Spatial time-frequency distribution of cross term-based direction-of-arrival estimation for weak non-stationary signal

Shao, Shuai; Liu, Aijun; Yu, Changjun; Yang, Hongjuan; Li, Yong; Li, Bo

doi:10.1186/s13638-019-1555-5

Cited by 8 publications

(5 citation statements)

References 23 publications

(27 reference statements)

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“…Because of the different economic development and urbanization in different regions in China, PM 2.5 pollution varies significantly in different regions. More importantly, different regions are not independent with each other, and some phenomena in one region are closely related to the same phenomena in other regions [42,43,44,45]. From here, it is necessary to take the spatial correlation and spatial spillover effects into account when analyzing the impact of PM 2.5 on health burden.…”

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Effects of PM2.5 on Health Burden: Evidence from China

Zeng

Zhang

2019

IJERPH

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By collecting the panel data of 29 regions in China from 2008 to 2017, this study used the spatial Durbin model (SDM) to explore the spatial effect of PM2.5 exposure on the health burden of residents. The most obvious findings to emerge from this study are that: health burden and PM2.5 exposure are not randomly distributed over different regions in China, but have obvious spatial correlation and spatial clustering characteristics. The maximum PM2.5 concentrations have a significant positive effect on outpatient expense and outpatient visits of residents in the current period, and the impact of PM2.5 pollution has a significant temporal lag effect on residents’ health burden. PM2.5 exposure has a spatial spillover effect on the health burden of residents, and the PM2.5 concentrations in the surrounding regions or geographically close regions have a positive influence on the health burden in the particular region. The impact of PM2.5 exposure is divided into the direct effect and the indirect effect (the spatial spillover effect), and the spatial spillover effect is greater than that of the direct effect. Therefore, we conclude that PM2.5 exposure has a spatial spillover effect and temporal lag effect on the health burden of residents, and strict regulatory policies are needed to mitigate the health burden caused by air pollution.

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Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Effects of PM2.5 on Health Burden: Evidence from China

Zeng

Zhang

2019

IJERPH

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“…First, we optimize the CKD kernel parameters (c, D, E) using the Bayesian optimization algorithm to minimize the cost function in Eq. (20). After that, we fix the CKD shape parameter to its optimal value, i.e.…”

Section: Tfd Performance Optimization and Evaluationmentioning

confidence: 99%

“…High performing TFDs are attained by filtering the WVD using a TF kernel that achieves the best trade-off between accuracy and resolution [1]. Various studies adopt TFDs to improve the performance of conventional signal processing methods in different areas such as biomedicine [18], [19], radar [20], [21] and telecommunication [22], [23]. Currently, the TFD performance is optimized and evaluated using TF measures such as ratio of norms [24], energy concentration [25], entropy [26], and application-specific criterion [27].…”

Section: Introductionmentioning

confidence: 99%

Design of an Optimal Piece-Wise Spline Wigner-Ville Distribution for TFD Performance Evaluation and Comparison

Al-Sa'D

Boashash

Gabbouj

2021

IEEE Trans. Signal Process.

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This paper proposes a new performance evaluation process for time-frequency distributions (TFD) by designing a reference optimal TFD and novel accuracy and resolution measures. The motivation comes from the need for a TFD performance evaluation method that is objective, capable of quantifying the TFD accuracy and resolution, can determine the performance difference among different TFDs, and suitable for signals with an arbitrary number of components, instantaneous frequency and amplitude. We formulate the proposed optimal TFD, namely the piece-wise spline Wigner-Ville distribution (PW-WVD), by decomposing a standard non-stationary signal model using piece-wise linear frequency modulated (LFM) basis and by exploiting the Wigner-Ville distribution optimality for LFM signals. We compare the designed PW-WVD to conventional optimal TFDs and show that the former is more suitable to serve as a reference for TFD performance evaluation. Using the PW-WVD we derive TFD accuracy and resolution measures, compare them to conventional approaches, and analyze their sensitivity to form a TFD selection criteria. We evaluate the accuracy and resolution of twelve different TFDs and develop precise TFD selection strategies with or without prior information on the signal parameters. Results indicate that the compact kernel distribution is the best performing TFD given no prior information on the signal parameters and different TFDs must be selected upon the availability of prior information.

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“…In recent years, weak signal detection has had a very wide range of demands in communication, radar, sonar, mechanical fault diagnosis, and other fields. In particular, with the emergence of scanning electron microscopes, high-performance detection imaging, optical fiber gas sensors, and other advanced technologies, the requirements for the sensitivity and accuracy of weak signal detection are also growing exponentially [1][2][3]. Many realworld signals, such as radar sonar signals, speech signals, nuclear magnetic resonance (NMR) spectroscopy, and vibration signals, are naturally modeled as a sum of exponential damped sinusoids.…”

Section: Introductionmentioning

confidence: 99%

A new selection method of singular values using correlation coefficients for detecting weak exponential damped sinusoidal signals under strong noise

Zhang

2024

Meas. Sci. Technol.

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Weak signal detection has attracted considerable attention in a wide range of research fields, especially the weak signal detection under strong noise is an urgent problem researches are concerned with. In this paper, a new criterion to select singular values using correlation coefficient is proposed for weak exponential damped sinusoidal signal detection, which has a wide variety of signal processing applications. The innovation of our method lies in selecting K most informative singular values rather than the most energetic singular values. The proposed method measures the similarity between component signals and useful signal through autocorrelation function and correlation coefficient, which can preserve more information of the original signal and be more suitable for weak signal detection scenarios under strong noise. The numerical experiments and analysis are performed to verify the efficiency and effectiveness of our method, which indicates the proposed method is superior to the singular value selection methods based on energy or simple difference principle for correlation coefficients. Compared with stochastic resonance methods suitable for weak signal detection under strong background noise, our proposed method also has significant advantages. Thus, the proposed method is beneficial to theoretical analysis and engineering applications.

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Spatial time-frequency distribution of cross term-based direction-of-arrival estimation for weak non-stationary signal

Cited by 8 publications

References 23 publications

Spatial-Temporal Effects of PM2.5 on Health Burden: Evidence from China

Spatial-Temporal Effects of PM2.5 on Health Burden: Evidence from China

Design of an Optimal Piece-Wise Spline Wigner-Ville Distribution for TFD Performance Evaluation and Comparison

A new selection method of singular values using correlation coefficients for detecting weak exponential damped sinusoidal signals under strong noise

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