Abstract
When a possible target is embedded in a low-rank (LR) Gaussian clutter (which is contained in a low-dimensional subspace) plus a white Gaussian noise, the detection process can be performed by applying the LR adaptive normalized matched filter (LR-ANMF), which is a function of the estimated projector. In a recent work, we derived an approximate distribution of the LR-ANMF under the $\mathcal {H}_0$ hypothesis by using a restrictive hypothesis (the target has to be orthogonal to the clutter subspace). In this paper, we propose to determine new approximations of the Pfa and the Pd of the LR-ANMF by relaxing this restrictive hypothesis. This new derivation is based on results concerning the convergence in a large dimension regime of quadratic forms. Simulations validate our result, in particular, when the tested signal is close to the clutter subspace.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: IEEE Transactions on Aerospace and Electronic Systems
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.