Single-Snapshot DoA Estimation in Array Antennas With Mutual Coupling Through a Multiscaling BCS Strategy

2017 ◽  
Vol 65 (6) ◽  
pp. 3203-3213 ◽  
Author(s):  
Paolo Rocca ◽  
Mohammad Abdul Hannan ◽  
Marco Salucci ◽  
Andrea Massa
Keyword(s):  
Mutual Coupling ◽  
Doa Estimation ◽  
Array Antennas ◽  
Single Snapshot

scholarly journals An OPMA for Robust Mutual Coupling Coefficients Estimation of URA with Single Snapshot in MIMO HF Sky-Wave Radar

2016 ◽  
Vol 2016 ◽  
pp. 1-11
Author(s):  
Yuguan Hou ◽  
Qingguo Jin ◽  
Shaochuan Wu ◽  
Zhuoming Li
Keyword(s):  
Mutual Coupling ◽  
Signal To Noise Ratio ◽  
Mean Shift ◽  
Success Probability ◽  
Doa Estimation ◽  
Coupling Coefficients ◽  
Music Algorithm ◽  
Low Snr ◽  
Wave Radar ◽  
Single Snapshot

Due to the fluctuation of the signal-to-noise ratio (SNR) and the single snapshot case in the MIMO HF sky-wave radar system, the accuracy of the online estimation of the mutual coupling coefficients matrix of the uniform rectangle array (URA) might be degraded by the classical approach, especially in the case of low SNR. In this paper, an Online Particle Mean-Shift Approach (OPMA) is proposed, which is to get a relatively more effective estimation of the mutual coupling coefficients matrix with the low SNR. Firstly, the spatial smoothing technique combined with the MUSIC algorithm of URA is introduced for the DOA estimation of the multiple targets in the case of single snapshot which are taken as coherent sources. Then, based on the idea of the particle filter, the online particles with a moderate computational complexity are used to generate some different estimation results. Finally, the mean-shift algorithm is applied to get a more robust estimate of the equivalent mutual coupling coefficients matrix. The simulation results demonstrate the validity of the proposed approach in terms of the success probability, the statistics of bias, and the variance. The proposed approach is more robust and more accurate than the other two approaches.

scholarly journals Distributed Super Nested Arrays: Reduce the Mutual Coupling between Array Antennas

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Hongyong Wang ◽  
Weibo Deng ◽  
Ying Suo ◽  
Xin Zhang ◽  
Yanmo Hu ◽  
...  
Keyword(s):  
Mutual Coupling ◽  
Doa Estimation ◽  
Baseline Length ◽  
Spatial Smoothing ◽  
Estimation Of Parameters ◽  
Signal Parameter ◽  
Array Antennas ◽  
Esprit Algorithm ◽  
Nested Arrays ◽  
Source Signals

In array, mutual coupling between the antennas is inevitable, which has an adverse effect on the estimation of parameters. To reduce the mutual coupling between the antennas of distributed nested arrays, this paper proposes a new array called the distributed super nested arrays, which have the good characteristics of the distributed nested arrays and can reduce the mutual coupling between the antennas. Then, an improved multiscale estimating signal parameter via rotational invariance techniques (ESPRIT) algorithm is presented for the distributed super nested arrays to improve the accuracy of direction-of-arrival (DOA) estimation. Next, we analyze the limitations of the spatial smoothing algorithm used by the distributed super nested arrays when there are multiple-source signals and the influence of the baseline length of distributed super nested arrays on the accuracy of DOA estimation. The simulation results show that the distributed super nested arrays can effectively reduce the mutual coupling between the array antennas, improve the DOA estimation performance, and significantly increase the number of detectable source signals.

