Two-set expected-likelihood GLRT technique for adaptive detection

We introduce a new generalized likelihood-ratio test (GLRT) framework for adaptive detection that differs from Kelly´s standard method (E.J. Kelly, 1986) in two main aspects. First, the separate functions of the primary and secondary data are respected, with a single set of interference estimates for both hypotheses being searched to optimize the detection performance. Second, instead of the traditional maximum likelihood (ML) principle, we propose to search for a set of estimates that generates statistically the same likelihood as the unknown true parameters. We present results for a typical example scenario that demonstrates considerable detection performance improvement.