Compressive Sensing for High-Resolution Direction-of-Arrival Estimation via Iterative Optimization on Sensing Matrix

A novel compressive sensing- (CS-) based direction-of-arrival (DOA) estimation algorithm is proposed to solve the performance degradation of the CS-based DOA estimation in the presence of sensing matrix mismatching. Firstly, a DOA sparse sensing model is set up in the presence of sensing matrix mismatching. Secondly, combining the Dantzig selector (DS) algorithm and least-absolute shrinkage and selection operator (LASSO) algorithm, a CS-based DOA estimation algorithm which performs iterative optimization alternatively on target angle information vector and sensing matrix mismatching error vector is proposed. The simulation result indicates that the proposed algorithm possesses higher angle resolution and estimation accuracy compared with conventional CS-based DOA estimation algorithms.

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Two-Dimensional Direction of Arrival (DOA) Estimation for Rectangular Array via Compressive Sensing Trilinear Model

International Journal of Antennas and Propagation ◽

10.1155/2015/297572 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 4

Author(s):

Huaxin Yu ◽

Xiaofeng Qiu ◽

Xiaofei Zhang ◽

Chenghua Wang ◽

Gang Yang

Keyword(s):

Compressive Sensing ◽

Direction Of Arrival ◽

Estimation Algorithm ◽

Doa Estimation ◽

Two Dimensional ◽

Rectangular Array ◽

Angle Estimation ◽

Estimation Performance ◽

Trilinear Model ◽

2D Doa Estimation

We investigate the topic of two-dimensional direction of arrival (2D-DOA) estimation for rectangular array. This paper links angle estimation problem to compressive sensing trilinear model and derives a compressive sensing trilinear model-based angle estimation algorithm which can obtain the paired 2D-DOA estimation. The proposed algorithm not only requires no spectral peak searching but also has better angle estimation performance than estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. Furthermore, the proposed algorithm has close angle estimation performance to trilinear decomposition. The proposed algorithm can be regarded as a combination of trilinear model and compressive sensing theory, and it brings much lower computational complexity and much smaller demand for storage capacity. Numerical simulations present the effectiveness of our approach.

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DOA Estimation for Uniform Linear Array with Compressive Sensing

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.713-715.1239 ◽

2015 ◽

Vol 713-715 ◽

pp. 1239-1243

Author(s):

Ying Zhang ◽

Guang Yao Xin ◽

Xiao Fei Zhang

Keyword(s):

Compressive Sensing ◽

Linear Array ◽

Signal To Noise Ratio ◽

High Sensitivity ◽

Estimation Algorithm ◽

Doa Estimation ◽

Signal To Noise ◽

Uniform Linear Array ◽

High Demand ◽

Estimation Algorithms

This paper discusses that the application of compressive sensing in direction of arrival (DOA) estimation. Traditional DOA estimation algorithms, such as MUSIC, ESPRIT, have shortcomings of high demand of initialization and sufficient number of snapshots and high sensitivity to signal-to-noise ratio (SNR). The proposed DOA estimation algorithm via OMP method based on compressed sensing (CS) can solve the above-mentioned problem and has good estimation performance. Computer simulations verify the effectiveness of the OMP algorithm.

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Wideband DOA Estimation on Co-prime Array via Atomic Norm Minimization

Energies ◽

10.3390/en13123235 ◽

2020 ◽

Vol 13 (12) ◽

pp. 3235 ◽

Cited By ~ 2

Author(s):

Hyeonjin Chung ◽

Young Mi Park ◽

Sunwoo Kim

Keyword(s):

