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Adaptive Radar Detection of Extended Gaussian Targets

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Conference paper

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We have addressed the derivation and the analysis of an adaptive decision scheme to detect possible extended targets modeled as Gaussian vectors known to belong to a given subspace noise returns from the cells under test are modeled as independent and identically-distributed Gaussian vectors with one and the same covariance matrix a set of secondary data free of signal components is also available secondary data are Gaussian-distributed and share the same covariance matrix of noise in the cells under test but for a possible different power level. The proposed detector relies on a two-step design procedure first we derive the GLRT assuming that the noise covariance matrix is known up to a scale factor then we come up with a fully adaptive detector by replacing the structure of the covariance matrix of the noise with the sample covariance matrix based upon the secondary data. The first step requires the maximum likelihood ML estimate of the covariance matrix of the useful signal under the signal-plus-noise hypothesis which in turn, has a known structure. That ML estimate has been firstly proposed by Bresler in 3 a different derivation is also proposed herein. The performance assessment is conducted resorting to the method proposed in 4-5 to model extended targets therein an exponential model for fully-polarized returns has been used assuming that each scattering center can be characterized by its relative range amplitude and polarization elipse.

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  • Statistics and Probability
  • Active and Passive Radar Detection and Equipment

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