Adaptive censored greatest-of CFAR detection

Himonas, S.D.

doi:10.1049/ip-f-2.1992.0032

Cited by 20 publications

(9 citation statements)

References 9 publications

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“…Thus, it displays the noise level in better resolution. In this detector, large samples are separated, and only samples that are smaller than β are not segregated [25,26].…”

Section: Cfar Detector Modelingmentioning

confidence: 99%

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A novel method based on comparison using threshold scale for CFAR detectorsunder environments with conditions of electromagnetic interference

Daneshvar¹,

Parhizgar²,

Salehi³

2019

Turk J Elec Eng & Comp Sci

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Detection of a noisy signal is a complex process. Many radar systems are working in an environment where the signal processing parts cannot overcome the effects of interference sources due to their high power. These sources of conflict may completely erode the signal or may make a mistake in deciding. It may make the return of the echoes of the goals difficult. To solve this problem, the detector processor can use a new algorithm to estimate noise power and then can set the threshold in different positions of the cell under test. The proposed algorithm, by differentiating between homogeneous and interference environments in a multitarget structure, selects a set of reference cells that surround the cell under test to estimate the unknown noise/clutter and determine the effective threshold. Then, to evaluate the performance of cell averaging of constant false alarm rate (CA-CFAR), censored mean level detector CFAR (CMLD-CFAR), and excision CFAR (EX-CFAR) detectors, we compared threshold, false alarm, and detection probability in terms of different correlation coefficients. The values were obtained using simulation by MATLAB software. The simulation results show that the excision parameter, by adding to the window of the reference cells that surround the cell under test, reduces the effects of background noise on the received signal. We conclude from the proposed method that the hybrid detector not only has higher quality detection interactions in heterogeneous environments but also has relatively less computational complexity than CA-CFAR, CMLD-CFAR, and EX-CFAR detectors.

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“…Thus, it displays the noise level in better resolution. In this detector, large samples are separated, and only samples that are smaller than β are not segregated [25,26].…”

Section: Cfar Detector Modelingmentioning

confidence: 99%

“…In [24], Srinivasan introduced the ensemble-CFAR (E-CFAR) detector. The variable index-CFAR (VI-CFAR) was presented in [25,26]. The upgraded version of the detector, called IVI-CFAR, was analyzed in [27].…”

Section: Introductionmentioning

confidence: 99%

A novel method based on comparison using threshold scale for CFAR detectorsunder environments with conditions of electromagnetic interference

Daneshvar¹,

Parhizgar²,

Salehi³

2019

Turk J Elec Eng & Comp Sci

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“…Barkat has developed smallest‐of CFAR detector [7], and the variability index CFAR detector has been provided to adapt the detector to the more environments [8]. In addition, many hybrid CFAR detectors have been developed to improve the detection performance [9, 10]. Souad has proposed Weber–Haykin based on automatic censoring detection in Weibull background [11].…”

Section: Introductionmentioning

confidence: 99%

Ship detection using online update of clutter map based on fuzzy statistics and spatial property

Pan

Liao

Yang

et al. 2018

IET Radar, Sonar & Navigation

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“…The OS-CFAR detector ranks the reference cell data in ascending numerical order with the purpose to form a new sequence where the kth-order statistic is selected as the noise power. Many other CFAR detectors are employed under the non-homogeneous noise conditions, for example, the generalised censored mean level (GCML) detector and the adaptive censored greatest-of CFAR (ACGO-CFAR) detector discussed in [8,9], respectively. The GCML detector discards the data associated with interfering targets before definition of the noise power and detection threshold.…”

Section: Introductionmentioning

confidence: 99%

Definition of adaptive detection threshold under employment of the generalised detector in radar sensor systems

Shbat

Tuzlukov

2014

IET signal process.

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An adaptive detection threshold under employment of the generalised detector (GD) in radar sensor systems is defined. GD is constructed in accordance with the generalised approach to signal processing in noise. To define the GD adaptive threshold based on the observed noise samples, the authors apply an appropriate noise power estimation technique. This study deals with an adaptive GD detection threshold definition as a function of the estimated noise power. Under investigations, they use two noise power estimation procedures. The first is the sliding window technique with the reference cells. The second procedure is based on the adaptive noise power estimation. Comparative analysis of simulation results demonstrates superiority by detection performance in favour of GD implementation in comparison with the well-known constant false alarm rate (CFAR) detectors, namely, cell averaging CFAR and ordered statistics CFAR detectors.

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Adaptive censored greatest-of CFAR detection

Cited by 20 publications

References 9 publications

A novel method based on comparison using threshold scale for CFAR detectorsunder environments with conditions of electromagnetic interference

A novel method based on comparison using threshold scale for CFAR detectorsunder environments with conditions of electromagnetic interference

Ship detection using online update of clutter map based on fuzzy statistics and spatial property

Definition of adaptive detection threshold under employment of the generalised detector in radar sensor systems

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