836 research outputs found
A Unifying Framework for Adaptive Radar Detection in Homogeneous plus Structured Interference-Part II: Detectors Design
This paper deals with the problem of adaptive multidimensional/multichannel
signal detection in homogeneous Gaussian disturbance with unknown covariance
matrix and structured (unknown) deterministic interference. The aforementioned
problem extends the well-known Generalized Multivariate Analysis of Variance
(GMANOVA) tackled in the open literature. In a companion paper, we have
obtained the Maximal Invariant Statistic (MIS) for the problem under
consideration, as an enabling tool for the design of suitable detectors which
possess the Constant False-Alarm Rate (CFAR) property. Herein, we focus on the
development of several theoretically-founded detectors for the problem under
consideration. First, all the considered detectors are shown to be function of
the MIS, thus proving their CFARness property. Secondly, coincidence or
statistical equivalence among some of them in such a general signal model is
proved. Thirdly, strong connections to well-known simpler scenarios found in
adaptive detection literature are established. Finally, simulation results are
provided for a comparison of the proposed receivers.Comment: Submitted for journal publicatio
A novel approach to robust radar detection of range-spread targets
This paper proposes a novel approach to robust radar detection of
range-spread targets embedded in Gaussian noise with unknown covariance matrix.
The idea is to model the useful target echo in each range cell as the sum of a
coherent signal plus a random component that makes the signal-plus-noise
hypothesis more plausible in presence of mismatches. Moreover, an unknown power
of the random components, to be estimated from the observables, is inserted to
optimize the performance when the mismatch is absent. The generalized
likelihood ratio test (GLRT) for the problem at hand is considered. In
addition, a new parametric detector that encompasses the GLRT as a special case
is also introduced and assessed. The performance assessment shows the
effectiveness of the idea also in comparison to natural competitors.Comment: 28 pages, 8 figure
Model Order Selection Rules For Covariance Structure Classification
The adaptive classification of the interference covariance matrix structure
for radar signal processing applications is addressed in this paper. This
represents a key issue because many detection architectures are synthesized
assuming a specific covariance structure which may not necessarily coincide
with the actual one due to the joint action of the system and environment
uncertainties. The considered classification problem is cast in terms of a
multiple hypotheses test with some nested alternatives and the theory of Model
Order Selection (MOS) is exploited to devise suitable decision rules. Several
MOS techniques, such as the Akaike, Takeuchi, and Bayesian information criteria
are adopted and the corresponding merits and drawbacks are discussed. At the
analysis stage, illustrating examples for the probability of correct model
selection are presented showing the effectiveness of the proposed rules
On Time-Reversal Imaging by Statistical Testing
This letter is focused on the design and analysis of computational wideband
time-reversal imaging algorithms, designed to be adaptive with respect to the
noise levels pertaining to the frequencies being employed for scene probing.
These algorithms are based on the concept of cell-by-cell processing and are
obtained as theoretically-founded decision statistics for testing the
hypothesis of single-scatterer presence (absence) at a specific location. These
statistics are also validated in comparison with the maximal invariant
statistic for the proposed problem.Comment: Reduced form accepted in IEEE Signal Processing Letter
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