Automatic censoring CFAR detector based on ordered data variability for nonhomogeneous environments

Farrouki, Atef; Barkat, M.

doi:10.1049/ip-rsn:20045006

Cited by 101 publications

(53 citation statements)

References 11 publications

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“…In multiple target situation, besides CA-CFAR and OS-CFAR, we compare the detection performance of PDLT to the ACCA-ODV, which is an symbolic order-statistic based CFAR detector for multiple target situations [29]. For the ACCA-ODV, the value of P fc is set equal to 10 −2 and the value of (N, p) set equal to (24,16).…”

Section: Multiple Target Situations With Different Number Of Interfermentioning

confidence: 99%

“…The number of censoring point is preset in the aforementioned two detector. This means that explicit priori information about the background environment is required [28,29]. To overcome this problem, Himonas and Barkat [28] presented a generalized two-level censored mean level detector (GTL-CMLD) and automatic CMLD (ACMLD).…”

Section: Introductionmentioning

confidence: 99%

“…To overcome this problem, Himonas and Barkat [28] presented a generalized two-level censored mean level detector (GTL-CMLD) and automatic CMLD (ACMLD). The ACMLD and GTL-CMLD are cell-by-cell censoring procedure and suffer from excessive computational burden [29]. Then Farrouki and Barkat [29] introduced an ordered data variability (ODV) based automatic censored cell averaging (ACCA) CFAR detector.…”

Section: Introductionmentioning

confidence: 99%

“…The ACMLD and GTL-CMLD are cell-by-cell censoring procedure and suffer from excessive computational burden [29]. Then Farrouki and Barkat [29] introduced an ordered data variability (ODV) based automatic censored cell averaging (ACCA) CFAR detector. The ACCA-ODV detector exhibits a low detection loss in a homogeneous environment and performs with considerable robustness in the presence of interference targets.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Positive Definite Matrix Space Based Detector With Limited Training Samples for Multiple Target Situations

Wen¹,

Huang²,

Cui³

et al. 2017

PIER M

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Abstract-Multiple target situation is a typical situation of nonhomogeneous clutter environment, which can cause excessive target masking in radar signal detection system. In order to reduce target masking caused by multiple target situations, this paper proposes a new detection structure based on positive-definite matrix space and limited training samples. The proposed detection structure uses a positive-definite matrix to estimate the background power level. In addition, with limited training samples, the detection structure is used to resist the multiple target situations. The simulation results show that the proposed detection structure exhibits a better detection performance than that of the well-known CA-CFAR in homogeneous environment. The detector also performs robustly in multiple target situations, even though 10 interfering targets exist in a length of 24 samples of reference window. Furthermore, the measured results validate the performance of the proposed method.

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Section: Multiple Target Situations With Different Number Of Interfermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Positive Definite Matrix Space Based Detector With Limited Training Samples for Multiple Target Situations

Wen¹,

Huang²,

Cui³

et al. 2017

PIER M

View full text Add to dashboard Cite

show abstract

“…In this detector, the adaptive threshold is the arithmetic mean of its reference cells. Many CFAR algorithms are developed to meet different situations namely in a transition in the clutter power distribution [12,13] and the presence of interfering targets [5,6,8,14,15,21,22,24] which refer to targets present in the reference window cells. When the number of interfering targets exceeds the supposed one, the developed detectors exhibit a serious performance degradation.…”

Section: Problem Formulationmentioning

confidence: 99%

Automatic Threshold Selection in Os-Cfar Radar Detection Using Information Theoretic Criteria

Magaz¹,

Belouchrani²,

Hamadouche³

2011

PIER B

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Abstract-This paper proposes a new approach for efficiently determining the unwanted interfering samples in the reference window, for the ordered statistics constant false alarm rate detector, based on the application of the information theoretic criteria principle. The proposed processor termed as Forward Automatic Order Selection Ordered Statistics Detector (FAOSOSD) does not require any prior information about the number of interfering targets. The proposed design aims to improve the Ordered Statistics Constant False Alarm Rate detector performance under severe interference situations. The number of interfering targets is obtained by minimizing the information theoretic criteria. Simulation results that illustrate the performance of the proposed method versus the classical OS-CFAR, the AND-CFAR and the OR-CFAR detectors are presented and discussed.

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Acquisition of PN sequences using multilayer perceptron neural network adaptive processor for multiuser detection in spread‐spectrum communication systems

Benkrinah

Benslama

2017

Int J Numerical Modelling

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A non-coherent serial acquisition scheme for direct sequence spread spectrum communication systems is analyzed and discussed in this paper. The adaptive thresholding based on constant false alarm rate and multilayer perceptron neural network (MLP-NN) techniques are combined to improve the performance of code division multiple access systems. One of the most important problems in code acquisition of pseudo-noise sequences for multiuser detection is the presence of interferences caused by the multiple access technique and multipath replicas. To solve this problem, an MLP-NN is trained and adapted to work as a constant false alarm rate detector using the error back propagation gradient descendent algorithm.It is named MLP-NN adaptive processor. The performance of this proposed algorithm is presented using the serial search acquisition system, which is chosen because of its simple hardware implementation. The performance of the MLP-NN adaptive processor algorithm in homogeneous and non-homogenous environments for additive white Gaussian noise and Rayleigh fading channels is evaluated via computer simulations. The obtained results are compared to other serial acquisition schemes using the cell-averaging adaptive processor, the order statistics adaptive processor, and the automatic censoring adaptive processor algorithms.

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Automatic censoring CFAR detector based on ordered data variability for nonhomogeneous environments

Cited by 101 publications

References 11 publications

Positive Definite Matrix Space Based Detector With Limited Training Samples for Multiple Target Situations

Positive Definite Matrix Space Based Detector With Limited Training Samples for Multiple Target Situations

Automatic Threshold Selection in Os-Cfar Radar Detection Using Information Theoretic Criteria

Acquisition of PN sequences using multilayer perceptron neural network adaptive processor for multiuser detection in spread‐spectrum communication systems

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