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 Performance enhancement of MPM using SNR boosting techniques

2016 ◽  
Vol 5 (4) ◽  
pp. 115
Author(s):  
Shimaa Mamdouh ◽  
Amr Hussein ◽  
Hamdy Elmekaty
Keyword(s):  
Signal Processing ◽  
Performance Enhancement ◽  
Mimo Systems ◽  
Signal To Noise Ratio ◽  
Matrix Pencil ◽  
Doa Estimation ◽  
Least Square ◽  
Test Case ◽  
Recursive Least Square ◽  
Low Snr

Signal to noise ratio (SNR) boosting is one of the most important research areas in signal processing. The effectiveness of SNR boosting is not limited to a specific application rather, it is widely used in image processing, signal processing, cognitive radio, MIMO systems, digital beam forming, and direction of arrival (DOA) estimation …etc. In this paper, the recursive least square (RLS) and wavelet based de-noising filters are exploited for SNR boosting in DOA estimation techniques for further performance enhancement. The matrix pencil method (MPM) as an effortlessness and high resolution DOA estimation technique is taken as a test case. That is because it suffers from performance deterioration under low SNR regimes. The simulation results reveal that the MPM based RLS de-noising filter outperforms the MPM based wavelet de-noising filter and the traditional MPM in terms of mean square error (MSE) especially at low SNR regimes.

scholarly journals DOA Estimation Using Fourth-Order Cumulants in Nested Arrays with Structured Imperfections

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 994
Author(s):  
Baoping Wang ◽  
Junhao Zheng
Keyword(s):  
Fourth Order ◽  
Mutual Coupling ◽  
Estimation Method ◽  
Doa Estimation ◽  
Coupling Model ◽  
Coupling Coefficients ◽  
Rank Reduction ◽  
Phase Errors ◽  
Reduction Property ◽  
Nested Arrays

Recently developed super nested array families have drawn much attention owing to their merits on keeping the benefits of the standard nested arrays while further mitigating coupling in dense subarray portions. In this communication, a new mutual coupling model for nested arrays is constructed. Analyzing the structure of the newly formed mutual coupling matrix, a transformation of the distorted steering vector to separate angular information from the mutual coupling coefficients is revealed. By this property, direction of arrival (DOA) estimates can be determined via a grid search for the minimum of a determinant function of DOA, which is induced by the rank reduction property. We also extend the robust DOA estimation method to accommodate the unknown mutual coupling and gain-phase mismatches in the nested array. Compared with the schemes of super nested array families on reducing the mutual coupling effects, the solutions presented in this paper has two advantages: (a) It is applicable to the standard nested arrays without rearranging the configuration to increase the inter-element spacing, alleviating the cross talk in dense uniform linear arrays (ULAs) as well as gain-phase errors in sparse ULA parts; (b) Perturbations in nested arrays are estimated in colored noise, which is significant but rarely discussed before. Simulations results corroborate the superiority of the proposed methods using fourth-order cumulants.

scholarly journals CC-MUSIC: An Optimization Estimator for Mutual Coupling Correction of L-Shaped Nonuniform Array with Single Snapshot

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yuguan Hou ◽  
Tongyu Zhang ◽  
Shaochuan Wu
Keyword(s):  
Global Optimization ◽  
Covariance Matrix ◽  
Mutual Coupling ◽  
Optimization Problem ◽  
Spatial Spectrum ◽  
Estimation Accuracy ◽  
Spectrum Estimation ◽  
Music Algorithm ◽  
Calibration Source ◽  
Single Snapshot

For the case of the single snapshot, the integrated SNR gain could not be obtained without the multiple snapshots, which degrades the mutual coupling correction performance under the lower SNR case. In this paper, a Convex Chain MUSIC (CC-MUSIC) algorithm is proposed for the mutual coupling correction of the L-shaped nonuniform array with single snapshot. It is an online self-calibration algorithm and does not require the prior knowledge of the correction matrix initialization and the calibration source with the known position. An optimization for the approximation between the no mutual coupling covariance matrix without the interpolated transformation and the covariance matrix with the mutual coupling and the interpolated transformation is derived. A global optimization problem is formed for the mutual coupling correction and the spatial spectrum estimation. Furthermore, the nonconvex optimization problem of this global optimization is transformed as a chain of the convex optimization, which is basically an alternating optimization routine. The simulation results demonstrate the effectiveness of the proposed method, which improve the resolution ability and the estimation accuracy of the multisources with the single snapshot.

