scholarly journals DOA Estimation for Underwater Target by Active Detection on Virtual Time Reversal Using a Uniform Linear Array

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2458 ◽  
Author(s):  
Haixia Jing ◽  
Haiyan Wang ◽  
Zhengguo Liu ◽  
Xiaohong Shen
Keyword(s):  
Time Reversal ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Doa Estimation ◽  
Signal Propagation ◽  
Propagation Model ◽  
Multipath Effects ◽  
Virtual Time ◽  
Underwater Target ◽  
Active Detection

Aiming at addressing the problem caused by multipath effects in direction of arrival (DOA) estimation for underwater targets, a method based on the active detection on virtual time reversal (ADVTR) Capon algorithm is proposed. Unlike the conventional passive target estimation method ignoring the multipath effects but only considering the direct wave, the proposed method is closer to the actual situation in that the multipath signal propagation model is fully taken into account; in addition, active detection (AD) and virtual time reversal (VTR) processes are added, which use active detection to estimate channels, and virtual time reversal to realize focusing in a computer after the source-receive array (SRA) receives the reflected signal of the target. The combination of the two methods can greatly improve the energy of SRA and the precision of target direction estimation. With the popular acoustic field simulation tool Bellhop, the model proposed in this paper is verified. Compared with the conventional Capon method without time reversal, the simulation results show that the ADVTR Capon estimation method is far better, in terms of resolution and suppressing the sidelobes. It is suitable for the target DOA estimation under low signal-to-noise ratio (SNR) conditions. Further, we also show the ADVTR Capon estimation method works well in a real tank experiment.

scholarly journals Low Complexity Sparse Beamspace DOA Estimation Via Single Measurement Vectors for Uniform Circular Array

2021 ◽  
Author(s):  
Di Zhao ◽  
Weijie Tan ◽  
Zhongliang Deng ◽  
Gang Li
Keyword(s):  
Optimization Problem ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Doa Estimation ◽  
Low Complexity ◽  
Estimation Methods ◽  
Circular Array ◽  
Single Measurement ◽  
Signal Model ◽  
Convex Optimization Problem

Abstract In this paper, we present a low complexity beamspace direction-of-arrival (DOA) estimation method for uniform circular array (UCA), which is based on the single measurement vectors (SMVs) via vectorization of sparse covariance matrix. In the proposed method, we rstly transform the signal model of UCA to that of virtual uniform linear array (ULA) in beamspace domain using the beamspace transformation (BT). Subsequently, by applying the vectorization operator on the virtual ULA-like array signal model, a new dimension-reduction array signal model consists of SMVs based on Khatri-Rao (KR) product is derived. And then, the DOA estimation is converted to the convex optimization problem. Finally, simulations are carried out to verify the eectiveness of the proposed method, the results show that without knowledge of the signal number, the proposed method not only has higher DOA resolution than subspace-based methods in low signal-to-noise ratio (SNR), but also has much lower computational complexity comparing other sparse-like DOA estimation methods.

scholarly journals DOA Estimation for UCAs Based on Virtual Subarray Beamforming Technique

2018 ◽  
Vol 232 ◽  
pp. 01012
Author(s):  
Bo Xu ◽  
Zhigang Huang
Keyword(s):  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Signal To Noise ◽  
Circular Arrays ◽  
Noise Ratio ◽  
Subarray Beamforming ◽  
Beamforming Technique

Direction-of-arrival (DOA) estimation is always a hotspot research in the fields of radar, sonar, communication and so on. And uniform circular arrays (UCAs) are more attractive in the context of DOA estimation since their symmetrical structures have potential to provide two directions coverage. This paper proposed a new DOA estimation method for UCAs via virtual subarray beamforming technique. The method would provide an acceptable DOA estimate even if the number of sources is great than the number of array elements. Also, the performance of the proposed method would hold good when the snapshot length or the signal-to-noise ratio (SNR) is small. Simulations show that the proposed technique offers significantly improved estimation resolution, capacity, and accuracy relative to the existing techniques.

scholarly journals DOA Estimation Method in Multipath Environment for Passive Bistatic Radar

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Panhe Hu ◽  
Qinglong Bao ◽  
Zengping Chen
Keyword(s):  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Doa Estimation ◽  
Estimation Accuracy ◽  
Bistatic Radar ◽  
Spatial Difference ◽  
Coherent Signals ◽  
Vhf Radar ◽  
Multipath Environment ◽  
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.

scholarly journals Multiple Sparse Measurement Gradient Reconstruction Algorithm for DOA Estimation in Compressed Sensing

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Weijian Si ◽  
Xinggen Qu ◽  
Yilin Jiang ◽  
Tao Chen
Keyword(s):  
Convex Optimization ◽  
Compressed Sensing ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Reconstruction Algorithm ◽  
Doa Estimation ◽  
Superior Performance ◽  
Multiple Measurement Vectors ◽  
Gradient Reconstruction ◽  
Quadratically Constrained

