Space-Time Adaptive Detection of Distributed Targets in Homogeneous Environment
E. Conte, A. DeMaio and
Dipartimento di Ingegneria Elettronica e delle Telecomunicazioni
UniversitÓ degli Studi di Napoli Federico II
Via Claudio, 21, I-80125 Napoli, Italy.
* UniversitÓ degli Studi di Lecce
Abstract We address the problem of detecting distributed targets (with unknown amplitudes) in Gaussian noise with unknown covariance matrix. Precisely, we assume that data are collected from n sensors and deal with the problem of detecting the presence of a target across H range cells. It is also assumed that a secondary data set is available and that each of such snapshots does not contain any useful target echo. All of data vectors possess one and the same covariance matrix.
Detectors based upon the Generalized Likelihood Ratio Test (GLRT) are designed and assessed. In particular, we derive a decision strategy which implements the GLRT over the entirety of data, referred to in the following as one-step GLRT detector, and receivers based upon a two-step procedure: for this case we first assume that the covariance matrix (or its structure only) is known and design the GLRT based upon the cells under test; then, in order to come up with completely adaptive structures we plug the sample covariance matrix, based upon secondary data, into the test in place of the unknown matrix.
Proposed detectors ensure the Constant False Alarm Rate Property with respect to the covariance matrix and achieve comparable performances. In particular, two-step detectors are to be preferred due to their simplified structures and to possible performance gains as H increases (for medium/high probabilities of detection). Finally, the comparison of the newly introduced detectors with a receiver assuming knowledge of the covariance matrix confirms their suitability for operating in real radar scenarios.
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