Optimality in detecting targets with unknown location
Signal Processing
CFAR detection strategies for distributed targets under conic constraints
IEEE Transactions on Signal Processing
One-and two-stage tunable receivers
IEEE Transactions on Signal Processing
One-and two-stage tunable receivers
IEEE Transactions on Signal Processing
Adaptive detection and estimation in the presence of useful signal and interference mismatches
IEEE Transactions on Signal Processing
Adaptive detection in Gaussian interference with unknown covariance after reduction by invariance
IEEE Transactions on Signal Processing
A rao test with enhanced selectivity properties in homogeneous scenarios
IEEE Transactions on Signal Processing
On the invariance, coincidence, and statistical equivalence of the GLRT, rao test, and wald test
IEEE Transactions on Signal Processing
Parametric adaptive radar detector with enhanced mismatched signals rejection capabilities
EURASIP Journal on Advances in Signal Processing
Distributed model-invariant detection of unknown inputs in networked systems
Proceedings of the 2nd ACM international conference on High confidence networked systems
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We introduce a framework for exploring array detection problems in a reduced dimensional space by exploiting the theory of invariance in hypothesis testing. This involves calculating a low-dimensional basis set of functions called the maximal invariant, the statistics of which are often tractable to obtain, thereby making analysis feasible and facilitating the search for tests with some optimality property. Using this approach, we obtain a locally most powerful invariant test for the unstructured covariance case and show that all invariant tests can be expressed in terms of the previously published Kelly's generalized likelihood ratio (GLRT) and Robey's adaptive matched filter (AMF) test statistics. Applying this framework to structured covariance matrices, corresponding to stochastic interferers in a known subspace, for which the GLRT is unavailable, we obtain the maximal invariant and propose several new invariant detectors that are shown to perform as well or better than existing ad-hoc detectors. These invariant tests are unaffected by most nuisance parameters, hence the variation in the level of performance is sharply reduced. This framework facilitates the search for such tests even when the usual GLRT is unavailable