DOA Estimation with a Combination of Iterative MUSIC and SBL in Low SNR Conditions

2021 ◽  
Author(s):  
Mengfan Zhou ◽  
Hui Tian ◽  
Shaoshuai Fan
Keyword(s):  
Doa Estimation ◽  
Low Snr

scholarly journals DOA Estimation in Non-Uniform Noise Based on Subspace Maximum Likelihood Using MPSO

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1429
Author(s):  
Jui-Chung Hung
Keyword(s):  
Local Search ◽  
Search Algorithm ◽  
Doa Estimation ◽  
Iterated Local Search ◽  
Local Search Algorithm ◽  
Noise Variance ◽  
Swarm Optimization ◽  
Noise Effects ◽  
Low Snr ◽  
Uniform Noise

In general, the performance of a direction of arrival (DOA) estimator may decay under a non-uniform noise and low signal-to-noise ratio (SNR) environment. In this paper, a memetic particle swarm optimization (MPSO) algorithm combined with a noise variance estimator is proposed, in order to address this issue. The MPSO incorporates re-estimation of the noise variance and iterated local search algorithms into the particle swarm optimization (PSO) algorithm, resulting in higher efficiency and a reduction in non-uniform noise effects under a low SNR. The MPSO procedure is as follows: PSO is initially utilized to evaluate the signal DOA using a subspace maximum-likelihood (SML) method. Next, the best position of the swarm to estimate the noise variance is determined and the iterated local search algorithm to reduce the non-uniform noise effect is built. The proposed method uses the SML criterion to rebuild the noise variance for the iterated local search algorithm, in order to reduce non-uniform noise effects. Simulation experiments confirm that the DOA estimation methods are valid in a high SNR environment, but in a low SNR and non-uniform noise environment, the performance becomes poor because of the confusion between noise and signal sources. The proposed method incorporates the re-estimation of noise variance and an iterated local search algorithm in the PSO. This method is effectively improved by the ability to reduce estimation deviation in low SNR and non-uniform environments.

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.

High-Resolution DOA Estimation Algorithm for a Single Acoustic Vector Sensor at Low SNR

2020 ◽  
Vol 68 ◽  
pp. 6142-6158
Author(s):  
Jun Zhang ◽  
Xiangyuan Xu ◽  
Zhifei Chen ◽  
Ming Bao ◽  
Xiao-Ping Zhang ◽  
...  
Keyword(s):  
High Resolution ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Acoustic Vector Sensor ◽  
Low Snr ◽  
Vector Sensor

scholarly journals DOA estimation method of spatial spectrum product for distributed coprime linear array

2021 ◽  
Vol 39 (5) ◽  
pp. 1130-1138
Author(s):  
Na WANG ◽  
Xuanzhi ZHAO ◽  
Zengli LIU ◽  
Jingjing ZHANG
Keyword(s):  
Estimation Method ◽  
Doa Estimation ◽  
Spatial Spectrum ◽  
Estimation Accuracy ◽  
Low Snr ◽  
Distributed Array ◽  
Coprime Arrays ◽  
Coprime Array ◽  
Mathematical Derivation ◽  
Better Than

Coprime array isAsparse array composed of two uniform linear arrays with different spacing. When the two subarrays are inAnon-coherent distributed configuration, the direction of arrival (DOA) method based on the covariance analysis of the complete coprime array is no longer effective. According to the essential attribute that the distance between the elements of two subarrays can eliminate the angle ambiguity, based on the mathematical derivation, Aspatial spectral product DOA estimation method for incoherent distributed coprime arrays is proposed. Firstly, the spatial spectrum of each subarray is calculated by using the snapshot data of each subarray, and then the DOA estimation is realized by multiplying the spatial spectrum of each subarray. The simulation results show that the estimation accuracy and angle resolution of the present method are better than those of the traditional ambiguity resolution methods, and the estimation performance is good in the mutual coupling and low SNR environment, with the good adaptability and stability. Moreover, by using the flexibility of distributed array, the matching error is effectively solved through the rotation angle.

scholarly journals A Novel ULA-Difference-Coarray-Based DOA Estimation Method for General Coherent Signals

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zhang Chen ◽  
Hao Wu ◽  
Yongxiang Liu
Keyword(s):  
Good Accuracy ◽  
Linear Array ◽  
Estimation Method ◽  
Doa Estimation ◽  
Estimation Accuracy ◽  
Coherent Signals ◽  
Low Snr ◽  
Sample Covariance ◽  
Lower Complexity ◽  
The Difference

In this article, a difference-coarray-based direction of arrival (DOA) method is introduced, which utilizes the uniform linear array (ULA) in a novel fashion to address the problem of DOA estimation for coherent signals. Inspired by the coarray-based estimators employed in cases of sparse arrays, we convert the sample covariance matrix of the observed signals into the difference coarray domain and process the signals using a spatial smoothing technique. The proposed method exhibits good accuracy and robustness in both the uncorrelated and coherent cases. Numerical simulations verify that the ULA difference coarray- (UDC-) based method can achieve good DOA estimation accuracy even when the SNR is very low. In addition, the UDC-based method is insensitive to the number of snapshots. Under extremely challenging conditions, the proposed UDC-ES-DOA method is preferred because of its outstanding robustness, while the UDC-MUSIC method is suitable for most moderate cases of lower complexity. Due to its demonstrated advantages, the proposed method is a promising and competitive solution for practical DOA estimation, especially for low-SNR or snapshot-limited applications.

