Source localization in a multipath environment via beamspace cumulant-based neural processing

Tser Ya Dai*, Ta-Sung Lee

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


An application of the radial-basis function neural network (RBF NN) on the angle-of-arrival (AOA) estimation of a desired source in multipath environments is investigated. In conjunction with a set of judiciously constructed beamformers, the RBF NN are used to estimate the desired AOA within an angular sector of interest (ASOI). With a pilot signal emitted from each of the training AOA's within the ASOI, the RBF NN is trained with the higher-order statistics (HOS) estimated from the received array data. In principle, the RBF NN AOA estimator maps the complex HOS into the desired angle response as an function approximator. By matching the HOS to the center vectors associated with the hidden nodes and linearly combining the node values, an AOA estimate results. The efficacy of the proposed AOA estimator is confirmed by computer simulations.

Original languageEnglish
Article number479768
Pages (from-to)3611-3614
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
StatePublished - 9 May 1995
EventProceedings of the 1995 20th International Conference on Acoustics, Speech, and Signal Processing. Part 2 (of 5) - Detroit, MI, USA
Duration: 9 May 199512 May 1995

Fingerprint Dive into the research topics of 'Source localization in a multipath environment via beamspace cumulant-based neural processing'. Together they form a unique fingerprint.

Cite this