Netted radar Hough detector in randomly arriving impulse interference

Kabakchiev, Christo; Garvanov, Ivan; Rohling, Hermann

doi:10.1049/cp:20070610

Cited by 6 publications

(2 citation statements)

References 10 publications

(20 reference statements)

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“…In a series of three papers, Carlson et al [1] presented a detailed analysis of adapting the Hough Transform (HT) to the search radar detection and tracking. Followed by Carlson's work, Kabakchiev studied the detection and tracking performance of the joint detector composed of CFAR detector and HT in different clutter background [2][3][4]. Aimed at the problem of computational complexity, large storage and limited precision caused by the HT algorithm, Fan proposed an improved randomized Hough transform (RHT) to detect and track dim targets [5].…”

Section: Introductionmentioning

confidence: 99%

Coarse-To-Fine 3D Randomized Hough Transform for Dim Target Detection

2014

AMM

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This paper makes some improvements on Roberts representation for straight line in space and proposes a coarse-to-fine three-dimensional (3D) Randomized Hough Transform (RHT) for the detection of dim targets. Using range, bearing and elevation information of the received echoes, 3D RHT can detect constant velocity target in space. In addition, this paper applies a coarse-to-fine strategy to the 3D RHT, which aims to solve both the computational and memory complexity problems. The validity of the coarse-to-fine 3D RHT is verified by simulations. In comparison with the 2D case, which only uses the range-bearing information, the coarse-to-fine 3D RHT has a better practical value in dim target detection.

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Section: Introductionmentioning

confidence: 99%

Coarse-To-Fine 3D Randomized Hough Transform for Dim Target Detection

2014

AMM

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“…In Ordered Statistics CFAR (OS-CFAR) pulse detectors, the k-th ordered sample in the reference window is an estimate of the background level in the test resolution cell. The performance of such OS-CFAR detector in the I presence of multipath interference in existing communication networks is evaluated and studied in [3]. Hansen and Sawyers [4,5] proposed the Greatest-Of selection logic in the cell averaging constant false alarm rate (GO-CFAR) detector to control the increase in the false alarm probability.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of AND-CFAR and OR-CFAR Processors Under Different Clutter Models

Khalid Abd-Ali,

Abd-Ullah

2013

ETJ

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In this paper an evaluation the detection performances for (AND-CFAR) and (OR-CFAR) processors under different clutter models is done for pulsed radar system.The clutter models used in this paper are three types of distribution (Exponential Clutter distribution, Rayleigh Clutter distribution and Weibull Clutter distribution). The two detectors (AND-CFAR and OR-CFAR) are the improved conventional Cell Average-CFAR (CA-CFAR) and Order Statistics CFAR (OS-CFAR) by making full use of the cell information. The two CFAR processors combine the results of the CA-CFAR and OS-CFAR to get a better detection performance.The mathematical equations of the probability of detection and probability of false alarm to the two detectors (AND-CFAR and OR-CFAR) for the three clutter models are founded under the assumptions that a homogenous background with a Swerling I target, and the reference cells are Independent and Identically Distributed (IID), also the detection performances of these detectors (AND-CFAR and OR-CFAR) have been evaluated for the three clutter models first and compared between them with those of CA-CFAR and OS-CFAR for Rayleigh distribution using MATLAB-Programming (M-File).In this paper is founded that the detection performance to AND-CFAR and OR-CFAR has not been affected by changing clutter models (clutter probability density function) for fixed probability of false alarm, also for the same probability of detection (Pd=0.7) and for fixed probability of false alarm (Pfa=10 -6 ) and N=12, signal-to-ratio power ration (SNR) which ensure this Pd is equal (18dB) for AND-CFAR, but its equal (18.5dB) for OR-CFAR, this means AND-CFAR is better detection performance than OR-CFAR for different clutter models.

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