Adaptive detection of range-spread target in compound-Gaussian clutter without secondary data

Abstract : In this paper, we address the problem of detecting a range-spread target embedded in a non-Gaussian clutter with unknown covariance matrix and without using secondary data. We propose a new autoregressive method based on the generalized likelihood ratio test (GLRT) that requires only the cells under test. This method is used to derive two new detectors, corresponding to two different scenarios: a) when all range cells contain the target and share the same covariance matrix (homogeneous clutter), b) when different covariance matrices for different range cells are assumed (heterogeneous clutter). The proposed method is shown to outperform the state of the art on various scenarios in terms of false alarm probability and detection probability, especially in critical scenario as small data records or low number of secondary data. Finally, it exhibits the desired constant false alarm rate (CFAR) property.
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Abdelmalek Mennad, Arezki Younsi, Mohammed Nabil El Korso, Abdelhak M. Zoubir. Adaptive detection of range-spread target in compound-Gaussian clutter without secondary data. Digital Signal Processing, Elsevier, 2016, pp.90-98. 〈http://www.sciencedirect.com/science/article/pii/S1051200416301439〉. 〈10.1016/j.dsp.2016.09.002〉. 〈hal-01421514〉

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