Title :
Comparison of GLR and maximal invariant detectors under structured clutter covariance
Author :
Kim, Hyung Soo ; Hero, Alfred O.
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Michigan Univ., Ann Arbor, MI, USA
Abstract :
There has been considerable recent interest in applying maximal invariant (MI) hypothesis testing as an alternative to the generalized likelihood ratio (GLR) test. This interest has been motivated by several attractive theoretical properties of MI tests including: exact robustness to variation of nuisance parameters, finite-sample min-max optimality (in some cases), and distributional robustness. However, in the deep-hide target detection problem, there are regimes for which either of the MI and the GLR tests can outperform the other. We discuss conditions under which the MI tests can be expected to outperform the GLR tests in the context of a radar imaging and target detection application. We also show that the relative advantage of the MI tests is robust to boundary estimation errors
Keywords :
adaptive signal detection; covariance matrices; estimation theory; minimax techniques; radar clutter; radar detection; radar imaging; radar target recognition; radar theory; adaptive detection algorithms; automatic target recognition; boundary estimation errors; covariance matrix; deep-hide target detection; distributional robustness; finite-sample min-max optimality; generalized likelihood ratio test; invariance principle; maximal invariant hypothesis testing; nuisance parameter variation; radar imaging; radar target detection; structured clutter covariance; Clutter; Covariance matrix; Detectors; Estimation error; Object detection; Performance evaluation; Radar detection; Radar imaging; Robustness; Testing;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on
Conference_Location :
Salt Lake City, UT
Print_ISBN :
0-7803-7041-4
DOI :
10.1109/ICASSP.2001.940245