Adaptive Detection of Subpixel Targets With Hypothesis Dependent Background Power

Abstract

We design and assess an adaptive scheme to detect a subpixel target in a sequence of images in the presence of an additive correlated Gaussian background. The presence of the subpixel target decreases the background power that hence may be different under the null and alternative hypotheses. We use the generalized likelihood ratio test (GLRT) to adapt the recently proposed modified matched subspace detector (MMSD) to unknown background variances under the null and alternative hypotheses using the secondary and primary data, respectively. We derive a modified adaptive subspace detector (MASD) that is sensitive to both energy in the target subspace and reduced energy in the orthogonal subspace. We contrast it with the MMSD and the well-known adaptive cosine estimator (ACE). Numerical simulations attest to the validity of the theoretical analysis and show that the proposed detector performance outperforms the ACE, especially in the case of dark subpixel targets. The performance-degrading effects of limited secondary data are presented for the proposed detector.