A Tensor-Based Subspace Approach for Bistatic MIMO Radar in Spatial Colored Noise

Wang, Xianpeng; Wang, Weiwei; Li, Xin; Wang, Junxiang

doi:10.3390/s140303897

Cited by 37 publications

(30 citation statements)

References 17 publications

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“…≥ s(r) > 0, where r denotes the rank of matrix A. The SVD domain is used in many signal processing applications [38][39][40]. It is particularly suitable for noise level estimation as it enables the separation of the underlying image signal and the additive noise.…”

Section: Noise Level Estimation In the Svd Domainmentioning

confidence: 99%

Application of Artificial Neural Network for Image Noise Level Estimation in the SVD domain

2019

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The blind additive white Gaussian noise level estimation is an important and a challenging area of digital image processing with numerous applications including image denoising and image segmentation. In this paper, a novel block-based noise level estimation algorithm is proposed. The algorithm relies on the artificial neural network to perform a complex image patch analysis in the singular value decomposition (SVD) domain and to evaluate noise level estimates. The algorithm exhibits the capacity to adjust the effective singular value tail length with respect to the observed noise levels. The results of comparative analysis show that the proposed ANN-based algorithm outperforms the alternative single stage block-based noise level estimating algorithm in the SVD domain in terms of mean square error (MSE) and average error for all considered choices of block size. The most significant improvements in MSE levels are obtained at low noise levels. For some test images, such as “Car” and “Girlface”, at σ = 1 , these improvements can be as high as 99% and 98.5%, respectively. In addition, the proposed algorithm eliminates the error-prone manual parameter fine-tuning and automates the entire noise level estimation process.

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Section: Noise Level Estimation In the Svd Domainmentioning

confidence: 99%

Application of Artificial Neural Network for Image Noise Level Estimation in the SVD domain

2019

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“…In the past decades, various spectrum estimation algorithms have been proposed. Typical algorithms including multiple signal classification (MUSIC) [8][9][10], estimating signal parameters via rotational invariance technique (ESPRIT) [11,12], propagator method [13], maximum likelihood (ML) [14,15], tensor-based approaches [16][17][18][19][20], and optimization-aware algorithms [21][22][23][24][25][26][27]. Generally speaking, MUSIC is computationally inefficient as it requires multiple peak search.…”

Section: Introductionmentioning

confidence: 99%

Joint Angle and Frequency Estimation Using One-Bit Measurements

Shi

Wang

et al. 2019

Sensors

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Joint angle and frequency estimation is an important branch in array signal processing with numerous applications in radar, sonar, wireless communications, etc. Extensive attention has been paid and numerous algorithms have been developed. However, existing algorithms rely on accurately quantified measurements. In this paper, we stress the problem of angle and frequency estimation for sensor arrays using one-bit measurements. The relationship between the covariance matrices of one-bit measurement and that of the accurately quantified measurement is extended to the tensor domain. Moreover, a one-bit parallel factor analysis (PARAFAC) estimator is proposed. The simulation results show that the angle and frequency estimation can be quickly achieved and correctly paired.

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“…Two-dimensional direction of arrival (2-D DOA) (i.e., elevation and azimuth angles) estimation of multiple signals with different array geometries is an important problem in many practical applications such as radars and wireless communications. Various methods have been developed for solving this problem [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ], such as the subspace-based methods [ 1 , 2 , 3 , 4 , 5 , 6 ], the sparse reconstruction methods [ 7 , 8 , 9 ], and the least-square approach [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Improved Spatial Differencing Scheme for 2-D DOA Estimation of Coherent Signals with Uniform Rectangular Arrays

Shi

Sun

et al. 2017

Sensors

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This paper proposes an improved spatial differencing (ISD) scheme for two-dimensional direction of arrival (2-D DOA) estimation of coherent signals with uniform rectangular arrays (URAs). We first divide the URA into a number of row rectangular subarrays. Then, by extracting all the data information of each subarray, we only perform difference-operation on the auto-correlations, while the cross-correlations are kept unchanged. Using the reconstructed submatrices, both the forward only ISD (FO-ISD) and forward backward ISD (FB-ISD) methods are developed under the proposed scheme. Compared with the existing spatial smoothing techniques, the proposed scheme can use more data information of the sample covariance matrix and also suppress the effect of additive noise more effectively. Simulation results show that both FO-ISD and FB-ISD can improve the estimation performance largely as compared to the others, in white or colored noise conditions.

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A Tensor-Based Subspace Approach for Bistatic MIMO Radar in Spatial Colored Noise

Cited by 37 publications

References 17 publications

Application of Artificial Neural Network for Image Noise Level Estimation in the SVD domain

Application of Artificial Neural Network for Image Noise Level Estimation in the SVD domain

Joint Angle and Frequency Estimation Using One-Bit Measurements

Improved Spatial Differencing Scheme for 2-D DOA Estimation of Coherent Signals with Uniform Rectangular Arrays

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