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.