Inverse radon transform–based micro-doppler analysis from a reduced set of observations

Stanković, Ljubiša; Daković, Miloš; Thayaparan, T.; Popovic-Bugarin, Vesna

doi:10.1109/taes.2014.140098

Cited by 65 publications

(34 citation statements)

References 41 publications

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“…to visualize the results, that is, for a high resolution presentation of the initial signal, eigenvectors and the resulting signal components. Note that w(n) denotes a window of length S w in (14) and (15).…”

Section: Decomposition Principlementioning

confidence: 99%

On the decomposition of multichannel nonstationary multicomponent signals

et al. 2020

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Section: Decomposition Principlementioning

confidence: 99%

On the decomposition of multichannel nonstationary multicomponent signals

et al. 2020

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“…Concerning the first category, linear FM (LFM) signals can be separated by Radon transform (RT), chirplet transform [20], Radon-Wigner (RW) transform [21,22] and Lv's distribution [23], while sinusoidally modulated signals can be separated by using the inverse Radon transform [24]. Recently, new solutions have been proposed for multivariate MCSs, i.e., for those signals whose measurement is available from several channels [25].…”

Section: Brief State Of the Artmentioning

confidence: 99%

Radon spectrogram-based approach for automatic IFs separation

Bruni

Tartaglione

Vitulano

2020

EURASIP J. Adv. Signal Process.

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The separation of overlapping components is a well-known and difficult problem in multicomponent signals analysis and it is shared by applications dealing with radar, biosonar, seismic, and audio signals. In order to estimate the instantaneous frequencies of a multicomponent signal, it is necessary to disentangle signal modes in a proper domain. Unfortunately, if signal modes supports overlap both in time and frequency, separation is only possible through a parametric approach whenever the signal class is a priori fixed. In this work, time-frequency analysis and Radon transform are jointly used for the unsupervised separation of modes of a generic frequency modulated signal in noisy environment. The proposed method takes advantage of the ability of the Radon transform of a proper time-frequency distribution in separating overlapping modes. It consists of a blind segmentation of signal components in Radon domain by means of a near-to-optimal threshold operation. The inversion of the Radon transform on each detected region allows us to isolate the instantaneous frequency curves of each single mode in the time-frequency domain. Experimental results performed on constant amplitudes chirp signals confirm the effectiveness of the proposed method, opening the way for its extension to more complex frequency modulated signals.

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“…There exist a variety of types of TFA methods, and they can be normally divided into two categories: the parametric TFA (PTFA) methods and the nonparametric TFA (NPTFA) methods. PTFA methods, such as polynomial [1,15], spline-kernelled chirplet transform (SCT) [16], and sinusoidal models [17], often involve the high-dimensional search of the IFs, which is very time consuming. Moreover, the predesigned parametric models may be only suitable for special applications.…”

Section: Related Workmentioning

confidence: 99%

Intrinsic Mode Chirp Multicomponent Decomposition with Kernel Sparse Learning for Overlapped Nonstationary Signals Involving Big Data

Sun

Miao

2018

Complexity

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We focus on the decomposition problem for nonstationary multicomponent signals involving Big Data. We propose the kernel sparse learning (KSL), developed for the T-F reassignment algorithm by the path penalty function, to decompose the instantaneous frequencies (IFs) ridges of the overlapped multicomponent from a time-frequency representation (TFR). The main objective of KSL is to minimize the error of the prediction process while minimizing the amount of training samples used and thus to cut the costs interrelated with the training sample collection. The IFs first extraction is decided using the framework of the intrinsic mode polynomial chirp transform (IMPCT), which obtains a brief local orthogonal TFR of signals. Then, the IFs curves of the multicomponent signal can be easily reconstructed by the T-F reassignment. After the IFs are extracted, component decomposition is performed through KSL. Finally, the performance of the method is compared when applied to several simulated micro-Doppler signals, which shows its effectiveness in various applications.

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Inverse radon transform–based micro-doppler analysis from a reduced set of observations

Cited by 65 publications

References 41 publications

On the decomposition of multichannel nonstationary multicomponent signals

On the decomposition of multichannel nonstationary multicomponent signals

Radon spectrogram-based approach for automatic IFs separation

Intrinsic Mode Chirp Multicomponent Decomposition with Kernel Sparse Learning for Overlapped Nonstationary Signals Involving Big Data

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