2D Direction-of-arrival Estimation for Sparse L-shaped Array based on Recursive Gridding

Zhang, Huaiji; Gao, Kangkang; Xing, Jiangnan; Jiang, Tao; Xu, Ruidong

doi:10.1109/apcap50217.2020.9246111

Cited by 2 publications

(7 citation statements)

References 6 publications

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“…To address the problem of the blurring generated by sparse arrays, the use of coprime arrays can avoid the generation of blurred angles, so coprime arrays are widely used in practice. The array element arrangements of the X and Y axes are changed to the mutual prime number (4, 5) and (3,7), respectively; and the array element arrangement of the X and Y axis are 𝑋: (0,4,5,8,10,12,15,16)𝜆/2, ( Figure 2 shows that the 2D MUSIC algorithm can effectively estimate elevation and azimuth with a high degree of accuracy. In practice, sparse arrays are often used instead of uniform line arrays to reduce the effect of the mutual coupling between array elements on the accuracy of DOA estimation and to increase the number of measurable sources [17].…”

Section: L-shaped Array Signal Modelmentioning

confidence: 99%

“…To address the problem of the blurring generated by sparse arrays, the use of coprime arrays can avoid the generation of blurred angles, so coprime arrays are widely used in practice. The array element arrangements of the X and Y axes are changed to the mutual prime number (4, 5) and (3,7), respectively; and the array element arrangement of the X and Y axis are 𝑋: (0,4,5,8,10,12,15,16)𝜆/2, ( Figure 3a shows that the spatial spectrum contains five distinct spectral peaks, the corresponding coordinates of which coincide with the center of the circle in Figure 3b, which are sharper than the spectral peaks in Figure 2. However, there are two blurred angles in it.…”

Section: L-shaped Array Signal Modelmentioning

confidence: 99%

Section: L-shaped Array Signal Modelmentioning

confidence: 99%

“…The dilation rate in Figure 9b is (2, 2), i.e., the difference in the position between adjacent values in the convolution kernel is 2, so when the dilation rate is (2, 2), the size of the receptive field is the same as when the convolution kernel is 5 × 5. In Figure 9c, the dilation rate is (3,3), the difference in the position of the values is 3, and the size of the receptive field is 7 × 7. The dilated convolution achieves an increase in the receptive field with the same convolution kernel and avoids an increase in computational effort.…”

Section: Dilated Convolutionmentioning

confidence: 99%

“…Two-dimensional DOA is of greater importance for spatial localization and is, therefore, one of the main focuses of current research in the field. Two-dimensional DOA estimation requires arrays to be arranged in a 2D plane, generally using L-shaped arrays, surface arrays, parallel arrays, or vector sensors [3,4]. Most 2D DOA estimation algorithms extend the 1D DOA estimation algorithm to a 2D spatial spectrum, such as the 2D multiple-signal classification (MUSIC) [5] algorithm and 2D estimating signal parameter via rotational invariance techniques (ESPRIT) algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of the Two-Dimensional Direction of Arrival for Low-Elevation and Non-Low-Elevation Targets Based on Dilated Convolutional Networks

Zhao

Liu

2023

Remote Sensing

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This paper addresses the problem of the two-dimensional direction-of-arrival (2D DOA) estimation of low-elevation or non-low-elevation targets using L-shaped uniform and sparse arrays by analyzing the signal models’ features and their mapping to 2D DOA. This paper proposes a 2D DOA estimation algorithm based on the dilated convolutional network model, which consists of two components: a dilated convolutional autoencoder and a dilated convolutional neural network. If there are targets at low elevation, the dilated convolutional autoencoder suppresses the multipath signal and outputs a new signal covariance matrix as the input of the dilated convolutional neural network to directly perform 2D DOA estimation in the absence of a low-elevation target. The algorithm employs 3D convolution to fully retain and extract features. The simulation experiments and the analysis of their results revealed that for both L-shaped uniform and L-shaped sparse arrays, the dilated convolutional autoencoder could effectively suppress the multipath signals without affecting the direct wave and non-low-elevation targets, whereas the dilated convolutional neural network could effectively achieve 2D DOA estimation with a matching rate and an effective ratio of pitch and azimuth angles close to 100% without the need for additional parameter matching. Under the condition of a low signal-to-noise ratio, the estimation accuracy of the proposed algorithm was significantly higher than that of the traditional DOA estimation.

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Section: L-shaped Array Signal Modelmentioning

confidence: 99%

“…To address the problem of the blurring generated by sparse arrays, the use of coprime arrays can avoid the generation of blurred angles, so coprime arrays are widely used in practice. The array element arrangements of the X and Y axes are changed to the mutual prime number (4, 5) and (3,7), respectively; and the array element arrangement of the X and Y axis are 𝑋: (0,4,5,8,10,12,15,16)𝜆/2, ( Figure 3a shows that the spatial spectrum contains five distinct spectral peaks, the corresponding coordinates of which coincide with the center of the circle in Figure 3b, which are sharper than the spectral peaks in Figure 2. However, there are two blurred angles in it.…”

Section: L-shaped Array Signal Modelmentioning

confidence: 99%

Section: L-shaped Array Signal Modelmentioning

confidence: 99%

Section: Dilated Convolutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Estimation of the Two-Dimensional Direction of Arrival for Low-Elevation and Non-Low-Elevation Targets Based on Dilated Convolutional Networks

Zhao

Liu

2023

Remote Sensing

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An L-Shaped Three-Level and Single Common Element Sparse Sensor Array for 2-D DOA Estimation

Cui

et al. 2023

Sensors

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The degree of freedom (DOF) is an important performance metric for evaluating the design of a sparse array structure. Designing novel sparse arrays with higher degrees of freedom, while ensuring that the array structure can be mathematically represented, is a crucial research direction in the field of direction of arrival (DOA) estimation. In this paper, we propose a novel L-shaped sparse sensor array by adjusting the physical placement of the sensors in the sparse array. The proposed L-shaped sparse array consists of two sets of three-level and single-element sparse arrays (TSESAs), which estimate the azimuth and elevation angles, respectively, through one-dimensional (1-D) spatial spectrum search. Each TSESA is composed of a uniform linear subarray and two sparse subarrays, with one single common element in the two sparse subarrays. Compared to existing L-shaped sparse arrays, the proposed array achieves higher degrees of freedom, up to 4Q1Q2+8Q1−5, when estimating DOA using the received signal covariance. To facilitate the correct matching of azimuth and elevation angles, the cross-covariance between the two TSESA arrays is utilized for estimation. By comparing and analyzing performance parameters with commonly used L-shaped and other sparse arrays, it is found that the proposed L-shaped TSESA has higher degrees of freedom and array aperture, leading to improved two-dimensional (2-D) DOA estimation results. Finally, simulation experiments validate the excellent performance of the L-shaped TSESA in 2-D DOA estimation.

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2D Direction-of-arrival Estimation for Sparse L-shaped Array based on Recursive Gridding

Cited by 2 publications

References 6 publications

Estimation of the Two-Dimensional Direction of Arrival for Low-Elevation and Non-Low-Elevation Targets Based on Dilated Convolutional Networks

Estimation of the Two-Dimensional Direction of Arrival for Low-Elevation and Non-Low-Elevation Targets Based on Dilated Convolutional Networks

An L-Shaped Three-Level and Single Common Element Sparse Sensor Array for 2-D DOA Estimation

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