Compressive Sensing ◽

Estimation Algorithm ◽

Doa Estimation ◽

Low Complexity ◽

Time Saving ◽

Estimation Algorithms ◽

Norm Minimization ◽

Atomic Norm ◽

Multiple Measurement Vector ◽

Low Dimensional

This paper introduces a low complexity wideband direction-of-arrival (DOA) estimation algorithm on the co-prime array. To increase the number of the detectable signal sources and to prevent an unnecessary increase in complexity, the low dimensional co-prime array vector is constructed by arranging elements of the correlation matrix at every frequency bin. The atomic norm minimization (ANM)-based approach resolves the grid-mismatch, which causes an inevitable error in the compressive sensing (CS)-based DOA estimation. However, the complexity surges when the ANM is exploited to the wideband DOA estimation on the co-prime array. The surging complexity of the ANM-based wideband DOA estimation on the co-prime array is handled by solving the time-saving semidefinite programming (SDP) motivated by the ANM for multiple measurement vector (MMV) case. Simulation results show that the proposed algorithm has high accuracy and low complexity compared to compressive sensing (CS)-based wideband DOA estimation algorithms that exploit the co-prime array.

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Tensor-Based DOA Estimation for Array Virtual Translation

10.21203/rs.3.rs-842179/v1 ◽

2021 ◽

Author(s):

Jiaqiang Peng ◽

Guimei Zheng

Keyword(s):

Tensor Decomposition ◽

Estimation Algorithm ◽

Doa Estimation ◽

Processing Technique ◽

Estimation Accuracy ◽

Signal Subspace ◽

Spatial Smoothing ◽

Factor Matrix ◽

Estimation Algorithms ◽

Array Aperture

Abstract In order to make up for the problem that the tensor-based spatial smoothing DOA estimation algorithm cannot make good use of the physical aperture of the array, this paper proposes a tensor-based array virtual translation DOA estimation algorithm. Under the framework of the tensor-based DOA estimation algorithm, the algorithm applies the array virtual translation technique to the factor matrix obtained after tensor decomposition, which can be expanded into signal subspace and approximately has a Vandermonde structure. Furthermore, the available array aperture of the algorithm is expanded, the estimation accuracy is improved, and the limitation of the physical array aperture on the algorithm’s multi-target estimation ability is broken. Since the processing technique proposed in this paper has nothing to do with the construction of tensors, this technique is suitable for all DOA estimation algorithms based on tensors. Theoretical analysis and numerical simulation verify the effectiveness of the algorithm proposed in this paper.

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Performance Analysis of Amplitude Comparison Monopulse Direction-of-Arrival Estimation

Applied Sciences ◽

10.3390/app10041246 ◽

2020 ◽

Vol 10 (4) ◽

pp. 1246 ◽

Cited By ~ 4

Author(s):

Do-Jin An ◽

Joon-Ho Lee

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Taylor Series Expansion ◽

Direction Of Arrival ◽

Estimation Algorithm ◽

Doa Estimation ◽

Estimation Accuracy ◽

Simulation Based ◽

The Mean ◽

Computationally Intensive

Amplitude-comparison monopulse radar in tracking radar uses the tracking scheme of a monopulse radar to estimate the angle components of a target. The performance of the amplitude comparison monopulse radar under measurement uncertainty is analysed. Measurement noises are modelled as Gaussian random variables. Taylor series expansion is adopted to get analytic expression of the mean square error (MSE). Estimation accuracy, in terms of the MSEs for estimate the direction-of-arrival (DOA) estimation algorithm, is usually obtained from the Monte Carlo simulation, which can be computationally intensive especially for large number of repetitions in the Monte Carlo simulation. To get reliable MSE in the Monte Carlo simulation, the number of repetitions should be very large, which implies that there is a trade-off between reliability of the MSE and computational burden in the Monte Carlo simulation. This paper shows the performance of amplitude comparison monopulse radar by linear approximation of nonlinear equations to estimate the DOA. The performance of amplitude comparison monopulse radar is quantitatively analysed via the MSEs, and the derived expression is validated by comparing the analytic MSEs with the simulation based MSEs. In addition, it is shown in the numerical results that analytically derived MSE is much less computationally intensive in comparison with the Monte Carlo simulation-based MSE, which implies that the proposed scheme in this paper results in drastic reduction in computational complexity for evaluation of the MSE.