scholarly journals An Efficient 2-D DOA Estimation for a Cylindrical Conformal Array with Unknown Mutual Coupling

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Chao Liu ◽  
Shunian Yin
Keyword(s):  
Mutual Coupling ◽  
Coupling Effect ◽  
Doa Estimation ◽  
Coupling Coefficients ◽  
Rotation Invariant ◽  
Rank Reduction ◽  
Estimation Algorithms ◽  
Conformal Array ◽  
Signal Parameters ◽  
Finding Method

The limited space of a conformal array may lead to a serious mutual coupling effect, which will significantly affect the performance of direction of arrival (DOA) estimation algorithms. In this paper, an efficient 2-D direction finding method is developed in the presence of unknown mutual coupling for the uniform cylindrical conformal array (CCA). To avoid the time-consuming two-dimensional spectral peak searching, the 2-D DOA estimation is decoupled and divided into two 1-D DOA estimations. Elevation is first estimated based on a subarray estimation of signal parameters via rotation invariant technique (ESPRIT), and then azimuth is estimated based on the rank reduction (RARE) method by using the elevation estimation result. Consequently, the mutual coupling coefficients can be estimated after getting the DOA estimates. The proposed method can well calibrate the mutual coupling effect of a cylindrical array with a low computational complexity. The final simulation results corroborate our analysis.

scholarly journals Direction of Arrival Estimation of Uncorrelated Signals Using Root-MUSIC Algorithm for ULAs and UCAs

2018 ◽  
Vol 7 (4.36) ◽  
pp. 398
Author(s):  
S. Venkata Rama Rao ◽  
A. Mallikarjuna Prasad ◽  
Ch. Santhi Rani
Keyword(s):  
Basic Problem ◽  
Signal To Noise Ratio ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Circular Array ◽  
Music Algorithm ◽  
Linear Arrays ◽  
Circular Arrays ◽  
Symmetric Structure ◽  
The Individual

In this paper, Root-MUSIC algorithm for direction of arrival (DOA) estimation of uncorrelated signals is explored both for uniform linear and uniform circular arrays. The basic problem in Uniform Linear Arrays (ULAs) is Mutual coupling between the individual elements of the antenna array. This problem is reduced in Uniform Circular Arrays (UCAs) because of its symmetric structure. The DOA estimation of uncorrelated signals that have different power levels is simulated on a MATLAB environment. And the noise consider is white across all the array elements. The factors considered for simulation are number of number of snapshots, array elements, radius of circular array, array length, and signal to noise ratio. 

Mutual Coupling Compensation in Receiving Arrays and Its Implementation on Software Defined Radios

2021 ◽  
Vol 35 (11) ◽  
pp. 1433-1434
Author(s):  
Sana Khan ◽  
Hassan Sajjad ◽  
Mehmet Ozdemir ◽  
Ercument Arvas
Keyword(s):  
Real Time ◽  
Mutual Coupling ◽  
Linear Array ◽  
Incident Angle ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Substantial Improvement ◽  
Uniform Linear Array ◽  
Low Snr ◽  
Software Defined Radios

Mutual coupling is compensated in a four element uniform linear receiving array using software defined radios. Direction of arrival (DoA) is estimated in real-time for the array with spacing d=lambda/4. The decoupling matrix was measured using a VNA for only one incident angle. After compensation the error in DoA estimation was reduced to 5%. Comparing the DoA results with d=lambda/2 spaced Uniform Linear Array (ULA), 1.2% error was observed. Although, the experiment was performed indoors with a low SNR, the results show a substantial improvement in the estimated DoA after compensation.

Angle Estimation for Bistatic MIMO Radar with Unknown Mutual Coupling Based on Improved Propagator Method

2014 ◽  
Vol 513-517 ◽  
pp. 3029-3033 ◽  
Author(s):  
Jian Feng Li ◽  
Wei Yang Chen ◽  
Xiao Fei Zhang
Keyword(s):  
Mutual Coupling ◽  
Signal To Noise Ratio ◽  
Multiple Input Multiple Output ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Rotational Invariance ◽  
Angle Estimation ◽  
Direction Of Departure ◽  
Input Multiple Output ◽  
Joint Direction

