Bounded non-linear covariance based ESPRIT method for noncircular signals in presence of impulsive noise

Zhang, Jiacheng; Qiu, Tianshuang; Luan, Shengyang; Li, Houjie

doi:10.1016/j.dsp.2019.01.018

Cited by 28 publications

(15 citation statements)

References 34 publications

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“…On this basis, [24] proposes an augmented phased fractional loworder moment (APFLOM) method with better performance than the work in [23] by utilizing the non-circular property of signal. Additionally, [29] extends the previous work in [21] to the nested array scenario [30]- [33] and proposes an enhanced bounded nonlinear covariance (EBNC) method, which presents better estimation performance than the BNC method [21]. However, the above mentioned methods rely on an unknown parameter that needs to be adjusted artificially (the choice of the order moment parameter is involved in the ROC, FLOM, PFLOM, and EBNC methods).…”

Section: Introductionmentioning

confidence: 95%

“…However, its performance may degrade with the increase of signal subspace rank. In addition, the correlation entropy [11]- [14], sparse Bayesian learning (SBL, [15]- [17]), sparse representation [18], [19], l p -MUSIC [20] and the bounded nonlinear covariance (BNC)-based method [21] are used for DOA estimation in impulsive noise. Nevertheless, these methods are only applicable to conventional uniform linear arrays (ULAs), and there are still gaps in sparse array scenarios.…”

Section: Introductionmentioning

confidence: 99%

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DOA Estimation With Nested Arrays in Impulsive Noise Scenario: An Adaptive Order Moment Strategy

Dong,

Zhao,

Pan

et al. 2024

IEEE Open J. Signal Process.

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Most of the existing direction of arrival (DOA) estimation methods in impulsive noise scenario are based on the fractional low-order moment statistics (FLOSs), such as the robust covariation-based (ROC), fractional low-order moment (FLOM), and phased fractional low-order moment (PFLOM). However, an unknown order moment parameter p needs to be selected in these approaches, which inevitably increases the computational load if the optimal value of the parameter p is determined by a large number of Monte Carlo experiments. To address this issue, we propose the adaptive order moment function (AOMF) and improved AOMF (IAOMF), which are applicable to the existing FLOSs-based methods and can also be extended to the case of sparse arrays. Moreover, we analyze the performance of AOMF and IAOMF, and simulation experiments verify the effectiveness of proposed methods.

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Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

DOA Estimation With Nested Arrays in Impulsive Noise Scenario: An Adaptive Order Moment Strategy

Dong,

Zhao,

Pan

et al. 2024

IEEE Open J. Signal Process.

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show abstract

“…Moreover, two or all three of the approaches can be combined together to increase the DOFs further. Examples include algorithms combining the noncircular properties and the fourthorder cumulants [15,16,17,18,19], combining the noncircular properties and sparse arrays [20,21,22,23], or combining the fourth-order cumulants and sparse arrays [24,25,26,27,28]. In [29], all the three approaches are employed together.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the fourth-order co-array for a mixture of circular and noncircular signals

Cai

Liu

et al. 2023

Digital Signal Processing

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“…Under an impulsive noise environment, the conventional DOA estimation methods, which are based on second-order statistics (SOS) or higherorder statistics (HOS), suffer serious performance degradation as SOS and HOS are not convergent for non-Gaussian impulsive noise. To suppress impulsive noise for DOA estimation, many concepts have been developed such as fractional lower-order statistics [24][25][26], correntropy [27,28], nonlinear functions [29,30] and so on. However, there are few works that focus on solving DOA estimation with MIMO radar based on sparse representation in impulsive noise.…”

Section: Introductionmentioning

confidence: 99%

Off-Grid DOA Estimation Using Sparse Bayesian Learning for MIMO Radar under Impulsive Noise

Zhang

Yang

et al. 2022

Sensors

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Direction of arrival (DOA) estimation is an essential and fundamental part of array signal processing, which has been widely used in radio monitoring, autonomous driving of vehicles, intelligent navigation, etc. However, it remains a challenge to accurately estimate DOA for multiple-input multiple-output (MIMO) radar in impulsive noise environments. To address this problem, an off-grid DOA estimation method for monostatic MIMO radar is proposed to deal with non-circular signals under impulsive noise. In the proposed method, firstly, based on the property of non-circular signal and array structure, a virtual array output was built and a real-valued sparse representation for the signal model was constructed. Then, an off-grid sparse Bayesian learning (SBL) framework is proposed and further applied to the virtual array to construct novel off-grid sparse model. Finally, off-grid DOA estimation was realized through the solution of the sparse reconstruction with high accuracy even in impulsive noise. Numerous simulations were performed to compare the algorithm with existing methods. Simulation results verify that the proposed off-grid DOA method enables evident performance improvement in terms of accuracy and robustness compared with other works on impulsive noise.

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Bounded non-linear covariance based ESPRIT method for noncircular signals in presence of impulsive noise

Cited by 28 publications

References 34 publications

DOA Estimation With Nested Arrays in Impulsive Noise Scenario: An Adaptive Order Moment Strategy

DOA Estimation With Nested Arrays in Impulsive Noise Scenario: An Adaptive Order Moment Strategy

Analysis of the fourth-order co-array for a mixture of circular and noncircular signals

Off-Grid DOA Estimation Using Sparse Bayesian Learning for MIMO Radar under Impulsive Noise

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