Radar Pulse Signal Filtering Using Vertical Synchrosqueezing

Abratkiewicz, Karol; Samczyński, Piotr

doi:10.1109/radarconf2248738.2022.9764306

Cited by 1 publication

(2 citation statements)

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“…The transformation points are relocated in the last step, resulting in the VSS deőned in (3). In practice, it comes down to adding the product F h x (t, ω ′ )e jω ′ (t−t0) to the output array element with an address along the X-axis that corresponds to the thread X-dimension identiőer and along the Y-axis with an address that corresponds to the estimated angular frequency.…”

Section: Convolutional Approachmentioning

confidence: 99%

“…It involves squeezing the vertical axis of the TF representation towards the instantaneous frequency of the signal, thereby enhancing the visibility of its frequency components and allowing for more accurate identiőcation of transient features. This approach can be advantageous in analyzing non-stationary signals, which has made vertical synchrosqueezing a technique successfully applied in many areas, including radar systems [2,3], voice signal processing [4], seismic analysis [5], and engine vibration monitoring [6].…”

Section: Introductionmentioning

confidence: 99%

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Real-Time CUDA Implementation of High-Order Vertical Synchrosqueezing

Gambrych,

Abratkiewicz

2024

Preprint

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Fast and efficient implementations of Fourier’s analysis techniques for non-stationary signals have been desired recently, not only in civilian but also in military applications. The ability to work in a real-time regime with high dependability and reliability when wideband signals are processed is a challenging but highly desirable task (e.g. analysis of radar signals). This article delves into the real-time implementations of two variants of vertical synchrosqueezing (VSS). The ability to achieve real-time processing was enabled by leveraging Nvidia CUDA technology. Validation through real-life radar signal processing scenarios has confirmed the accuracy and efficiency of the proposed algorithms. To the authors’ knowledge, this is the first real-time implementation of VSS given in the literature.

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Section: Convolutional Approachmentioning

confidence: 99%