Extraction of the pulse width and pulse repetition period of linear FM radar signal using time-frequency analysis

Ahmad, Ashraf Adamu; Lawan, Sagir; Ajiya, M.; Yusuf, Zainab Yunusa; Bello, Lawal

doi:10.37121/jase.v3i1.69

Cited by 8 publications

(11 citation statements)

References 19 publications

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“…We emphasize here that we did not consider coherent additional techniques based on timefrequency analysis (e.g., Refs. [15][16][17], since they were beyond the scope of this research.…”

Section: Introductionmentioning

confidence: 99%

Magnitude-based pulse width estimation via efficient edge detection

Ranney

Tom

Tadas

et al. 2022

J. Appl. Rem. Sens.

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In recent years, researchers have addressed the problem of using noncoherent approaches to estimate pulse width and pulse repetition interval. Since the measured transmitter is noncooperative, and noncoherent integration gain can be realized, the input signal-to-noise ratio (SNR) for these estimators becomes critical. We examine multiple edge detectors that exploit moving sums calculated as part of a Haar filtering of the received signal magnitudes. Two different ratio tests are considered in addition to the Haar filtering (or "difference of boxes") approach, and a binary hypothesis test is designed based on a "smallest of" constant false alarm rate formulation. Probability arguments are then invoked to derive readily evaluated expressions for the detection thresholds. Tests are conducted, indicating that performance of the ratio-based approaches is comparable in terms of processed peak-to-background ratio. However, comparisons of root mean-squared (RMS) error indicate that the difference-based (Haar) approach produces lower error than both ratio-based approaches. The Haar filter approach is further demonstrated to remain effective (100% detection, 0% false alarm, RMS estimation errors of <3%) at low SNRs of ∼0 dB.

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“…We emphasize here that we did not consider coherent additional techniques based on timefrequency analysis (e.g., Refs. [15][16][17], since they were beyond the scope of this research.…”

Section: Introductionmentioning

confidence: 99%

Magnitude-based pulse width estimation via efficient edge detection

Ranney

Tom

Tadas

et al. 2022

J. Appl. Rem. Sens.

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show abstract

“…For obtaining frequency features to achieve high accuracy in damage identification, time-frequency technologies have been widely applied in radar signal recognition (Ahmad et al, 2020), equipment fault detection (Ulloa and Barbieri, 2018), and nondestructive testing (Obuchowski et al, 2014;Dorafshan and Azari, 2020;Li et al, 2020). Gong et al (Gong et al, 2020) proposed an algorithm for the automatic extraction of the stress wave reflection period based on image processing to measure the different lengths of buried metal piles in soil.…”

Section: Introductionmentioning

confidence: 99%

A Novel Piezoceramic-Based Sensing Technology Combined With Visual Domain Networks for Timber Damage Quantification

et al. 2021

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Early detection of timber damage is essential for the safety of timber structures. In recent decades, wave-based approaches have shown great potential for structural damage assessment. Current damage assessment accuracy based on sensing signals in the time domain is highly affected by the varied boundary conditions and environmental factors in practical applications. In this research, a novel piezoceramic-based sensing technology combined with a visual domain network was developed to quantitatively evaluate timber damage conditions. Numerical and experimental studies reveal the stress wave propagation properties in different cases of timber crack depths. Through the spectrogram visualization process, all sensing signals in the time domain were transferred to images which contain both time and frequency features of signals collected from different crack conditions. A deep neural network (DNN) was adopted for image training, testing, and classification. The classification results show high efficiency and accuracy for identifying crack conditions for timber structures. The proposed technology can be further integrated with a fielding sensing system to provide real-time monitoring of timber damage in field applications.

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“…In principle, JTFA tracks the variations of a signal in time and frequency simultaneously and thereby constructs a two-dimensional (2D) time-frequency spectrum, which provides an overall and exhaustive description of the given signal. Thus, the JTFA techniques are superior to the conventional single-domain techniques in the anti-noise and positioning capabilities, which can facilitate the extraction of a weak signal from a strong noise environment and the decomposition of multiple components [20][21][22]. Some representative JTFA techniques, such as continuous wavelet transform (CWT), synchrosqueezing transform (SST), Wigner-Ville distribution (WVD), and fractional Fourier transform (FRFT), have been proposed for various applications, such as radar, acoustics, communication, and seismology [16,[23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Parametric Decomposition of Pulsed Lidar Signals with Noise Corruption Using FRFT Spectrum Analysis

Liu

et al. 2021

Remote Sensing

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The parametric decomposition of full-waveform Lidar data is challenging when faced with heavy noise scenarios. In this paper, we report a fractional Fourier transform (FRFT)-based approach for accurate parametric decomposition of pulsed Lidar signals with noise corruption. In comparison with other joint time-frequency analysis (JTFA) techniques, FRFT is found to present a one-dimensional Lidar signal by a particular two-dimensional spectrum, which can exhibit the mathematical distribution of the multiple components in Lidar signals even with a heavy noise interference. A FRFT spectrum-processing solution with histogram clustering and moving LSM fitting is designed to extract the amplitude, time offset, and pulse width contained in the mathematical distribution. Extensive experimental results demonstrate that the proposed FRFT spectrum analysis method can remarkably outperform the conventional Levenberg–Marquardt-based method. In particular, it can accurately decompose the amplitudes, time offsets, and pulse widths of the pulsed Lidar signal with a −10-dB signal-to-noise-ratio by mean deviation ratios of 4.885%, 0.531%, and 7.802%, respectively.

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Extraction of the pulse width and pulse repetition period of linear FM radar signal using time-frequency analysis

Cited by 8 publications

References 19 publications

Magnitude-based pulse width estimation via efficient edge detection

Magnitude-based pulse width estimation via efficient edge detection

A Novel Piezoceramic-Based Sensing Technology Combined With Visual Domain Networks for Timber Damage Quantification

Parametric Decomposition of Pulsed Lidar Signals with Noise Corruption Using FRFT Spectrum Analysis

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