Reduction of sidelobe and speckle artifacts in microwave imaging: the CLEAN technique

Tsao, Jeffrey; Steinberg, Bernhard

doi:10.1109/8.1144

Cited by 405 publications

(202 citation statements)

References 4 publications

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“…There are various methods for obtaining one-dimensional (1-D) scattering centers, including using FFTbased CLEAN [4,14,15], an EP-based method [1,5], and a model-based method such as Prony's method [16]. Model-based methods have high resolution characteristics, but are very sensitive to random noise and require an accurate estimation of the number of scattering centers present in the data, as determined by using some information-based criterion (typically with a large computational cost) [16].…”

Section: Feature Extraction Using Fft-based Cleanmentioning

confidence: 99%

Performance Enhancement of Target Recognition Using Feature Vector Fusion of Monostatic and Bistatic Radar

Lee¹,

Choi²,

Cho³

et al. 2014

PIER

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Abstract-This paper proposes a fusion technique of feature vectors that improves the performance of radar target recognition. The proposed method utilizes more information than simple monostatic or bistatic (single receiver) algorithms by combining extracted feature vectors from multiple (two or three) receivers. In order to verify the performance of the proposed method, we use the calculated monostatic and bistatic RCS of three full-scale aircraft and the measured monotatic and bistatic RCS of four scalemodel targets. The scattering centers are extracted using one-dimensional FFT-based CLEAN and then used as feature vectors for a neural network classifier. The results show that our method has better performance than algorithms that solely use monostatic or bistatic data.

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Section: Feature Extraction Using Fft-based Cleanmentioning

confidence: 99%

Performance Enhancement of Target Recognition Using Feature Vector Fusion of Monostatic and Bistatic Radar

Lee¹,

Choi²,

Cho³

et al. 2014

PIER

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“…Reduction of sidelobe energy can be performed by a weighting value for each baseline, but this reduces the effective aperture size and thus the angular resolution. There are a number of known methods from astronomy for deconvolution of dirty images of incoherent sources made with a dirty beam, such are CLEAN and MEM [3]- [5]. Modified techniques are also proposed for coherent distributed sources [6].…”

Section: System Performancementioning

confidence: 99%

Radar Interferometric Measurements With a Planar Patch Antenna Array

Nilsson

Bååth²

2007

IEEE Sensors J.

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Abstract-A planar patch antenna array has been made for radar interferometry. The antenna array consists of 32 rectangular patches on a ceramic loaded teflon substrate. The patches are individually coupled to the microwave electronics in two orthogonal circular polarizations. The radar interferometer is intended for topographic imaging in industrial environment, in this case, a blast furnace producing hot metal for the steel and metal industry. Ordinary imaging techniques with IR or visible radiation are not possible to use due to high temperatures, scattering from dust and particles. A model of blast furnace burden material surface was measured and detected.

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“…There are several existing algorithms that are partially related to the object signal detection, such as the Relax algorithm by Li etc [6], CLEAN technology by Sao etc. [7], FFT signal separation method by Gough [8] and FastICA algorithm by Hyvarinen etc [9][10][11]. Although these algorithms are partially related to the weak signal separation, their performances on passive communication system are still not sufficient for the practical applications.…”

Section: Introductionmentioning

confidence: 99%

Blind Separation of Weak Object Signals Against the Unknown Strong Jamming in Communication Systems

Zhu

Xie

et al. 2017

Wireless Pers Commun

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To obtain the mixed weak object signal against the super power signal (jamming) is still an challenging task in modern communication systems. In this paper, a novel framework is designed for weak object signal blind separation against the strong interference signal. To extract the strong interference signal,firstly, we separate the mixed signals with the optimized FastICA algorithm, then, an improved Interference Cancellation algorithm is proposed as reference signal based on the separated strong signal. Next, we separate the weak mixed signals by the improved FastICA algorithm again. Finally, we discuss the performance of the proposed method and verify the novel method based on several simulations. The experimental results demonstrate the effectiveness and robustness of the proposed method.

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Reduction of sidelobe and speckle artifacts in microwave imaging: the CLEAN technique

Cited by 405 publications

References 4 publications

Performance Enhancement of Target Recognition Using Feature Vector Fusion of Monostatic and Bistatic Radar

Performance Enhancement of Target Recognition Using Feature Vector Fusion of Monostatic and Bistatic Radar

Radar Interferometric Measurements With a Planar Patch Antenna Array

Blind Separation of Weak Object Signals Against the Unknown Strong Jamming in Communication Systems

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