In this paper, joint direction of departure (DOD) and direction of arrival (DOA) estimation for multiple-input multiple-output (MIMO) radar with unknown mutual coupling is studied. An improved propagator calculation method is proposed to overcome the performance degradation problem when signal to noise ratio (SNR) is low. Thereafter, according to the Toeplitz structure of the mutual coupling matrix, the rotational invariance can be extracted for the angle estimation regardless of the mutual coupling from the augmented propagator matrix. The angle estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT)-like algorithm and conventional PM-like method, and angles are automatically paired. The simulation results verify the effectiveness of the algorithm.

scholarly journals Direction Finding with Gain/Phase Errors and Mutual Coupling Errors in the Presence of Auxiliary Sensors

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Weijian Si ◽  
Di Wu ◽  
Lutao Liu ◽  
Xinggen Qu
Keyword(s):  
Efficient Method ◽  
Mutual Coupling ◽  
Doa Estimation ◽  
Direction Finding ◽  
Coupling Coefficients ◽  
Decoupling Method ◽  
Estimation Algorithms ◽  
Phase Errors ◽  
Simulation Results ◽  
Array Errors

Many classical direction of arrival (DOA) estimation algorithms suffer from sensitivity to array errors. A simple but efficient method is presented for direction finding in the presence of gain and phase errors as well as mutual coupling errors. By applying a group of auxiliary sensors, DOAs and gain and phase coefficients can be simultaneously estimated, and mutual coupling coefficients can also be estimated by utilizing a novel decoupling method. The proposed algorithm does not require iterative operation or any calibration sources or spectral peak searching. Simulation results demonstrate the effectiveness of the proposed method.

scholarly journals Optimization Algorithm for DOA Estimation of a Shallow Sea Target Based on Active Time Reversal

2018 ◽  
Vol 36 (2) ◽  
pp. 270-275
Author(s):  
Haixia Jing ◽  
Haiyan Wang ◽  
Zhengguo Liu ◽  
Xiaohong Shen ◽  
Zhichen Zhang
Keyword(s):  
Time Reversal ◽  
Array Signal Processing ◽  
Signal To Noise Ratio ◽  
Doa Estimation ◽  
Shallow Sea ◽  
Estimation Model ◽  
Signal To Noise ◽  
Active Time ◽  
Low Snr ◽  
Processing Theory

Time reversal technique is applied to the DOA estimation of a shallow sea target, and a method based on active time reversal (ATR) is proposed to achieve correct estimation under multipath and low signal-to-noise (SNR) conditions. Combining the classical ray theory with array signal processing theory, the conventional multipath DOA estimation model based on uniform line array and the ATR-based DOA estimation model are set up respectively. The Capon algorithm is employed to simulate the models and compare it with conventional one. The simulation results show that the ATR-based estimation model can better estimate the azimuth angle of the target than the conventional counterpart, provide higher resolution and better suppress side lobes with the same signal-to-noise ratio (SNR), especially the low SNR.

scholarly journals Source Enumeration Approaches Using Eigenvalue Gaps and Machine Learning Based Threshold for Direction-of-Arrival Estimation

2021 ◽  
Vol 11 (4) ◽  
pp. 1942
Author(s):  
Yunseong Lee ◽  
Chanhong Park ◽  
Taeyoung Kim ◽  
Yeongyoon Choi ◽  
Kiseon Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Signal To Noise Ratio ◽  
Poor Performance ◽  
Direction Of Arrival ◽  
Estimation Accuracy ◽  
High Signal ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Low Snr

Source enumeration is an important procedure for radio direction-of-arrival finding in the multiple signal classification (MUSIC) algorithm. The most widely used source enumeration approaches are based on the eigenvalues themselves of the covariance matrix obtained from the received signal. However, they have shortcomings such as the imperfect accuracy even at a high signal-to-noise ratio (SNR), the poor performance at low SNR, and the limited detection number of sources. This paper proposestwo source enumeration approaches using the ratio of eigenvalue gaps and the threshold trained by a machine learning based clustering algorithm for gaps of normalized eigenvalues, respectively. In the first approach, a criterion formula derived with eigenvalue gaps is used to determine the number of sources, where the formula has maximum value. In the second approach, datasets of normalized eigenvalue gaps are generated for the machine learning based clustering algorithm and the optimal threshold for estimation of the number of sources are derived, which minimizes source enumeration error probability. Simulation results show that our proposed approaches are superior to the conventional approaches from both the estimation accuracy and numerical detectability extent points of view. The results demonstrate that the second proposed approach has the feasibility to improve source enumeration performance if appropriate learning datasets are sufficiently provided.

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