Orthogonal Projection DOA Estimation with a Single Snapshot

2013 ◽  
Vol E96.B (5) ◽  
pp. 1215-1217 ◽  
Author(s):  
Ann-Chen CHANG ◽  
Chih-Chang SHEN
Keyword(s):  
Orthogonal Projection ◽  
Doa Estimation ◽  
Single Snapshot

scholarly journals DoA Estimation Using Neural Tangent Kernel under Electromagnetic Mutual Coupling

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1057
Author(s):  
Qifeng Wang ◽  
Xiaolin Hu ◽  
Xiaobao Deng ◽  
Nicholas E. Buris
Keyword(s):  
Mutual Coupling ◽  
Deep Neural Network ◽  
Doa Estimation ◽  
Antenna Element ◽  
Root Finding ◽  
Multiple Signal ◽  
Estimation Problems ◽  
Limiting Case ◽  
Signal Doa Estimation ◽  
Polynomial Root Finding

Antenna element mutual coupling degrades the performance of Direction of Arrival (DoA) estimation significantly. In this paper, a novel machine learning-based method via Neural Tangent Kernel (NTK) is employed to address the DoA estimation problem under the effect of electromagnetic mutual coupling. NTK originates from Deep Neural Network (DNN) considerations, based on the limiting case of an infinite number of neurons in each layer, which ultimately leads to very efficient estimators. With the help of the Polynomial Root Finding (PRF) technique, an advanced method, NTK-PRF, is proposed. The method adapts well to multiple-signal scenarios when sources are far apart. Numerical simulations are carried out to demonstrate that this NTK-PRF approach can handle, accurately and very efficiently, multiple-signal DoA estimation problems with realistic mutual coupling.

scholarly journals Thermos Array: Two-Dimensional Sparse Array with Reduced Mutual Coupling

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Lei Sun ◽  
Minglei Yang ◽  
Baixiao Chen
Keyword(s):  
Closed Form ◽  
Degrees Of Freedom ◽  
Mutual Coupling ◽  
Doa Estimation ◽  
Two Dimensional ◽  
Spatial Smoothing ◽  
Planar Arrays ◽  
Half Wavelength ◽  
Planar Array ◽  
Small Spacing

Sparse planar arrays, such as the billboard array, the open box array, and the two-dimensional nested array, have drawn lots of interest owing to their ability of two-dimensional angle estimation. Unfortunately, these arrays often suffer from mutual-coupling problems due to the large number of sensor pairs with small spacing d (usually equal to a half wavelength), which will degrade the performance of direction of arrival (DOA) estimation. Recently, the two-dimensional half-open box array and the hourglass array are proposed to reduce the mutual coupling. But both of them still have many sensor pairs with small spacing d, which implies that the reduction of mutual coupling is still limited. In this paper, we propose a new sparse planar array which has fewer number of sensor pairs with small spacing d. It is named as the thermos array because its shape seems like a thermos. Although the resulting difference coarray (DCA) of the thermos array is not hole-free, a large filled rectangular part in the DCA can be facilitated to perform spatial-smoothing-based DOA estimation. Moreover, it enjoys closed-form expressions for the sensor locations and the number of available degrees of freedom. Simulations show that the thermos array can achieve better DOA estimation performance than the hourglass array in the presence of mutual coupling, which indicates that our thermos array is more robust to the mutual-coupling array.

Analysis and design methods of planar array antennas with slotted waveguides taking into account mutual coupling

1989 ◽  
Vol 72 (10) ◽  
pp. 103-110 ◽  
Author(s):  
Yoshihiko Konishi ◽  
Hitoshi Mizutamari ◽  
Shin-Ichi Sato ◽  
Seiji Mano ◽  
Takashi Katagi
Keyword(s):  
Mutual Coupling ◽  
Design Methods ◽  
Analysis And Design ◽  
Array Antennas ◽  
Planar Array

scholarly journals Reweighted ‐norm minimisation for high‐resolution DOA estimation under unknown mutual coupling

2018 ◽  
Vol 54 (23) ◽  
pp. 1346-1348 ◽  
Author(s):  
Dandan Meng ◽  
Xianpeng Wang ◽  
Mengxing Huang ◽  
Chong Shen
Keyword(s):  
High Resolution ◽  
Mutual Coupling ◽  
Doa Estimation
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