A dual-resolution unitary ESPRIT method for DOA estimation based on sparse co-prime MIMO radar

Qiu, Shuang; Ma, Xiaofeng; Zhang, Renli; Han, Yubing; Sheng, Wexing

doi:10.1016/j.sigpro.2022.108753

Cited by 19 publications

(7 citation statements)

References 38 publications

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“…To separate these sources, the problem is then turned to spatial filtering to estimate the direction of each source with the uniform line array (ULA) [2,3]. DOA estimation is a meaningful topic in array signal processing and it has been widely used in radar, sonar, seismic exploration, radio astronomy, and wireless communication [4][5][6][7][8]. However, it is full of challenges in the underwater environment, especially because the received signals are coherent by multipath transmission.…”

Section: Introductionmentioning

confidence: 99%

Underwater wideband coherent signals DOA estimation using sparse representation and deconvolution

Fan,

Tao,

Qian

et al. 2024

Meas. Sci. Technol.

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The beamforming-based direction of arrival (DOA) estimation method is widely used because of its advantages of robustness, which requires large array aperture to ensure high spatial resolution. The deconvolution beam-forming method has high-resolution DOA estimation without increasing the array aperture. However, it is based on the assumption of incoherent narrowband signals. When the signals are coherent, the high cross-term sidelobes result in large estimation errors. In this paper, a new sparse deconvolution beamforming is proposed to estimate the DOA of wideband coherent signals. First, extending the deconvolution beamforming method to the wideband case by matrix transformation. Second, solving the problem that the traditional deconvolution algorithm is not applicable to coherent signals by sparse representation. Numerical simulations and underwater experiments are presented to verify the effectiveness of the proposed method. The results show that even at low Signal-to-Noise Ratio (SNR) and blind environment parameters, the proposed method can obtain better performance compared with other coherent signals DOA estimation algorithms.

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Section: Introductionmentioning

confidence: 99%

Underwater wideband coherent signals DOA estimation using sparse representation and deconvolution

Fan,

Tao,

Qian

et al. 2024

Meas. Sci. Technol.

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“…Source localization is a research branch of array signal processing, and it plays an important role in radar, electronic monitoring, navigation, aerospace, and medical imaging [1][2][3]. In the initial stage of source location research, the direction of incoming wave is mainly estimated for radiation far-field (RFF) sources and a large number of related algorithms are proposed [4][5][6][7][8]. With the rapid development of 5G and MIMO technologies [8,9], the space electromagnetic field environment becomes more complex, and the distances between some sources and arrays are less than the Rayleigh distance.…”

Section: Introductionmentioning

confidence: 99%

“…In the initial stage of source location research, the direction of incoming wave is mainly estimated for radiation far-field (RFF) sources and a large number of related algorithms are proposed [4][5][6][7][8]. With the rapid development of 5G and MIMO technologies [8,9], the space electromagnetic field environment becomes more complex, and the distances between some sources and arrays are less than the Rayleigh distance. The location of such sources also needs to consider the estimation of source range parameters.…”

Section: Introductionmentioning

confidence: 99%

A High Degree of Freedom Radiation Near-Field Source Localization Algorithm with Gain–Phase Error

Zhang,

Li,

et al. 2024

Journal of Sensors

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The limitation of the number of estimable sources in the localization of radiation near-field sources with gain–phase error is examined in this paper. When only the reference element has no gain–phase error, a new method based on an accurate model is proposed to enhance the maximum number of estimable sources. Based on the location parameter details of the auxiliary source, the method first derives the gain–phase error estimate matrix. Second, the source steering vector including errors is estimated using the total least square estimating signal parameter via rotational invariance techniques (TLS-ESPRIT), and the time-shifted data matrix is built utilizing the space–time combination idea, thus increasing the degree of freedom of the array. Then, the source steering vector containing the error is modified by the error compensation matrix constructed according to the moment of gain–phase error estimation. Finally, the estimated values of the source position parameters are obtained by using the closed formula of the gain phase of the modified source steering vector and the source position parameters. The experimental results show that the maximum estimable source number of the proposed algorithm is significantly improved compared with the previous results when only the reference array element has no gain–phase error. When the array number is 5 and 9, the maximum estimable source number of the algorithm is 9 and 17, respectively.

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“…However, most studies on DOA estimation have been performed assuming white Gaussian conditions of ideal additive noise. As a result, many algorithms have been developed, including super-resolution DOA estimation algorithms such as Multiple Signal Classification (MUSIC) [4][5][6] and Signal Parameter Estimation Using Rotational Invariance Techniques (ESPRIT) [7][8][9]. In practical environments, many signals and noises do not exactly follow Gaussian distributions.…”

Section: Introductionmentioning

confidence: 99%

A Novel DOA Estimation Algorithm Based on Robust Mixed Fractional Lower-Order Correntropy in Impulsive Noise

Lan,

Hu,

Zhang

et al. 2024

Electronics

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The estimation of direction of arrival (DOA) is paramount in the realm of practical array signal processing systems. Nevertheless, traditional estimation methods often rely heavily on the Gaussian noise assumption, rendering them ineffective in achieving high-precision estimates in environments plagued by strong impulsive noise. To address this challenge, this paper introduces a novel DOA estimation algorithm that leverages mixed fractional lower-order correntropy (MFLOCR) in the context of Alpha-stable distributed impulsive noise. Correntropy is used as a measure of the similarity of the signals, using a Gaussian function to smooth extreme values and provide greater robustness against impulsive noise. By utilizing diverse kernel lengths to jointly regulate the kernel function, the concept of correntropy is expanded and implemented in the fractional lower-order moment (FLOM) algorithm for received signals. Subsequently, the MFLOCR is derived by adjusting the resulting form of correntropy. Finally, an enhanced DOA estimation algorithm is proposed that combines the MFLOCR operator with the MUSIC algorithm, specifically tailored for impulsive noise environments. Furthermore, a proof of boundedness is provided to validate the effectiveness of the proposed approach in such noisy conditions. Simulation experiments confirmed that the proposed method outperforms existing DOA estimation methods in the context of intense impulsive noise, a low generalized signal-to-noise ratio (GSNR), and a smaller number of snapshots.

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A dual-resolution unitary ESPRIT method for DOA estimation based on sparse co-prime MIMO radar

Cited by 19 publications

References 38 publications

Underwater wideband coherent signals DOA estimation using sparse representation and deconvolution

Underwater wideband coherent signals DOA estimation using sparse representation and deconvolution

A High Degree of Freedom Radiation Near-Field Source Localization Algorithm with Gain–Phase Error

A Novel DOA Estimation Algorithm Based on Robust Mixed Fractional Lower-Order Correntropy in Impulsive Noise

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