Range Resolution of Targets Using Automatic Detectors

Trunk, G. V.

doi:10.1109/taes.1978.308625

Cited by 236 publications

(99 citation statements)

References 2 publications

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“…Let's denote the product PS by W, then the density function of Kdistributed random variable W is ,lfLw)=-2 ._ 2 K,,-,(cw) , w >() (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14) where a is the shape parameter, c is the scale parameter and Kv(x) is the modified Bessel function of the second kind.…”

Section: Simulation Proceduresmentioning

confidence: 99%

“…So In simulation of detection performance, the clutter samples in the reference window are generated from I 2 and the test cell sample from 101". (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19) Then the signal to clutter power ratio, SCR, is given by…”

Section: Simulation Proceduresmentioning

confidence: 99%

“…In order to detect two closely spaced targets, Trunk [14] has proposed the selection of the smaller-of (SO) the sums in the leading and lagging windows for the estimation of noise power. He has shown by simulation that this scheme resolves two closely spaced targets very well.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent Use of CFAR Algorithms

Antonik¹,

Bowles²,

Capraro³

et al. 1993

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Section: Simulation Proceduresmentioning

confidence: 99%

Section: Simulation Proceduresmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Use of CFAR Algorithms

Antonik¹,

Bowles²,

Capraro³

et al. 1993

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“…The Cell Average CFAR (CA-CFAR) algorithm was first proposed by H.M. Finn and R.S. Johnson [11], followed by the presence of the Ordered Statistics CFAR (OS-CFAR) [12], Greatest Opt-CFAR (GO-CFAR) [13], Smallest Opt-CFAR (SO-CFAR) [14], etc. The CA-CFAR detector is the most commonly used and performs well in homogeneous clutter, but is not good for uneven background clutter or multiple targets.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Component Selection-Based Discriminative Model for Object Detection in High-Resolution SAR Imagery

Xiong

et al. 2018

IJGI

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This paper proposes an innovative Adaptive Component Selection-Based Discriminative Model (ACSDM) for object detection in high-resolution synthetic aperture radar (SAR) imagery. In order to explore the structural relationships between the target and the components, a multi-scale detector consisting of a root filter and several part filters is established, using Histogram of Oriented Gradient (HOG) features to describe the object from different resolutions. To make the detected components of practical significance, the size and anchor position of each component are determined through statistical methods. When training the root model and the corresponding part models, manual annotation is adopted to label the target in the training set. Besides, a penalty factor is introduced to compensate information loss in preprocessing. In the detection stage, the Small Area-Based Non-Maximum Suppression (SANMS) method is utilised for filtering out duplicate results. In the experiments, the aeroplanes in TerraSAR-X SAR images are detected by the ACSDM algorithm and different comparative methods. The results indicate that the proposed method has a lower false alarm rate and can detect the components accurately.

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“…In order to overcome the disadvantages of the CFA R detectors, the VI-CFA R detector was proposed by Smith and Varshney [3,4] . In this detector, the data in the reference slid ing window is used to compute two statistics VI and MR. Based on these statistics, two tests are performed in order to select the algorith ms (CA-CFA R, GO-CFA R [5] and SO-CFA R [6] ) to be used for the estimation of the clutter power in the test cell [7] . So the detector co mbines the advantages of the CA-CFAR, GO-CFA R and SO-CFAR algorithms.…”

Section: Introductionmentioning

confidence: 99%

An Improved VI-CFAR Detector Based on GOS

Zhao

2016

MATEC Web of Conferences

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Abstract. In combination with the advantages of CA-CFAR, GO-CFAR an d SO-CFAR algorithm, the VI-CFAR has strong adaptability both in homogeneous and non-homogeneous environment. However, if the interfering targets are present in both the halves of the reference sliding windo ws, the use of the windo w with the smallest mean is affected by them and therefore results in a performance degradation. In order to overcome the shortcoming, an improved VI-CFAR detector based on GOS (I VI-CFAR) is proposed in this paper. We introduce the IVI-CFAR detector and make performance simulation and analysis in homogenous and non-homogenous environment. In the homogeneous environment, the IVI-CFAR detector has some CFAR loss relative to the CA-CFAR detector. In the clutter edge environment, the IVI-CFAR detector keeps the good performance of the VI-CFAR detector. In multiple interfering targets environment, the IVI-CFAR detector performs robustly, which is similar to the OS-CFAR detector. In addition, the IVI-CFAR detector shortens the sample sorting time of the OS-CFARR detector.

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Range Resolution of Targets Using Automatic Detectors

Cited by 236 publications

References 2 publications

Intelligent Use of CFAR Algorithms

Intelligent Use of CFAR Algorithms

Adaptive Component Selection-Based Discriminative Model for Object Detection in High-Resolution SAR Imagery

An Improved VI-CFAR Detector Based on GOS

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