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Direction-of-Departure and Direction-of-Arrival Estimation Algorithm Based on Compressive Sensing: Data Fitting

Remote Sensing ◽

10.3390/rs12172773 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2773

Author(s):

Ji Woong Paik ◽

Wooyoung Hong ◽

Joon-Ho Lee

Keyword(s):

Cost Function ◽

Compressive Sensing ◽

Direction Of Arrival ◽

Estimation Algorithm ◽

Doa Estimation ◽

Data Fitting ◽

Superior Performance ◽

Constraint Condition ◽

Direction Of Departure ◽

The Cost

In this paper, a compressive sensing-based data fitting direction-of-departure/direction-of-arrival (DOD/DOA) estimation algorithm is proposed to apply the superior performance of compressive sensing method to the bistatic MIMO sonar systems. The algorithm proposed in this paper optimizes the output data via convex optimization-based sparse recovery, so that it is possible to estimate the DOD and the DOA for each target accurately. In order to minimize the amount of computation, the cost function with constraint condition is implemented in this paper. Furthermore, the constraint condition parameter of the cost function is analytically derived. Through various simulations, it is shown that the superior DOD and DOA estimation performance of the proposed algorithm and that the analytical derivation of the constraint condition parameter is useful for determination of regularization parameter.

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Direction of Arrival Estimation Under Near-Field Interference Using Matrix Filter

Journal of Computational Acoustics ◽

10.1142/s0218396x1540007x ◽

2015 ◽

Vol 23 (04) ◽

pp. 1540007 ◽

Cited By ~ 3

Author(s):

Guolong Liang ◽

Wenbin Zhao ◽

Zhan Fan

Keyword(s):

Near Field ◽

Direction Of Arrival ◽

Doa Estimation ◽

Direction Of Arrival Estimation ◽

Far Field ◽

Data Simulation ◽

Estimation Algorithms ◽

Simulation Results

Direction of arrival (DOA) estimation is of great interest due to its wide applications in sonar, radar and many other areas. However, the near-field interference is always presented in the received data, which may result in degradation of DOA estimation. An approach which can suppress the near-field interference and preserve the far-field signal desired by using a spatial matrix filter is proposed in this paper and some typical DOA estimation algorithms are adjusted to match the filtered data. Simulation results show that the approach can improve capability of DOA estimation under near-field inference efficiently.

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An Enhanced Smoothed L0-Norm Direction of Arrival Estimation Method Using Covariance Matrix

Sensors ◽

10.3390/s21134403 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4403

Author(s):

Ji Woong Paik ◽

Joon-Ho Lee ◽

Wooyoung Hong

Keyword(s):

Covariance Matrix ◽

Phased Array ◽

Null Space ◽

Estimation Method ◽

Direction Of Arrival ◽

Estimation Algorithm ◽

Doa Estimation ◽

Direction Of Arrival Estimation ◽

Improve Performance ◽

Space Projection

An enhanced smoothed l0-norm algorithm for the passive phased array system, which uses the covariance matrix of the received signal, is proposed in this paper. The SL0 (smoothed l0-norm) algorithm is a fast compressive-sensing-based DOA (direction-of-arrival) estimation algorithm that uses a single snapshot from the received signal. In the conventional SL0 algorithm, there are limitations in the resolution and the DOA estimation performance, since a single sample is used. If multiple snapshots are used, the conventional SL0 algorithm can improve performance in terms of the DOA estimation. In this paper, a covariance-fitting-based SL0 algorithm is proposed to further reduce the number of optimization variables when using multiple snapshots of the received signal. A cost function and a new null-space projection term of the sparse recovery for the proposed scheme are presented. In order to verify the performance of the proposed algorithm, we present the simulation results and the experimental results based on the measured data.