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.

An enhanced spatial smoothing algorithm for coherent signals DOA estimation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bingbing Qi ◽  
Dunge Liu
Keyword(s):  
Noise Suppression ◽  
Doa Estimation ◽  
Spatial Smoothing ◽  
Weak Correlation ◽  
Correlation Matrices ◽  
Space Correlation ◽  
Content Type ◽  
Estimation Performance ◽  
Low Snr ◽  
Time Space

PurposeThe existing dimensionality reduction algorithms suffer serious performance degradation under low signal-to-noise ratio (SNR) owing to the presence of noise. To address these problems, an enhanced spatial smoothing scheme is proposed that exploits the subarray time-space correlation matrices to reconstruct the data matrix to overcome this weakness. This method uses the strong correlation of signal and the weak correlation of noise in time and space domains, which improves the noise suppression ability.Design/methodology/approachIn this paper, an enhanced spatial smoothing method is proposed. By using the strong correlation of signal and the weak correlation of noise, the time-space smoothed array covariance matrix based on the subarray time-space correlation matrices is constructed to improve the noise suppression ability. Compared with the existing Toeplitz matrix reconstruction and spatial smoothing methods, the proposed method improves the DOA estimation performance at low SNR.FindingsTheoretical analysis and simulation results show that compared with the existing dimensionality reduction processing algorithms, the proposed method improves the DOA estimation performance in cases with a low SNR. Furthermore, in cases where the DOAs between the coherent sources are closely spaced and the snapshot number is low, our proposed method significantly improves the performance of the DOA estimation performance.Originality/valueThe proposed method improves the DOA estimation performance at low SNR. In particular, for the cases with a low SNR, the proposed method provides a better RMSE. The convergence of the proposed method is also faster than other methods for the low number of snapshots. Our analysis also confirms that in cases where the DOAs between the coherent sources are closely spaced, the proposed method achieves a much higher angular resolution than that of the other methods.

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 MIMO Sonar DOA Estimation with Joint Matched-Filtering Based on Transmission Diversity Smoothing

2020 ◽  
Vol 38 (1) ◽  
pp. 6-13
Author(s):  
Kuan Fan ◽  
Chao Sun ◽  
Xionghou Liu ◽  
Guangyu Jiang
Keyword(s):  
Matched Filter ◽  
Signal Transmission ◽  
Multiple Input Multiple Output ◽  
Estimation Method ◽  
Impulse Response Function ◽  
Doa Estimation ◽  
Matched Filtering ◽  
Low Snr ◽  
Match Filtering ◽  
Transmission Diversity

There is a class of methods based on transmission diversity smoothing by multiple-input multiple-output(MIMO) sonar called MIMO-TDS which is considered as one of the most effective methods for estimation of direction-of-arrival(DOA) using MIMO sonar systems. MIMO-TDS produced by orthogonal signal transmission for active sonar can be immediately implemented with high resolution algorithms such as MVDR to estimate the direction of received signals. However, the orthogonal transmission mode of MIMO-TDS is doomed to a loss of transmission array gain indirectly leading to the problem that the echoes are not equipped with as high SNR as enough for an accurate target localization, especially in scenarios in which the targets are far away from array. In order to solving the "low SNR" problem, a solution using all transmitted signals simultaneously to design a joint matched-filter intended for received signal is proposed to improve the performance of MIMO-TDS, which is inspired by the match-filtering concept of "MIMO sonar virtual array method" simplified as MIMO-VA. And accordingly, the unit impulse response function of proposed joint matched-filter is the equivalent of linear sum of all orthogonal transmitted signals and the modified MIMO-TDS is named as "joint matched-filtering MIMO sonar transmission diversity smoothing DOA estimation method", which could be simplified as MIMO-TDS-MF. The characteristic of proposed method is analyzed theoretically and compared to MIMO-TDS and MIMO-VA in this paper:Compared with MIMO-TDS, the proposed method not only retains the advantage of transmission diversity smoothing but also improves the SNR by joint match-filtering; What's more, compared with MIMO-VA, MIMO-TDS-MF is equipped with substantially less computation than the former due to an employment of much fewer matched-filters and is in possession of a superior robustness to that of MIMO-VA. Numerical experiments demonstrate the efficiency of proposed MIMO-TDS-MF.

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