Radon transformation of time-frequency distributions for analysis of multicomponent signals

Wood, J.C.; Barry, D. T.

doi:10.1109/icassp.1992.226437

Cited by 36 publications

(45 citation statements)

References 11 publications

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“…MOVING TARGET IMAGERY BASED ON LVD As noted in (18), due to the target movement, additional APM can be observed as the last three phase terms in linear and quadratic modulations. Because of the APM, the moving target will be both displaced and defocused in the SAR image.…”

Section: Moving Target Spectral Signaturesmentioning

confidence: 99%

“…From (18), the moving target can be focused in range dimension by simply using an FT with respect to k r . Then, substituting k x = −k 0 vt n /R 0 and k 0 = 4π/λ, the azimuth phase modulated signal in the range-compressed data domain can be shown with the single variablet n as…”

Section: A Lfm Signal Modelmentioning

confidence: 99%

“…Although, for maneuvering target imaging, we can adopt the method introduced in [31] to compensate the cubic phase and further obtain a refocused target image based on the LVD RWT is with rotational angle searching step 0.1 • [18], and FrFT is with rotational order searching step 0.0002 [21].…”

Section: Maneuvering Target Imagingmentioning

confidence: 99%

“…In such a case, the APM should be in a multicomponent LFM form, and the WVD would be no longer useful due to its bilinearity causing cross-terms [18], [19]. To mitigate the cross-terms, Radon [18] and Hough [19] transforms are utilized to concentrate the target energy within a small area on a corresponding parametric plane. The Radon-Wigner transform (RWT) [18], Wigner-Hough transform (WHT) [19], and Radon-ambiguity transform [20] are three typical methods.…”

Section: Introductionmentioning

confidence: 99%

“…To mitigate the cross-terms, Radon [18] and Hough [19] transforms are utilized to concentrate the target energy within a small area on a corresponding parametric plane. The Radon-Wigner transform (RWT) [18], Wigner-Hough transform (WHT) [19], and Radon-ambiguity transform [20] are three typical methods. However, these methods are still bilinear and cannot be recognized as true representations for the physical attributes of the LFM signal, i.e., centroid frequency and chirp rate.…”

Section: Introductionmentioning

confidence: 99%

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Airborne SAR Moving Target Signatures and Imagery Based on LVD

Yang

Xing

et al. 2015

IEEE Trans. Geosci. Remote Sensing

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This paper presents a new ground moving target imaging (GMTIm) algorithm for airborne synthetic aperture radar (SAR) based on a novel time-frequency representation (TFR), Lv's distribution (LVD). We first analyze generic moving target signatures for a multichannel SAR and then derive the analytical spectrum of a point target moving at a constant velocity by a polar format algorithm for SAR image formation. SAR motion deviation from a predetermined flight track is considered to facilitate airborne SAR applications. LVD, as a recently developed TFR for the analysis of multicomponent linear-frequencymodulated signal, is adopted to represent the target kinematic spectrum in the Doppler centroid frequency and chirp rate domain. As a result, the proposed SAR-GMTIm algorithm is capable of imaging multiple moving targets even when they are located at the same range resolution cell. Some practical issues such as imaging maneuvering targets and small/weak targets are discussed to enhance the applicability of the proposed algorithm. Simulation results with isotropic point moving targets are presented to validate the effectiveness and superiority of the proposed algorithm. Raw data collected by an airborne multichannel SAR are also used to verify the performance improvement made by the proposed algorithm.Index Terms-Ground moving target imaging (GMTIm), Lv's distribution (LVD), synthetic aperture radar (SAR), timefrequency representation (TFR).

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Section: Moving Target Spectral Signaturesmentioning

confidence: 99%

Section: A Lfm Signal Modelmentioning

confidence: 99%

Section: Maneuvering Target Imagingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Airborne SAR Moving Target Signatures and Imagery Based on LVD

Yang

Xing

et al. 2015

IEEE Trans. Geosci. Remote Sensing

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Quantification of first heart sound frequency dynamics across the human chest wall

Wood

Barry

1994

Med. Biol. Eng. Comput.

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Power spectral analysis has attracted attention because of its potential for non-invasive cardiac diagnosis. However, time-frequency analysis of first heart sound frequency dynamics from canine epicardium has demonstrated that cardiac vibrations are fundamentally multi-component and non-stationary, questioning the validity of power spectral techniques. In this study, we employed time-frequency transforms to characterise first heart sound frequency dynamics from 27 sites across the human thorax. In contrast to the dynamics observed epicardially, the first heart sound frequency law was dominated by quasi-stationary and impulse-like components implying that the instantaneous power and the power spectrum contain most of the diagnostic information in the first heart sound.

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