Passive Localization of a Radiating Gaussian Subspace Signal
David Ramírez, Ignacio Santamarı́a, Louis L. Scharf · 2025
This work investigates the problem of passive source localization (PSL) using observations from two distributed sensors. The transmitted signal is assumed to lie in a known low-dimensional subspace, with its location in this subspace determined by a colored Gaussian random vector. This second-order model contrasts with the first-order model of [1], which assigns no distribution to the location of the signal in the known subspace. We derive the generalized likelihood ratio for this second-order model and compare its performance to the first-order model of [1] and the second-order model of [2], where the location vector is assumed to be a white Gaussian vector. Numerical experiments reveal that the proposed detector achieves superior performance for highly colored signals, as quantified by the spectral flatness of the eigenvalues of the signal covariance matrix. Performance degrades as the signal becomes increasingly white, aligning with theoretical expectations.