Fourth-Order Complex-Lag PWVD for Multicomponent Signals with Application in ISAR Imaging of Maneuvering Targets

Wang, Yong; Jiang, Yicheng

doi:10.1007/s00034-010-9154-z

Cited by 9 publications

(3 citation statements)

References 20 publications

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“…Step 2: Dechirp the cubic Chirplet atom to the form of (25), and estimate the chirp rateb k for y 1 (t) based on (34).…”

Section: Cubic Chirplet Decompositionmentioning

confidence: 99%

“…One kind of ISAR imaging algorithm based on the AM-FM model uses the time-frequency distributions (TFDs), where high resolution TFDs with reduced cross-term are used to substitute the Fourier transform in azimuth focusing. These algorithms include joint time-frequency analysis [21,22], short time Fourier transform [23], high order TFDs [24,25] and some improved version of Cohen's class distributions [26,27]. The other kind of ISAR imaging algorithm is based on adaptive Chirplet decomposition.…”

Section: Introductionmentioning

confidence: 99%

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Inverse synthetic aperture radar imaging of targets with complex motion based on cubic Chirplet decomposition

Wang

Zhao

Jiang

2015

IET signal process.

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High resolution inverse synthetic aperture radar (ISAR) imaging of targets with complex motion is a main topic in the radar imaging domain. In fact, the traditional range-Doppler algorithm is not appropriate to generate a focused ISAR images because of the time-varying Doppler shifts caused by the target's complex motion. In this study, the azimuth received signal is modelled as multi-component amplitude-modulated and frequency-modulated (AM-FM) signal, and a novel algorithm for the cubic Chirplet decomposition based on generalised cubic phase function is proposed to investigate the AM-FM signal analytically. Then, the corresponding ISAR imaging algorithm associated with the rangeinstantaneous-Doppler technique is proposed. Results of simulated and real data demonstrate the effectiveness of the presented algorithm.

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“…Step 2: Dechirp the cubic Chirplet atom to the form of (25), and estimate the chirp rateb k for y 1 (t) based on (34).…”

Section: Cubic Chirplet Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inverse synthetic aperture radar imaging of targets with complex motion based on cubic Chirplet decomposition

Wang

Zhao

Jiang

2015

IET signal process.

Self Cite

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“…derivatives of sine/cosine signal modulations caused by rotating target parts). Therefore, the complex‐time distributions (CTDs) [16–25] appear as an optimal choice for characterisation of fast‐varying data, providing high concentration along IF, and consequently, the exact IF tracking.…”

Section: Introductionmentioning

confidence: 99%

Sparse time–frequency representation for signals with fast varying instantaneous frequency

Orović

Draganic

Stanković

2015

IET Radar, Sonar & Navigation

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Time-frequency distributions have been used to provide high resolution representation in a large number of signal processing applications. However, high resolution and accurate instantaneous frequency (IF) estimation usually depend on the employed distribution and complexity of signal phase function. To ensure an efficient IF tracking for various types of signals, the class of complex time distributions has been developed. These distributions facilitate analysis in the cases when standard distributions cannot provide satisfactory results (e.g., for highly nonstationary signal phase). In that sense, an ambiguity based form of the forth order complex-time distribution is considered, in a new compressive sensing (CS) context. CS is an intensively growing approach in signal processing that allows efficient analysis and reconstruction of randomly undersampled signals. In this paper, the randomly chosen ambiguity domain coefficients serve as CS measurements. By exploiting sparsity in the time-frequency plane, it is possible to obtain highly concentrated IF using just small number of random coefficients from ambiguity domain. Moreover, in noisy signal case, this CS approach can be efficiently combined with the L-statistics producing robust time-frequency representations. Noisy coefficients are firstly removed using the L-statistics and then reconstructed by using CS algorithm. The theoretical considerations are illustrated using experimental results.

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An Approach to 2D Signals Recovering in Compressive Sensing Context

Stanković

Orović

2016

Circuits Syst Signal Process

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In this paper we study the compressive sensing effects on 2D signals exhibiting sparsity in 2D DFT domain. A simple algorithm for reconstruction of randomly under-sampled data is proposed. It is based on the analytically determined threshold that precisely separates signal and non-signal components in the 2D DFT domain. The algorithm operates fast in a single iteration providing the accurate signal reconstruction. In the situations that are not comprised by the analytic derivation and constrains, the algorithm is still efficient and need just a couple of iterations. The proposed solution shows promising results in ISAR imaging (simulated data are used), where the reconstruction is achieved even in the case when less than 10% of data is available.

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Fourth-Order Complex-Lag PWVD for Multicomponent Signals with Application in ISAR Imaging of Maneuvering Targets

Cited by 9 publications

References 20 publications

Inverse synthetic aperture radar imaging of targets with complex motion based on cubic Chirplet decomposition

Inverse synthetic aperture radar imaging of targets with complex motion based on cubic Chirplet decomposition

Sparse time–frequency representation for signals with fast varying instantaneous frequency

An Approach to 2D Signals Recovering in Compressive Sensing Context

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