A Generalized Noise Reconstruction Approach for Robust DOA Estimation

Cong, Jingyu; Wang, Xianpeng; Lan, Xiang; Liu, Wei

doi:10.1109/trs.2023.3299184

Cited by 7 publications

(4 citation statements)

References 50 publications

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“…The GMTFD matrix ρ yy (t, f ) 1. The effective TF points of the GMTFD matrix are selected by Equation (20).…”

Section: Inputmentioning

confidence: 99%

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Spatial Parameter Identification for MIMO Systems in the Presence of Non-Gaussian Interference

Zhang,

Shi,

Chen

et al. 2024

Remote Sensing

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Reliable identification of spatial parameters for multiple-input multiple-output (MIMO) systems, such as the number of transmit antennas (NTA) and the direction of arrival (DOA), is a prerequisite for MIMO signal separation and detection. Most existing parameter estimation methods for MIMO systems only consider a single parameter in Gaussian noise. This paper develops a reliable identification scheme based on generalized multi-antenna time-frequency distribution (GMTFD) for MIMO systems with non-Gaussian interference and Gaussian noise. First, a new generalized correlation matrix is introduced to construct a generalized MTFD matrix. Then, the covariance matrix based on time-frequency distribution (CM-TF) is characterized by using the diagonal entries from the auto-source signal components and the non-diagonal entries from the cross-source signal components in the generalized MTFD matrix. Finally, by making use of the CM-TF, the Gerschgorin disk criterion is modified to estimate NTA, and the multiple signal classification (MUSIC) is exploited to estimate DOA for MIMO system. Simulation results indicate that the proposed scheme based on GMTFD has good robustness to non-Gaussian interference without prior information and that it can achieve high estimation accuracy and resolution at low and medium signal-to-noise ratios (SNRs).

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“…The GMTFD matrix ρ yy (t, f ) 1. The effective TF points of the GMTFD matrix are selected by Equation (20).…”

Section: Inputmentioning

confidence: 99%

“…Estimation of the NTA via the GMTFD matrixInput:The GMTFD matrix ρ yy (t, f ) 1. The effective TF points of the GMTFD matrix are selected by Equation(20). 2.…”

mentioning

confidence: 99%

Spatial Parameter Identification for MIMO Systems in the Presence of Non-Gaussian Interference

Zhang,

Shi,

Chen

et al. 2024

Remote Sensing

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“…Li et al 5 utilized a temporal attention model to create global guidance for fusing visual and motion features. Additionally, aiming at posture estimation and action recognition, these method in references [6][7][8][9][10][11] are proposed. To achieve action recognition, Li et al 12 sufficiently take account into hand motion features, gaze features, and object features.…”

Section: Introductionmentioning

confidence: 99%

Recognition to weightlifting postures using convolutional neural networks with evaluation mechanism

He,

Li,

Tang

et al. 2023

Measurement and Control

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For modern sport training, critical posture recognition of athletes can be helpful for athlete training. This paper proposes convolutional neural networks using a two-stage evaluation mechanism to recognize four critical postures of a weightlifter, that is, force releasing, knee flexion, knee extension and highest point. Using the proposed convolutional neural networks classify images and extract image features. Meanwhile, a two-stage evaluation mechanism is adopted to calculate the scores of image features, based on the calculated scores, the four critical postures can be accurately recognized. Experimental results show that the accuracy of our method is 92.85% in the recognition of the four critical postures, which defeats the competitive methods in critical posture recognition. Moreover, the training time of the proposed method linearly augments along with the increasing of data volume, that is, non-exponential growth, consequently, our method can be applied to large-scale image datasets. We demonstrate that the two-stage mechanism can calculate the scores of image features independently of specific scenarios, which assist neural networks improve classification capabilities. Moreover, using the two-stage mechanism can simplify the designed complexity of neural network architectures, thus reducing the training parameter of neural networks in the process of critical posture recognition.

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“…Its capability to capture real-time trajectories and mechanical behaviors of agricultural materials, facilitating in-depth exploration of material-machinery interactions, is instrumental in guiding optimal machinery design. Consequently, DEM is finding increased application in agricultural machinery research [6][7][8][9][10]. However, it is imperative to input accurate material physical and contact parameters when constructing a discrete element model.…”

Section: Introductionmentioning

confidence: 99%

Research on Cutting Angle Design Optimization of Rubber Cutter Based on Discrete Element Method

Zhang,

Wen,

Chen

et al. 2023

Agriculture

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This paper focuses on obtaining fundamental data for optimizing the design of intelligent equipment for cutting natural rubber and its key components. It uses natural rubber bark as the research subject and employs specific experimental apparatus to measure the physical properties and contact coefficients of the rubber bark. The discrete element method, along with the Hertz–Mindlin model featuring bonding contacts, are employed to create a discrete element model of natural rubber bark. Parameters are calibrated, and model validation is performed. Subsequently, a one-factor simulation test is conducted to assess various cutting angles of the rubber cutter knife. A secondary Fourier fitting is applied to fit the curve to the average shear force values obtained from the simulation. The results indicate that the lowest average shear force, at 84.345 N, occurs within the range of cutting angles between 25° and 30°. The corresponding optimal cutting angle is 29.294°, suggesting that cutting with low resistance can be achieved at this angle, leading to reduced power consumption. Following a statistical analysis of field rubber-cutting tests conducted in a forest setting, it was found that the average power consumption for rubber-cutting operations under the optimal cutting angle is 0.96 W·h. Additionally, the volume of rubber discharged in the initial 5 min period is 6.53 mL. These findings hold significant importance for guiding the optimization and enhancement of the design of intelligent equipment for cutting natural rubber and its key components.

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A Generalized Noise Reconstruction Approach for Robust DOA Estimation

Cited by 7 publications

References 50 publications

Spatial Parameter Identification for MIMO Systems in the Presence of Non-Gaussian Interference

Spatial Parameter Identification for MIMO Systems in the Presence of Non-Gaussian Interference

Recognition to weightlifting postures using convolutional neural networks with evaluation mechanism

Research on Cutting Angle Design Optimization of Rubber Cutter Based on Discrete Element Method

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