1 research outputs found
Unimodality-Constrained Matrix Factorization for Non-Parametric Source Localization
Herein, the problem of simultaneous localization of multiple sources given a
number of energy samples at different locations is examined. The strategies do
not require knowledge of the signal propagation models, nor do they exploit the
spatial signatures of the source. A non-parametric source localization
framework based on a matrix observation model is developed. It is shown that
the source location can be estimated by localizing the peaks of a pair of
location signature vectors extracted from the incomplete energy observation
matrix. A robust peak localization algorithm is developed and shown to decrease
the source localization mean squared error (MSE) faster than O(1/M^1.5) with M
samples, when there is no measurement noise. To extract the source signature
vectors from a matrix with mixed energy from multiple sources, a
unimodality-constrained matrix factorization (UMF) problem is formulated, and
two rotation techniques are developed to solve the UMF efficiently. Our
numerical experiments demonstrate that the proposed scheme achieves similar
performance as the kernel regression baseline using only 1/5 energy measurement
samples in detecting a single source, and the performance gain is more
significant in the cases of detecting multiple sources