overview on performance of the DOA estimation based on MUSIC as well as ESPRIT (both TLS and Pro ESPRIT) algorithm on uniform linear array (ULA) and in the presence of white noise. We will describe what DOA estimation is, and give a mathematical model of DOA estimation for subspace based DOA methods such as MUSIC, TLS ESPRIT and Pro ESPRIT. Then estimate DOA based on the MUSIC, TLS ESPRIT as well as Pro ESPRIT algorithm, with simulations with MATLAB to simulate factors that affect accuracy and resolution of DOA estimation. The performance of these DOA algorithms for a set of input parameters such as number of snapshots, number of array elements, signal-to-noise ratio, angular separation, element spacing are investigated.

AN IMPROVED L1-SVD ALGORITHM BASED ON NOISE SUBSPACE FOR DOA ESTIMATION

In this paper, an improved L1-SVD algorithm based on noise subspace is presented for direction of arrival (DOA) estimation using reweighted L1 norm constraint minimization. In the proposed method, the weighted vector is obtained by utilizing the orthogonality between noise subspace and signal subspace spanned by the array manifold matrix. The presented algorithm banishes the nonzero entries whose indices are inside of the row support of the jointly sparse signals by smaller weights and the other entries whose indices are more likely to be outside of the row support of the jointly sparse signals by larger weights. Therefore, the sparsity at the real signal locations can be enhanced by using the presented method. The proposed approach ofiers a good deal of merits over other DOA techniques. It not only increases robustness to noise, but also enhances resolution in DOA estimation. Furthermore, it is not very sensitive to the incorrect determination of the number of signals and can primely suppress spurious peak in DOA estimation. Simulation results are shown that the presented algorithm has better performance than the existing algorithms, such as MUSIC, L1-SVD algorithm.