Multistep Knowledge-Aided Iterative ESPRIT: Design and Analysis

Pinto, Silvio F. B.; Lamare, Rodrigo C. de

doi:10.1109/taes.2018.2811098

Cited by 24 publications

(18 citation statements)

References 35 publications

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“…In this section, we mainly compare the performance of the following algorithms: TLS-ESPRIT, Root-MUSIC, AF-ESPRIT in [5], MS-KAI-ESPRIT in [7], VI-MUSIC in [15], and the proposed VI-LC-ESPRIT algorithm. K is the number of source signals, N is the number of total sensors, and L is the sample snapshots.…”

Section: Complexity Analysismentioning

confidence: 99%

“…This algorithm eliminates the influence of the number of snapshots on the computational complexity through SVD and reduces the estimation complexity without losing estimation performance. Pinto [7] and others proposed an estimation algorithm abbreviated as MS-KAI-ESPRIT that performs better at a low signal-to-noise ratio (SNR) and small amounts of snapshots. First, this algorithm sets a convergence factor and roughly estimates the DOA of the source signal through the classical ESPRIT algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Velocity-independent and low-complexity method for 1D DOA estimation using an arbitrary cross-linear array

Ning¹,

Jing²,

Li³

et al. 2020

EURASIP J. Adv. Signal Process.

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This paper focuses on a low-complexity one-dimensional (1D) direction-of-arrival (DOA) algorithm with an arbitrary cross-linear array. This algorithm is highly accurate without the performance error usually caused by the uncertainty factor of the wave velocity in the underwater environment. The geometric relationship between two crossed linear arrays is employed to derive the expression of DOA estimation with the finding that this algorithm is capable of excluding the wave velocity variable in the DOA estimation expression. A method without parameter pairing is also proposed to reduce the complexity of this algorithm. Additionally, the influence of wave velocity is analyzed in terms of RMSE c and the Cramer-Rao bound (CRB) for 1D DOA with the arbitrary cross-linear array is established. The simulation results demonstrate that the proposed algorithm can achieve better performance than the traditional algorithm under the condition of an inaccurate estimate of wave velocity. Compared with the velocity-independent DOA algorithm, it exhibits the feature of low complexity.

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Section: Complexity Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Velocity-independent and low-complexity method for 1D DOA estimation using an arbitrary cross-linear array

Ning¹,

Jing²,

Li³

et al. 2020

EURASIP J. Adv. Signal Process.

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show abstract

“…e direction of arrival (DOA) [1] estimation problem is one of the important research directions in the signal processing of sensor arrays [2,3], and it is widely used in radar [4][5][6]. High-precision DOA estimation based on subspace has become the focus of research [7][8][9]; for example, multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariance techniques (ESPRIT) [10] can provide higher resolution to estimate the direction of arrival of uncorrelated and partially related signals [11][12][13]. But in practice, there are a large number of coherent sources due to multipath propagation and cofrequency interference [14].…”

Section: Introductionmentioning

confidence: 99%

Two-Dimensional DOA Estimation of MIMO Radar Coherent Source Based on Toeplitz Matrix Set Reconstruction

Zhang

Jin

2021

Security and Communication Networks

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In order to realize the high-precision direction of arrival (DOA) estimation of the coherent source of two-dimensional multiple-input and multiple-output (MIMO) radar, a solution is given by combining Toeplitz matrix set reconstruction. MIMO radar obtains a larger aperture with fewer arrays. Traditional two-dimensional reconstruction Toeplitz-like algorithms use part of the information in the construction of two correlation matrices or covariance matrices to construct the Toeplitz matrix when performing two-dimensional coherent source DOA estimation, which makes the information utilization incomplete and requires additional denoising processing. To solve the above problems, this paper proposes an improved Toeplitz matrix set reconstruction algorithm based on the two-dimensional reconstruction Toeplitz class algorithm. The complete array element receiving signal vector is used to construct two Toeplitz matrix sets containing complete information, and then their conjugate transposes. Multiply and sum to correct the matrix to obtain a full-rank matrix, so as to achieve the purpose of decoherence and combine the traditional ESPRIT algorithm to perform two one-dimensional reconstruction processing through rotation invariance and then perform angle matching to achieve two-dimensional coherent signal angle estimation, while avoiding additional denoising processing. Finally, the simulation results of the cross array and the L-shaped array verify the effectiveness of the algorithm in this paper and further extend it to the two-dimensional MIMO radar array model and compare it with the traditional ESPRIT-like algorithm and the REC-FBSS-ESPRIT algorithm. In comparison, the algorithm in this paper has better performance under the conditions of successful resolution, DOA estimation accuracy, and low signal-to-noise ratio.

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“…In order to find an unique mapping between the signal x and the measurement y, the constraint of sparsity on x can be utilized [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], Corresponding author: Sheng Li (email: shengli@zjut.edu.cn). [37], [38], [39], [40], [41], [42], [43], [44], [45]. The sparse representation for x can be expressed as:…”

Section: Introductionmentioning

confidence: 99%

Design of Compressed Sensing System With Probability-Based Prior Information

Jiang

Zhu

et al. 2020

IEEE Trans. Multimedia

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This paper deals with the design of a sensing matrix along with a sparse recovery algorithm by utilizing the probability-based prior information for compressed sensing system. With the knowledge of the probability for each atom of the dictionary being used, a diagonal weighted matrix is obtained and then the sensing matrix is designed by minimizing a weighted function such that the Gram of the equivalent dictionary is as close to the Gram of dictionary as possible. An analytical solution for the corresponding sensing matrix is derived which leads to low computational complexity. We also exploit this prior information through the sparse recovery stage and propose a probability-driven orthogonal matching pursuit algorithm that improves the accuracy of the recovery. Simulations for synthetic data and application scenarios of surveillance video are carried out to compare the performance of the proposed methods with some existing algorithms. The results reveal that the proposed CS system outperforms existing CS systems.

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Multistep Knowledge-Aided Iterative ESPRIT: Design and Analysis

Cited by 24 publications

References 35 publications

Velocity-independent and low-complexity method for 1D DOA estimation using an arbitrary cross-linear array

Velocity-independent and low-complexity method for 1D DOA estimation using an arbitrary cross-linear array

Two-Dimensional DOA Estimation of MIMO Radar Coherent Source Based on Toeplitz Matrix Set Reconstruction

Design of Compressed Sensing System With Probability-Based Prior Information

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