An Improved ESPRIT-Based Algorithm for Monostatic FDA-MIMO Radar With Linear or Nonlinear Frequency Increments

Feng, Maoyuan; Yang, Yunxiu; Shu, Qin; Yang, Rong

doi:10.1109/lcomm.2021.3075208

Cited by 14 publications

(5 citation statements)

References 13 publications

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“…denotes the virtual array manifold, unlike the steering vector a(θ k ) in (8). Obviously, such a vectorized signal model effectively increases the degrees of freedom (DOFs) of the virtual array and expands the array aperture.…”

Section: Spsf Principlementioning

confidence: 99%

“…Currently, these efforts towards direction finding can be roughly grouped into two categories, i.e., subspace technologies [5][6][7][8] and sparse signal recovery (SSR) attempts [9][10][11][12][13]. The former are represented by the multiple signal classification (MUSIC) algorithm [5] and the estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm [6], officially opening the era of super-resolution direction finding.…”

Section: Introductionmentioning

confidence: 99%

“…The former are represented by the multiple signal classification (MUSIC) algorithm [5] and the estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm [6], officially opening the era of super-resolution direction finding. Afterwards, subspace estimators [7,8] are successively refined from different aspects, such as accuracy and robustness. These subspace frameworks largely rest with the eigenvalue decomposition (EVD) of the covariance matrix to decouple the subspaces, and then exploit the inherent relationships between the array manifolds and the decoupled subspaces to estimate DOAs.…”

Section: Introductionmentioning

confidence: 99%

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Reweighted Off-Grid Sparse Spectrum Fitting for DOA Estimation in Sensor Array with Unknown Mutual Coupling

Wang

Lan

et al. 2023

Sensors

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In the environment of unknown mutual coupling, many works on direction-of-arrival (DOA) estimation with sensor array are prone to performance degradation or even failure. Moreover, there are few literatures on off-grid direction finding using regularized sparse recovery technology. Therefore, the scenario of off-grid DOA estimation in sensor array with unknown mutual coupling is investigated, and then a reweighted off-grid Sparse Spectrum Fitting (Re-OGSpSF) approach is developed in this article. Inspired by the selection matrix, an undisturbed array output is formed to remove the unknown mutual coupling effect. Subsequently, a refined off-grid SpSF (OGSpSF) recovery model is structured by integrating the off-grid error term obtained from the first-order Taylor approximation of the higher-order term into the underlying on-grid sparse representation model. After that, a novel Re-OGSpSF framework is formulated to recover the sparse vectors, where a weighted matrix is developed by the MUSIC-like spectrum function to enhance the solution’s sparsity. Ultimately, off-grid DOA estimation can be realized with the help of the recovered sparse vectors. Thanks to the off-grid representation and reweighted strategy, the proposed method can effectively and efficiently achieve high-precision continuous DOA estimation, making it favorable for real-time direction finding. The simulation results validate the superiority of the proposed method.

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Section: Spsf Principlementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reweighted Off-Grid Sparse Spectrum Fitting for DOA Estimation in Sensor Array with Unknown Mutual Coupling

Wang

Lan

et al. 2023

Sensors

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show abstract

“…The nonlinear problems in the above formula can be ignored. But when a linear system works, a single-frequency sine wave will have harmonics, division, and difference [7]. Because of such a waveform, the principle of linear superposition of waves cannot meet the requirements, so it is necessary to use nonlinear sound theory instead.…”

Section: Dy Y Yy Dr Rmentioning

confidence: 99%

Simulation and Characteristic Analysis of College Music Teaching Based on Nonlinear Equation

2023

Applied Mathematics and Nonlinear Sciences

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Musical symbols are a good way of expressing human emotions and communication. It is a kind of nonlinear dynamic system with hysteresis. It isn’t easy to analyze its nonlinear characteristics by conventional methods. This paper divides it into several parts from the form of music. Secondly, this paper studies the local attributes of music signals and generalizes and deduces their general features. Then the Lyapunov index of music is numerically simulated, and its energy spectrum is analyzed. It is proved that the method is weak to some extent. Through the music correlation dimension analysis, we find its inherent nonlinear characteristics. The nonlinear music signals corresponding to classical piano works are given in this paper. Finally, the correlation dimension is numerically simulated by differential operation. The results show that the nonlinear characteristics of this method are stable.

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“…That is, while beam performance is the key factor in obtaining actual and accurate target information, generating the perfect beam depends on the system parameters of the antenna array and variable space. Previous studies have reported multicarrier approaches and non-linear frequency increment to decouple the joint-range-angle beampattern [17]. What we know about beampattern synthesis of FDA radar is largely based upon empirical studies that investigate how to decouple the joint-range-angle beam.…”

Section: Introductionmentioning

confidence: 99%

Highly focussed beampattern synthesis in FDA‐MIMO radar with multicarrier transmission

Geng

Cheng

et al. 2022

IET Radar Sonar & Navi

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Multicarrier technology is widely used for improving the performance of beampattern synthesis in frequency diverse array multiple-input and multiple-output (FDA-MIMO) Radar. However, how to use multicarrier technology to achieve highly focussed beampattern synthesis is still an open problem. To this end, the authors first establish the system model of FDA-MIMO radar with multicarrier transmission and analyse the parameters influencing beampattern synthesis by Cramér-Rao lower bound derivation. It can be recognised that frequency increments and multicarrier distribution are key parameters hindering highly focussed beampattern synthesis. On top of the investigation, a simple yet effective Multidimensional Non-linear Frequency Component, named multidimensional frequency components (MNFC), is proposed for forming a highly focussed beam. Specifically, MNFC is a two-dimension frequency component for FDA-MIMO radar. The first dimension is the frequency increment weighted by the Dynamic Taylor Window Function that effectively decouples the joint-range-angle beampattern. The second dimension is a non-linear multicarrier weighted by Log with a Linear Coefficient for further optimising dot-shaped beampatterns for target indication with high precision. Finally, numerical simulations and comparative experiments are performed to validate the superiority of the MNFC in beampattern synthesis.

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An Improved ESPRIT-Based Algorithm for Monostatic FDA-MIMO Radar With Linear or Nonlinear Frequency Increments

Cited by 14 publications

References 13 publications

Reweighted Off-Grid Sparse Spectrum Fitting for DOA Estimation in Sensor Array with Unknown Mutual Coupling

Reweighted Off-Grid Sparse Spectrum Fitting for DOA Estimation in Sensor Array with Unknown Mutual Coupling

Simulation and Characteristic Analysis of College Music Teaching Based on Nonlinear Equation

Highly focussed beampattern synthesis in FDA‐MIMO radar with multicarrier transmission

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