5 research outputs found
Signal subspace change detection in structured covariance matrices
International audienceTesting common properties between covariance matricesis a relevant approach in a plethora of applications. In thispaper, we derive a new statistical test in the context of structuredcovariance matrices. Specifically, we consider low rank signalcomponent plus white Gaussian noise structure. Our aim is totest the equality of the principal subspace, i.e., subspace spannedby the principal eigenvectors of a group of covariance matrices. Adecision statistic is derived using the generalized likelihood ratiotest. As the formulation of the proposed test implies a non-trivialoptimization problem, we derive an appropriate majorizationminimizationalgorithm. Finally, numerical simulations illustratethe properties of the newly proposed detector compared to thestate of the art
Adaptive Detection of Structured Signals in Low-Rank Interference
In this paper, we consider the problem of detecting the presence (or absence)
of an unknown but structured signal from the space-time outputs of an array
under strong, non-white interference. Our motivation is the detection of a
communication signal in jamming, where often the training portion is known but
the data portion is not. We assume that the measurements are corrupted by
additive white Gaussian noise of unknown variance and a few strong interferers,
whose number, powers, and array responses are unknown. We also assume the
desired signals array response is unknown. To address the detection problem, we
propose several GLRT-based detection schemes that employ a probabilistic signal
model and use the EM algorithm for likelihood maximization. Numerical
experiments are presented to assess the performance of the proposed schemes
Auto-regressive model based polarimetric adaptive detection scheme part I: Theoretical derivation and performance analysis
This paper deals with the problem of target detection in coherent radar systems exploiting polarimetric diversity. We resort to a parametric approach and we model the disturbance affecting the data as a multi-channel autoregressive (AR) process. Following this model, a new polarimetric adaptive detector is derived, which aims at improving the target detection capability while relaxing the requirements on the training data size and the computational burden with respect to existing solutions. A complete theoretical characterization of the asymptotic performance of the derived detector is provided, using two different target fluctuation models. The effectiveness of the proposed approach is shown against simulated data, in comparison with alternative existing solutions