21 research outputs found
On the Maximal Invariant Statistic for Adaptive Radar Detection in Partially-Homogeneous Disturbance with Persymmetric Covariance
This letter deals with the problem of adaptive signal detection in
partially-homogeneous and persymmetric Gaussian disturbance within the
framework of invariance theory. First, a suitable group of transformations
leaving the problem invariant is introduced and the Maximal Invariant Statistic
(MIS) is derived. Then, it is shown that the (Two-step) Generalized-Likelihood
Ratio test, Rao and Wald tests can be all expressed in terms of the MIS, thus
proving that they all ensure a Constant False-Alarm Rate (CFAR).Comment: submitted for journal publicatio
Exploiting persymmetry for low-rank Space Time Adaptive Processing
International audienceReducing the number of secondary data used to estimate the Covariance Matrix (CM) for Space Time Adaptive Processing (STAP) techniques is still an active research topic. Within this framework, the Low-Rank (LR) structure of the clutter is well-known and the corresponding LR STAP filters have been shown to exhibit a smaller Signal Interference plus Noise Ratio (SINR) loss than classical STAP filters, only 2r secondary data (where r is the clutter rank) instead of 2m (where m is the data size) are required to reach the classical 3 dB SNR loss. By using other features of the radar system, other properties of the CM can be exploited to further reduce the number of secondary data; this is the case for active systems using a symmetrically spaced linear array with constant pulse repetition interval, which results in a persymmetric structure of the noise CM. In this context, we propose to combine this property of the CM and the LR structure of the clutter to perform CM estimation. In this paper, the resulting STAP filter is shown, both theoretically and experimentally, to exhibit good performance with fewer secondary data; 3 dB SINR Loss is achieved with only r secondary data
Adaptive Radar Detection of a Subspace Signal Embedded in Subspace Structured plus Gaussian Interference Via Invariance
This paper deals with adaptive radar detection of a subspace signal competing
with two sources of interference. The former is Gaussian with unknown
covariance matrix and accounts for the joint presence of clutter plus thermal
noise. The latter is structured as a subspace signal and models coherent pulsed
jammers impinging on the radar antenna. The problem is solved via the Principle
of Invariance which is based on the identification of a suitable group of
transformations leaving the considered hypothesis testing problem invariant. A
maximal invariant statistic, which completely characterizes the class of
invariant decision rules and significantly compresses the original data domain,
as well as its statistical characterization are determined. Thus, the existence
of the optimum invariant detector is addressed together with the design of
practically implementable invariant decision rules. At the analysis stage, the
performance of some receivers belonging to the new invariant class is
established through the use of analytic expressions
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
Classification Schemes for the Radar Reference Window: Design and Comparisons
In this paper, we address the problem of classifying data within the radar
reference window in terms of statistical properties. Specifically, we partition
these data into statistically homogeneous subsets by identifying possible
clutter power variations with respect to the cells under test (accounting for
possible range-spread targets) and/or clutter edges. To this end, we consider
different situations of practical interest and formulate the classification
problem as multiple hypothesis tests comprising several models for the
operating scenario. Then, we solve the hypothesis testing problems by resorting
to suitable approximations of the model order selection rules due to the
intractable mathematics associated with the maximum likelihood estimation of
some parameters. Remarkably, the classification results provided by the
proposed architectures represent an advanced clutter map since, besides the
estimation of the clutter parameters, they contain a clustering of the range
bins in terms of homogeneous subsets. In fact, such information can drive the
conventional detectors towards more reliable estimates of the clutter
covariance matrix according to the position of the cells under test. The
performance analysis confirms that the conceived architectures represent a
viable means to recognize the scenario wherein the radar is operating at least
for the considered simulation parameters.Comment: Accepted by IEEE Transactions on Aerospace and Electronic System