GLRT subspace detection of multi-pixel targets with known and unknown spatial parameters in presence of signal-dependent background power

Golikov, Victor; Lebedeva, Olga; Blanco, Marco Antonio Rodríguez

doi:10.1016/j.compeleceng.2016.04.016

Cited by 5 publications

(7 citation statements)

References 15 publications

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“…The article [19] synthesizes the detection algorithm for this scenario. For simplicity, it is assumed that the signal reflected from the sea surface can be represented as a normal random uncorrelated process c m ∼ N 0, σ 2 c I and channel noise n m ∼ N 0, σ 2 n I , where σ 2 c > σ 2 n .…”

Section: Problem Statement and Detector Designmentioning

confidence: 99%

“…This algorithm was obtained at unknown object size and known spectral frequency range of the reflected signal. To describe the detection algorithm, the article [19] used the orthogonal (N × N)…”

Section: Problem Statement and Detector Designmentioning

confidence: 99%

“…Detection algorithms based on statistical methods are widely used in remote sensing systems where channel noise and random intense background environment are present [13][14][15][16][17][18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

“…Adaptive algorithms and algorithms that take into account the specific features of scenarios, based on the generalized likelihood ratio test (GLRT), were considered in [15,16,[21][22][23]. A modified adaptive subspace detector (MASD) was proposed for detecting faintly discernible objects during image processing [17][18][19][20]. The problem of detecting low-contrast objects on an agitated sea surface was proposed in [25].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we continued to study the possibility of the detection of small low contrast fluctuating objects on the agitated sea surface in the presence of channel noise. As shown in [19], the detection algorithm obtained by the GLRT method depends on a priori information about the parameters of the object. In contrast to [25], two algorithms obtained in [19] are considered here: the first (MSD) was obtained in the absence of a priori information about the object size, the second algorithm (MMSD) was obtained provided that the size of the sub-image is much smaller than the object size.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Multi-Pixel Low Contrast Object on a Real Sea Surface

et al. 2022

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Video images captured at long range often show low-contrast floating objects of interest on a sea surface. A comparative experimental study of the statistical characteristics of reflections from floating objects and from the agitated sea surface showed differences in the correlation and spectral characteristics of these reflections. The functioning of the recently proposed modified matched subspace detector (MMSD) is based on the separation of the observed data spectrum on two subspaces: relatively low and relatively high frequencies. In the literature, the MMSD performance has been evaluated in general and using only a sea model (i.e., additive Gaussian background clutter). This paper extends the performance evaluating methodology for low contrast object detection using only a real sea dataset. The methodology assumes an object of low contrast if the mean and variance of the object and the surrounding background are the same. The paper assumes that the energy spectrum of the object and the sea are different. The paper investigates a scenario in which an artificially created model of a floating object with specified statistical parameters is placed on the surface of a real sea image. The paper compares the efficiency of the classical matched subspace detector (MSD) and MMSD for detecting low-contrast objects on the sea surface. The article analyzes the dependence of the detection probability at a fixed false alarm probability on the difference between the statistical means and variances of a floating object and the surrounding sea.

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Section: Problem Statement and Detector Designmentioning

confidence: 99%

Section: Problem Statement and Detector Designmentioning

confidence: 99%