Adaptive direction detection of extended targets in noise plus unknown subspace interference

We address adaptive detection of extended radar targets embedded in homogeneous Gaussian noise plus subspace interference. We assume that the actual steering vector lies in a fixed and unknown direction of a preassigned and known subspace, while interfering signals are supposed to belong to an unknown subspace, with directions possibly varying from one resolution cell to another. The resulting detection problem is formulated in the framework of statistical hypothesis testing and solved using an ad hoc algorithm strongly related to the GLRT. The performance analysis, carried out also in comparison to natural benchmarks, shows that the proposed detector is an effective solution to the problem of detecting a one-dimensional signal embedded in noise and unknown subspace interference.