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A Direction-of-Arrival Estimation Algorithm Based on Compressed Sensing and Density-Based Spatial Clustering and Its Application in Signal Processing of MEMS Vector Hydrophone

Sensors ◽

10.3390/s21062191 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2191

Author(s):

Huichao Yan ◽

Ting Chen ◽

Peng Wang ◽

Linmei Zhang ◽

Rong Cheng ◽

...

Keyword(s):

Compressed Sensing ◽

Spatial Clustering ◽

Direction Of Arrival ◽

Estimation Algorithm ◽

Doa Estimation ◽

Cluster Center ◽

Direction Of Arrival Estimation ◽

Vector Hydrophone ◽

Regularization Parameters ◽

Selection Of

Direction of arrival (DOA) estimation has always been a hot topic for researchers. The complex and changeable environment makes it very challenging to estimate the DOA in a small snapshot and strong noise environment. The direction-of-arrival estimation method based on compressed sensing (CS) is a new method proposed in recent years. It has received widespread attention because it can realize the direction-of-arrival estimation under small snapshots. However, this method will cause serious distortion in a strong noise environment. To solve this problem, this paper proposes a DOA estimation algorithm based on the principle of CS and density-based spatial clustering (DBSCAN). First of all, in order to make the estimation accuracy higher, this paper selects a signal reconstruction strategy based on the basis pursuit de-noising (BPDN). In response to the challenge of the selection of regularization parameters in this strategy, the power spectrum entropy is proposed to characterize the noise intensity of the signal, so as to provide reasonable suggestions for the selection of regularization parameters; Then, this paper finds out that the DOA estimation based on the principle of CS will get a denser estimation near the real angle under the condition of small snapshots through analysis, so it is proposed to use a DBSCAN method to process the above data to obtain the final DOA estimate; Finally, calculate the cluster center value of each cluster, the number of clusters is the number of signal sources, and the cluster center value is the final DOA estimate. The proposed method is applied to the simulation experiment and the micro electro mechanical system (MEMS) vector hydrophone lake test experiment, and they are proved that the proposed method can obtain good results of DOA estimation under the conditions of small snapshots and low signal-to-noise ratio (SNR).

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Multi-Frequency Based Direction-of-Arrival Estimation for 2q-Level Nested Radar & Sonar Arrays

Sensors ◽

10.3390/s18103385 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3385 ◽

Cited By ~ 3

Author(s):

Hao Zhou ◽

Guoping Hu ◽

Junpeng Shi ◽

Ziang Feng

Keyword(s):

Direction Of Arrival ◽

Estimation Algorithm ◽

Doa Estimation ◽

Research Area ◽

Frequency Separation ◽

Frequency Method ◽

Virtual Sensors ◽

Order Difference ◽

Sonar Systems ◽

Minimum Number

Direction finding is a hot research area in radar and sonar systems. In the case of q ≥ 2, the 2qth-order cumulant based direction of arrival (DOA) estimation algorithm for the 2q-level nested array can achieve high resolution performance. A virtual 2qth-order difference co-array, which contains O(N2q) virtual sensors in the form of a uniform linear array (ULA), is yielded and the Gaussian noise is eliminated. However, some virtual elements are separated by the holes among the 2qth-order difference co-array and cannot be fully used. Even though the application of the multi-frequency method for minimum frequency separation (MFMFS) can fill the holes with low computation complexity, it requires that the number of frequencies must increase with the number of holes. In addition, the signal spectra have to be proportional for all frequencies, which is hard to satisfy when the number of holes is large. Aiming at this, we further propose a multi-frequency method for a minimum number of frequencies (MFMNF) and discuss the best frequency choice under two specific situations. Simulation results verify that, compared with the MFMFS method, the proposed MFMNF method can use only one frequency to fill all the holes while achieving a longer virtual array and the DOA estimation performance is, therefore, improved.

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