Parameterized Resampling Time-Frequency Transform

Li, Tianqi; He, Qingbo; Peng, Z.K.

doi:10.1109/tsp.2022.3220027

Cited by 15 publications

(3 citation statements)

References 46 publications

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“…The generalized MTFD extends the noise power in the (t, f ) while focusing on the source signal power within the instantaneous bandwidth. By replacing the generalized correlation matrix of the signal with G yy (t, τ) and by defining ρ yy (t, f ), the generalized MTFD matrix can be employed to characterize the signal covariance matrix to make it adapt to many traditional second-order-based array processing methods [48].…”

Section: Generalized Mtfd Matrix Constructionmentioning

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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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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Section: Generalized Mtfd Matrix Constructionmentioning

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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show abstract

“…In such cases, time-frequency transform can be employed to estimate the joint distribution of signal in time-frequency domains. This representation helps to reveal the relationship between the time domain and frequency domain [46]. One commonly used time-frequency transform is STFT.…”

Section: Signal Modelmentioning

confidence: 99%

Self-Supervised Representation Learning for Quasi-Simultaneous Arrival Signal Identification Based on Reconnaissance Drones

Guo

Xiong

et al. 2023

Drones

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Reconnaissance unmanned aerial vehicles are specifically designed to estimate parameters and process intercepted signals for the purpose of identifying and locating radars. However, distinguishing quasi-simultaneous arrival signals (QSAS) has become increasingly challenging in complex electromagnetic environments. In order to address the problem, a framework for self-supervised deep representation learning is proposed. The framework consists of two phases: (1) pre-train an autoencoder. For learning the unlabeled QSAS representation, the ConvNeXt V2 is trained to extract features from masked time–frequency images and reconstruct the corresponding signal in both time and frequency domains; (2) transfer the learned knowledge. For downstream tasks, encoder layers are frozen, the linear layer is fine-tuned to classify QSAS under few-shot conditions. Experimental results demonstrate that the proposed algorithm can achieve an average recognition accuracy of over 81% with the signal-to-noise ratio in the range of −16 dB∼16 dB. Compared to existing CNN-based and Transformer-based neural networks, the proposed algorithm shortens the time of testing by about 11× and improves accuracy by up to 21.95%.

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“…Zhou et al [ 38 ] proposed an effective nonstationary signal analysis method based on GPTF and a multicomponent instantaneous frequency extraction method that was superior to traditional time–frequency analysis methods and could be applied to feature extraction of large rotating machinery for condition monitoring and fault diagnosis. Li et al [ 39 ] proposed a parameterized resampling time–frequency transformation method that effectively improved the time–frequency resolution of nonstationary multicomponent signals. Li et al [ 40 ] proposed the PDM method, which constructs a pseudo-time domain by setting the PDMF, thus realizing the order tracking of vibration signals generated by rotating mechanical parts, which solved the frequency distortion problem and achieved excellent anti-noise performance.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Parameterized Domain Mapping Method and Its Application in Wheel–Rail Coupled Fault Diagnosis for Rail Vehicles

Yang

Yao

et al. 2023

Sensors

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The rapid development of cities in recent years has increased the operational pressure of rail vehicles, and due to the characteristics of rail vehicles, including harsh operating environment, frequent starting and braking, resulting in rails and wheels being prone to rail corrugation, polygons, flat scars and other faults. These faults are coupled in actual operation, leading to the deterioration of the wheel–rail contact relationship and causing harm to driving safety. Hence, the accurate detection of wheel–rail coupled faults will improve the safety of rail vehicles’ operation. The dynamic modeling of rail vehicles is carried out to establish the character models of wheel–rail faults including rail corrugation, polygonization and flat scars to explore the coupling relationship and characteristics under variable speed conditions and to obtain the vertical acceleration of the axle box. An APDM time–frequency analysis method is proposed in this paper based on the PDMF adopting Rényi entropy as the evaluation index and employing a WOA to optimize the parameter set. The number of iterations of the WOA adopted in this paper is decreased by 26% and 23%, respectively, compared with PSO and SSA, which means that the WOA performs at faster convergence speed and with a more accurate Rényi entropy value. Additionally, TFR obtained using APDM realizes the localization and extraction of the coupled fault characteristics under rail vehicles’ variable speed working conditions with higher energy concentration and stronger noise resistance corresponding to prominent ability of fault diagnosis. Finally, the effectiveness of the proposed method is verified using simulation and experimental results that prove the engineering application value of the proposed method.

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Parameterized Resampling Time-Frequency Transform

Cited by 15 publications

References 46 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

Self-Supervised Representation Learning for Quasi-Simultaneous Arrival Signal Identification Based on Reconnaissance Drones

An Adaptive Parameterized Domain Mapping Method and Its Application in Wheel–Rail Coupled Fault Diagnosis for Rail Vehicles

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