Temporal synchronization of MIMO wireless communication in the presence of interference
IEEE Transactions on Signal Processing
How to Compare Noisy Patches? Patch Similarity Beyond Gaussian Noise
International Journal of Computer Vision
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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This paper addresses a target detection problem in radar imaging for which the covariance matrix of unknown Gaussian clutter has block diagonal structure. This block diagonal structure is the consequence of a target lying along a boundary between two statistically independent clutter regions. Here, we design adaptive detection algorithms using both the generalized likelihood ratio (GLR) and the invariance principles. There has been considerable interest in applying invariant hypothesis testing as an alternative to the GLR test. This interest has been motivated by several attractive properties of invariant tests including: exact robustness to variation of nuisance parameters and possible finite-sample min-max optimality. However, in our deep-hide target detection problem, there are regimes for which neither the GLR nor the invariant tests uniformly outperforms the other. We discuss the relative advantages of GLR and invariance procedures in the context of this radar imaging and target detection application