Summed area table for optimizing of processing time on CA CFAR algorithm

Miftahushudur, Tajul; Kurniawan, Dayat; Putra, Amj

doi:10.1109/icramet.2015.7380784

Cited by 3 publications

(1 citation statement)

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“…The S-CFAR algorithm chooses the CFAR reference cell using the sample size in the test cell. In [29], Meng recommended the use of a rating theorem to estimate the false alarm level of the S-CFAR.…”

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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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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Section: Introductionmentioning

confidence: 99%