CFAR integration processors in randomly arriving impulse interference

Himonas, S.D.

doi:10.1109/7.303750

Cited by 20 publications

(7 citation statements)

References 14 publications

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“…The occurrence of these disturbances, even with low probability of appearance, worsens detector performance. It is known that the application of different CFAR processors in homogeneous interference with unknown intensity and pulse jamming with known parameters improves detection performance [2,3,4,5]. The usage of these processors together with a Hough detector would improve the probability characteristics [7,8,9,10].…”

Section: Introductionmentioning

confidence: 99%

Performance of Hough Detectors in Presence of Randomly Arriving Impulse Interference

Doukovska

Kabakchiev

2006

2006 International Radar Symposium

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In the following paper are investigated several types of Hough detectors with CFAR processors in order to choose the most efficient one in the presence clatter and randomly arriving impulse interference environment. The Hough detector with fixed threshold is compared to other five types of Hough detectors. These are one and twodimensional Hough CFAR detectors -a Hough CA CFAR (Cell Average Constant False Alarm Rate) detector, a Hough EXC (Excision) CFAR detector, a Hough CFAR BI (Binary Integration) detector, a Hough EXC CFAR BI (Excision Constant False Alarm Rate with Binary Integration) detector and a Hough API (Adaptive censoring Post detection Integration) CFAR detector. The achieved results reveal that the Hough detector with the API CFAR algorithm is the most effective. The research work is performed in MATLAB computational environment.

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Section: Introductionmentioning

confidence: 99%

Performance of Hough Detectors in Presence of Randomly Arriving Impulse Interference

Doukovska

Kabakchiev

2006

2006 International Radar Symposium

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“…In the case of c = 1 (the exponential distribution), (16) approve the result obtained by [3]. The Pfa of the SO-CFAR with binary integration is given by (7).…”

Section: Analysis Of the So-cfar Detectormentioning

confidence: 87%

Performance analysis of mean level constant false alarm rate detectors with binary integration in Weibull background

Baadeche

Soltani

2015

IET Radar, Sonar & Navigation

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In this study, the authors analyse the performance of some constant false alarm rate (CFAR) detectors namely: the cell averaging-CFAR (CA-CFAR), greatest of CFAR (GO-CFAR), the smallest of CFAR (SO-CFAR), the smallest of OS and CA-CFAR (SOSCA-CFAR) and the OS and CA-CFAR greatest of (OSCAGO-CFAR) detectors in homogeneous and nonhomogeneous Weibull background with binary integration under the assumption of a known shape parameter. The nonhomogeneity is modelled by the presence of interfering targets and the presence of a clutter edge in the reference window. The authors derive close-form expressions of the probability of false alarm in the case of a homogeneous clutter environment. For a non-homogeneous environment, the performance of these detectors is investigated by means of Monte Carlo simulations. The obtained results showed that the best false alarm rate performance at clutter boundary is obtained for the OSCAGO and GO-CFAR detectors, whereas the SOSCA and SO-CFAR detectors present better performance for the interfering targets situation.

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“…Another approach for estimating the interference level, proposed by Himonas in [2], is used in this paper. Later, Himonas suggests this method to be used for censoring of randomly arriving interference impulses from the reference window, when the test cell contains no impulse interference [3]. Behar offers in [4] an adaptive censoring PI CFAR detector in the presence of Poisson distribution pulse jamming.…”

Section: Introductionmentioning

confidence: 99%

“…Behar offers in [4] an adaptive censoring PI CFAR detector in the presence of Poisson distribution pulse jamming. She considers more complex and more general situation than the one studied in [2,3], in which the two-dimensional reference window and the test cells are corrupted by randomly arriving impulse interference and the model of the appearance of pulse jamming is more general.…”

Section: Introductionmentioning

confidence: 99%

Systolic Architecture for Adaptive Censoring CFAR PI Detector

Garvanov

Kabakchiev

Daskalov³

2006

Large-Scale Scientific Computing

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A new parallel algorithm for signal processing and a parallel systolic architecture of a robust constant false alarm rate (CFAR) processor with post-detection integration and adaptive censoring (RACPI) is presented in the paper. This detector is effective in conditions of flow from strong impulse interference. The ACPI CFAR processor uses sorting and censoring algorithms. We offer the sorting algorithm to be realized on the basis of the odd-even transposition sort method. We propose the censoring algorithm to be used for obtaining of the noise level estimation and for estimation of the impulse interference parameters. These parameters are needed for automatically choosing the scale factor, which keeps the false alarm rate constant. The real-time implementation of this detection algorithm requires large computational resources because of the great volume and high speed of the incoming data. The time consumption of the sorting and censoring procedures is also very high and therefore the practical realization is difficult. For all these reasons, we choose systolic architectures in the considered case for being more effective than conventional multiprocessor architectures. The computational losses of the systolic architecture are estimated in terms of the number of the processor elements, the computational time and the speed-up needed for realtime implementation.

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CFAR integration processors in randomly arriving impulse interference

Cited by 20 publications

References 14 publications

Performance of Hough Detectors in Presence of Randomly Arriving Impulse Interference

Performance of Hough Detectors in Presence of Randomly Arriving Impulse Interference

Performance analysis of mean level constant false alarm rate detectors with binary integration in Weibull background

Systolic Architecture for Adaptive Censoring CFAR PI Detector

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