Graph and Projection Pursuits Approach for Time Frequency Analysis

Li, Bing C.; Martin, Lockheed

doi:10.1109/radarconf2147009.2021.9455233

Cited by 3 publications

(5 citation statements)

References 11 publications

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“…This is demonstrated in Figure 1. PCT has been used in instantaneous frequency analysis [11][12][13][14][15][16][17][18]. However, the PCT implementation involves high dimensional searches and is very expensive.…”

Section: Polynomial Chirplet Transform For Fmrf Signal Parameter Esti...mentioning

confidence: 99%

“…In Graph and projection pursuits approach [18] is recommended to group and filter ridge points. This approach not only groups separate ridge points, but also separate and group cross ridge points as shown in Figure 3.…”

Section: B Connected Ridge Graph Polynomial Fittingmentioning

confidence: 99%

“…Polynomial chirplet transform can also be used in solving the separation and classification issues of co-channel and co-duration emitters. It is demonstrated in [17] [18] that the polynomial chirplet transform approach can separate and process co-channel and co-duration emitters while traditional techniques are incapable of process them due to the overlapping in both time and spectrum space.…”

Section: Introductionmentioning

confidence: 99%

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Connected spectrogram graph fitting and random optimization combined time frequency analysis

2023

Radar Sensor Technology XXVII

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Because of its simple approximate representation to nonlinear time variant frequency modulations, polynomial chirplet has been used in radar, gravity analysis, electronic warfare and acoustic signal processing. However, due to its high dimension parameter spaces, direct polynomial chirplet transform has extremely high computational cost. In addition, the discrete implementation of polynomial chirplet transform causes a limited parameter estimation accuracy which may not satisfy the requirement in real applications. In this paper, by combining a connected spectrogram graph fitting with random optimization, we develop a new technique to address these computational cost and parameter estimation accuracy issues. We first convert a high dimensional polynomial chirplet transform into a low dimensional spectrogram implementation which significantly reduces computational cost. We then introduce interpolation and random optimization methods to improve the parameter estimation accuracy.

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Section: Polynomial Chirplet Transform For Fmrf Signal Parameter Esti...mentioning

confidence: 99%

Section: B Connected Ridge Graph Polynomial Fittingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Connected spectrogram graph fitting and random optimization combined time frequency analysis

2023

Radar Sensor Technology XXVII

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“…The scalogram calculated by ( 13) is further simplified by substituting the FMRF signal of ( 4) into (14),…”

Section: Define a Rectangle Window Functionmentioning

confidence: 99%

“…Unlike chirplet and polynomial chirplet transforms which need to perform transforms from time to high dimensional frequency and chirp spaces, the short-time Fourier transform approach creates spectrograms and only needs to perform time to frequency transforms. Using fast Fourier transforms, the short-time Fourier transform for a local window with size W only needs O WlogW ðÞ computations, which are much lower than utilizing chirplet or polynomial chirplet transform approaches [14,15]. Spectrograms are created by a fixed window size Fourier transform.…”

Section: Introductionmentioning

confidence: 99%

Time Frequency Analysis for Radio Frequency (RF) Signal Processing

Li¹

2022

Recent Advances in Wavelet Transforms and Their Applications

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In high-density radio frequency (RF) signal environments, receivers usually acquire signals from multiple sources. These RF signals may be co-channel and co-duration, which cause significant difficulties for processing them. Time-Frequency analysis combined with a projection pursuits graph approach provides an effective way to detect, separate, and classify these multiple source RF signals. Time-frequency analysis includes a spectrogram approach and a scalogram approach. The feature points on the instantaneous frequency function of a frequency modulation radio frequency (FMRF) signal can be extracted from either the spectrogram or scalogram of this FMRF signal. With the projection pursuits graph approach, these feature points are grouped into time-frequency functions to represent the multiple components for the separation, detection, and classification of this multisource FMRF signal.

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Demodulation and Chirp Spectrogram Combined Approach for Nonlinear Time Frequency Analysis

2024

2024 IEEE Radar Conference (RadarConf24)

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Graph and Projection Pursuits Approach for Time Frequency Analysis

Cited by 3 publications

References 11 publications

Connected spectrogram graph fitting and random optimization combined time frequency analysis

Connected spectrogram graph fitting and random optimization combined time frequency analysis

Time Frequency Analysis for Radio Frequency (RF) Signal Processing

Demodulation and Chirp Spectrogram Combined Approach for Nonlinear Time Frequency Analysis

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