Selection of optimal window length using STFT for quantitative SNR analysis of LFM signal

Yin, Qinye; Shen, Liran; Lu, Mingyu; Wang, Xiangyang; Liu, Zhi

doi:10.1109/jsee.2013.00004

Cited by 21 publications

(15 citation statements)

References 23 publications

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“…Therefore according to [33] its classical spectrogram calculated using the Gaussian window g(t) is described by…”

Section: Resultsmentioning

confidence: 99%

“…Therefore the selection of window parameters, especially the effective window width, is particularly important and determines the resolution of the spectrograms in the time-frequency plane [33,34]. However, the instantaneous frequency rate profile calculated for clearly separated components is weakly dependent on the width in the extensive range of window widths.…”

Section: Instantaneous Frequency Rate Profilementioning

confidence: 99%

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The instantaneous frequency rate spectrogram

Czarnecki

2016

Mechanical Systems and Signal Processing

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“…Therefore according to [33] its classical spectrogram calculated using the Gaussian window g(t) is described by…”

Section: Resultsmentioning

confidence: 99%

Section: Instantaneous Frequency Rate Profilementioning

confidence: 99%

The instantaneous frequency rate spectrogram

Czarnecki

2016

Mechanical Systems and Signal Processing

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“…It has been proved that the LFM signal produces optimal SNR using the matched filter for a special type of noise. The target cannot be detected effectively when the target echo is blurred by the strong noise or the interference [ 24 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. However, the target may be detected when it is away from a strong interference area, although strong noise or interference can significantly reduce the detection probability.…”

Section: Target Detection Methodsmentioning

confidence: 99%

Self-Adaption Matched Filter and Bi-Directional Difference Method for Moving Target Detection

Zhu

Chen

Pan

et al. 2018

Sensors

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In this paper, a self-adaption matched filter (SMF) and bi-directional difference techniques are proposed to detect a small moving target in urban environments. Firstly, the SMF technique is proposed to improve the signal-to-interference-noise ratio (SINR) by using the power factor. The properties of the transmitting signal, the target echoes and the interference and noise are considered during the power factor generation. The amplitude coherent accumulation technique that extracts the coherent amplitude information of echoes after being processed by the SMF, is used to improve the SINR based on multiple measurements. Finally, the bi-directional difference technique is proposed to distinguish the target echoes and the interference/noise. Simulations and experiments are conducted to validate and demonstrate that small moving targets can be detected with high probability using the proposed method in urban environments, even with just one measurement.

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“…Optimization of the TFR parameters for the representation of a single AM/FM component (possibly embedded in noise) was considered in [10,16,17]. The main aim of these approaches is usually to estimate the component's instantaneous frequency ν(t) = φ (t) (see [18][19][20] for an overview of related concepts and algorithms), which is reconstructed from the optimized TFR.…”

Section: "Monocomponent" Approachesmentioning

confidence: 99%

Linear and synchrosqueezed time–frequency representations revisited: Overview, standards of use, resolution, reconstruction, concentration, and algorithms

Iatsenko

McClintock

Stefanovska

2015

Digital Signal Processing

198

146

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Time-frequency representations (TFRs) of signals, such as the windowed Fourier transform (WFT), wavelet transform (WT) and their synchrosqueezed versions (SWFT, SWT), provide powerful analysis tools. Here we present a thorough review of these TFRs, summarizing all practically relevant aspects of their use, reconsidering some conventions and introducing new concepts and procedures to advance their applicability and value. Furthermore, a detailed numerical and theoretical study of three specific questions is provided, relevant to the application of these methods, namely: the effects of the window/wavelet parameters on the resultant TFR; the relative performance of different approaches for estimating parameters of the components present in the signal from its TFR; and the advantages/drawbacks of synchrosqueezing. In particular, we show that the higher concentration of the synchrosqueezed transforms does not seem to imply better resolution properties, so that the SWFT and SWT do not appear to provide any significant advantages over the original WFT and WT apart from a more visually appealing pictures. The algorithms and Matlab codes used in this work, e.g. those for calculating (S)WFT and (S)WT, are freely available for download.

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Selection of optimal window length using STFT for quantitative SNR analysis of LFM signal

Cited by 21 publications

References 23 publications

The instantaneous frequency rate spectrogram

The instantaneous frequency rate spectrogram

Self-Adaption Matched Filter and Bi-Directional Difference Method for Moving Target Detection

Linear and synchrosqueezed time–frequency representations revisited: Overview, standards of use, resolution, reconstruction, concentration, and algorithms

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