A novel direction of arrival (DOA) estimation method in compressed sensing (CS) is proposed, in which the DOA estimation problem is cast as the joint sparse reconstruction from multiple measurement vectors (MMV). The proposed method is derived through transforming quadratically constrained linear programming (QCLP) into unconstrained convex optimization which overcomes the drawback thatl1-norm is nondifferentiable when sparse sources are reconstructed by minimizingl1-norm. The convergence rate and estimation performance of the proposed method can be significantly improved, since the steepest descent step and Barzilai-Borwein step are alternately used as the search step in the unconstrained convex optimization. The proposed method can obtain satisfactory performance especially in these scenarios with low signal to noise ratio (SNR), small number of snapshots, or coherent sources. Simulation results show the superior performance of the proposed method as compared with existing 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 Augmented Lagrange Based on Modified Covariance Matching Criterion Method for DOA Estimation in Compressed Sensing

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Weijian Si ◽  
Xinggen Qu ◽  
Lutao Liu
Keyword(s):  
Compressed Sensing ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Doa Estimation ◽  
Semidefinite Program ◽  
Matching Criterion ◽  
Multiple Measurement Vectors ◽  
Covariance Matching ◽  
Augmented Lagrange Method ◽  
Cramer Rao Bound

A novel direction of arrival (DOA) estimation method in compressed sensing (CS) is presented, in which DOA estimation is considered as the joint sparse recovery from multiple measurement vectors (MMV). The proposed method is obtained by minimizing the modified-based covariance matching criterion, which is acquired by adding penalties according to the regularization method. This minimization problem is shown to be a semidefinite program (SDP) and transformed into a constrained quadratic programming problem for reducing computational complexity which can be solved by the augmented Lagrange method. The proposed method can significantly improve the performance especially in the scenarios with low signal to noise ratio (SNR), small number of snapshots, and closely spaced correlated sources. In addition, the Cramér-Rao bound (CRB) of the proposed method is developed and the performance guarantee is given according to a version of the restricted isometry property (RIP). The effectiveness and satisfactory performance of the proposed method are illustrated by simulation results.

scholarly journals DOA Estimation of Unknown Emitter Signal Based on Time Reversal and Coprime Array

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1398 ◽  
Author(s):  
Bing Li ◽  
Shiqi Liu ◽  
Deshuang Zhao ◽  
Bin-Jie Hu
Keyword(s):  
Time Reversal ◽  
Noise Suppression ◽  
Fuzzy Inference ◽  
Signal To Noise Ratio ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Channel Noise ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Coprime Array

In this paper, a novel direction-of-arrival (DOA) estimation for unknown (anonymous) emitter signal (ES) based on time reversal (TR) and coprime array (CA) is proposed. The resolution and accuracy of DOA estimation are enhanced from two aspects: one is from the view of array arrangement: the new distribution of CA is designed to reduce the holes, increase the degree of freedom (DOF) and apertures by rotating and translating only one subarray, which simplifies the operation. The other one is from the view of the algorithm: a neoteric DOA estimation algorithm with noise suppression based on TR, Capon and adaptive neuro-fuzzy inference system (ANFIS) is proposed for solving the wide sidelobe, multipath effect, low resolution and accuracy produced by conventional algorithms, in particular, those cannot work effectively under the existed hole condition. Furthermore, the resubmitting distorted noise and channel noise are suppressed effectively, which is not taken into considered in the conventional Capon algorithm. Simulation results including the resolution, accuracy, root mean square error (RMSE), Cramér-Rao lower bound (CRLB) and the compared analyses on uniform linear array (ULA), nested array (NA) and minimum redundancy array(MRA) demonstrate the performance advantages of the proposed DOA estimation algorithm even at very low signal-to-noise ratio (SNR) condition.

scholarly journals Low complexity sparse beamspace DOA estimation via single measurement vectors for uniform circular array

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Di Zhao ◽  
Weijie Tan ◽  
Zhongliang Deng ◽  
Gang Li
Keyword(s):  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Doa Estimation ◽  
Low Complexity ◽  
Estimation Methods ◽  
Observation Data ◽  
Circular Array ◽  
Single Measurement ◽  
Signal Model ◽  
Element Space

AbstractIn this paper, we present a low complexity sparse beamspace direction-of-arrival (DOA) estimation method for uniform circular array (UCA). In the proposed method, we firstly use the beamspace transformation (BT) to transform the signal model of UCA in element-space domain to that of virtual uniform linear array (ULA) in beamspace domain. Subsequently, by applying the vectoring operator on the virtual ULA-like array signal model, a novel dimension-reduction sparse beamspace signal model is derived based on Khatri-Rao (KR) product, the observation data of which is represented by the single measurement vectors (SMVs) via vectorization of sparse covariance matrix. And then, the DOA estimation is formulated as a convex optimization problem by following the concept of a sparse-signal-representation (SSR) of the SMVs. Finally, simulations are carried out to validate the effectiveness of the proposed method. The results show that without knowledge of the number of signals, the proposed method not only has higher DOA resolution than the subspace-based methods in low signal-to-noise ratio (SNR), but also has far lower computational complexity than other sparse-like DOA estimation methods.

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