Off-Grid Doa Estimation Based on Sparse Representation and Rife Algorithm

Xu, Lveqiu; Chen, Junli; Gao, and Yang

doi:10.2528/pierm17070404

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…The SS-MUSIC algorithm is more sensitive to the accuracy ofR yy in Eq. (12), as compared to the CS approach. 5, 2, 1, 1, 1, 1], respectively.…”

Section: Simulations and Discussionmentioning

confidence: 99%

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Doa Estimation Using Triply Primed Arrays Based on Fourth-Order Statistics

Hsu¹,

Kiang²

2018

PIER M

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Abstract-A triply primed array (TPA) is configured on three mutually primed integers (N 1 , N 2 and N 3 ), which operates with O(N 1 N 2 N 3 ) degree-of-freedoms to estimate the direction-of-arrivals (DOAs) of multiple incident quasi-stationary signals. The set of unique and contiguous lags of the proposed TPA is searched and verified. Simulation results verify that the proposed TPA can detect more incident signals with higher accuracy than its compatible counterparts.

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“…The SS-MUSIC algorithm is more sensitive to the accuracy ofR yy in Eq. (12), as compared to the CS approach. 5, 2, 1, 1, 1, 1], respectively.…”

Section: Simulations and Discussionmentioning

confidence: 99%

“…In [11], co-prime arrays were generalized to include nested array [4] as a special case. In [12], a nearest grid DOA estimation method, supported by Rife algorithm, was proposed to detect DOAs that were off the grid. In [13], a low-complexity 1 -normalization was proposed to merge same or conjugated lags.…”

Section: Introductionmentioning

confidence: 99%

Doa Estimation Using Triply Primed Arrays Based on Fourth-Order Statistics

Hsu¹,

Kiang²

2018

PIER M

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“…They are prone to fall into local optimality and have high request initial values for the iterations. To address the above limitations in calibration without calibration sources, SBL algorithms, initially proposed in compressive sensing theory [12,13], are subsequently introduced into the field of array signal processing and used to solve DOA estimation and errors correction in array signal processing [14][15][16]. The sparse reconstruction algorithm is used to recover the signal to reduce the number of samples, data transmission, storage, and processing cost of the signal and improve the parameter estimability.…”

Section: Introductionmentioning

confidence: 99%

Sparse Bayesian Learning Based Doa Estimation and Array Gain-Phase Error Self-Calibration

Li¹,

Zhao²

2022

PIER M

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This paper proposes a joint estimation algorithm based on sparse-Bayesian learning (SBL) for the gain-phase problem between array antenna channels. The algorithm uses the idea of the iterative method to jointly estimate the direction-of-arrival (DOA) and gain-phase error calibration coefficients in the iterative process, combining self-calibration and calibration with a calibration source. At each iteration, the rough value of DOA is first estimated using SBL, and then the DOA estimate is used to calculate the gain-phase error calibration coefficient. The value obtained in each iteration is brought into the error cost function, which is constructed based on the principle of signal and noise subspace orthogonality. Iterations are continued until convergence to find the minimum value of the cost function. The algorithm does not require a priori knowledge of array perturbations and has good performance in DOA and array gain and phase error estimation. Simulations and experimental measurements show that the method has better calibration performance than other methods based on optimization algorithms, and the algorithm effectively improves the antenna gain.

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An improved atomic search algorithm for optimization and application in ML DOA estimation of vector hydrophone array

Wang¹,

He²,

Guo³

et al. 2022

MATH

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<abstract><p>The atom search optimization (ASO) algorithm has the characteristics of fewer parameters and better performance than the traditional intelligent optimization algorithms, but it is found that ASO may easily fall into local optimum and its accuracy is not higher. Therefore, based on the idea of speed update in particle swarm optimization (PSO), an improved atomic search optimization (IASO) algorithm is proposed in this paper. Compared with traditional ASO, IASO has a faster convergence speed and higher precision for 23 benchmark functions. IASO algorithm has been successfully applied to maximum likelihood (ML) estimator for the direction of arrival (DOA), under the conditions of the different number of signal sources, different signal-to-noise ratio (SNR) and different population size, the simulation results show that ML estimator with IASO algorithum has faster convergence speed, fewer iterations and lower root mean square error (RMSE) than ML estimator with ASO, sine cosine algorithm (SCA), genetic algorithm (GA) and particle swarm optimization (PSO). Therefore, the proposed algorithm holds great potential for not only guaranteeing the estimation accuracy but also greatly reducing the computational complexity of multidimensional nonlinear optimization of ML estimator.</p></abstract>

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Off-Grid Doa Estimation Based on Sparse Representation and Rife Algorithm

Cited by 3 publications

References 16 publications

Doa Estimation Using Triply Primed Arrays Based on Fourth-Order Statistics

Doa Estimation Using Triply Primed Arrays Based on Fourth-Order Statistics

Sparse Bayesian Learning Based Doa Estimation and Array Gain-Phase Error Self-Calibration

An improved atomic search algorithm for optimization and application in ML DOA estimation of vector hydrophone array

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