ABORT-like detectors: a Bayesian approach

Francesco Bandiera (1) , Olivier Besson (2) , Angelo Coluccia (1) , Giuseppe Ricci (1)

1 Università del Salento [Lecce]
2 DEOS - Département Electronique, Optronique et Signal

In this paper, we deal with the problem of adaptive radar detection of point-like targets in presence of noise with unknown spectral properties. As customary, we assume that a set of data sharing the same properties of the noise in the cell under test is available. To cope with a limited number of training data, a Bayesian framework is adopted at the design stage. In order to come up with detectors with good rejection capabilities, the possible presence of a fictitious signal under the null hypothesis is modeled probabilistically, as opposite to the conventional ABORT-like approach. Several detectors are devised for the problem at hand, with different complexities. The performance assessment, conducted by means of Monte Carlo simulations, reveals that a good trade-off between detection power and selectivity can be achieved, even assuming a limited number of training data.

Mots clés: Adaptive detection; Bayesian estimation; Orthogonal rejection

Domaines: Traitement du signal et de l'image [eess.SP]

Francesco Bandiera, Olivier Besson, Angelo Coluccia, Giuseppe Ricci. ABORT-like detectors: a Bayesian approach. IEEE Transactions on Signal Processing, 2015, 36 (19), pp.5274-5284. ⟨10.1109/TSP.2015.2451117⟩. ⟨hal-01925407⟩