Study and analysis of a new detector of radar targets using CFAR with Gabor transform

Beklaouz, Hadj Larbi; Hamadouch, M.; Mimi, Malika; Ahmed, A. Taleb

doi:10.1109/iwssip.2008.4604460

Cited by 1 publication

(1 citation statement)

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“…TF representation [55][56][57][58] provides an effective way to analyze multi-component signals and has been applied in radar target detection [59][60][61]. Panagopoulos and Soraghan [62] presented a method for weak target detection by X-band radar using a set of three signal processing techniques (i.e., signal averaging, time-frequency representation, and morphological filtering) to suppress unwanted sea clutter radar echo.…”

Section: Introductionmentioning

confidence: 99%

Improving Ship Detection in Clutter-Edge and Multi-Target Scenarios for High-Frequency Radar

et al. 2021

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As one of the main sensors for continuous maritime measurements of sea state parameters, high-frequency surface wave radar (HFSWR) also plays an important role in ship detection and tracking. Compact HFSWR often suffers from missing targets, especially when the target appears near the Doppler region with heavy sea clutter or near another target in a multi-target scenario. To address this problem, an automatic ship detection method based on time–frequency (TF) analysis is presented in this paper. The TF target ridge areas are extracted in the TF image via the eigenvalues of the Hessian matrix, image edge detection, and local maximum search. Then, whether ship signals exist in the TF ridges or not is decided by a decision threshold that is calculated by fitting the probability distribution function (PDF) of sea clutter in the TF domain. The proposed TF method can separate TF ridges of similar Doppler frequency and performs constant false alarm rate (CFAR) detection for TF targets, which facilitates detecting these targets that are masked by sea clutter and other large targets. Experimental results show that the number of detected ships that match with the automatic identification system (AIS) records is four times more than that obtained by the conventional constant false alarm rate (CFAR) detectors and 1.3 times more than that by the state-of-the-art TF method in consideration of approximately the same number of detected targets.

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