Subspace-based frequency estimation of sinusoidal signals in alpha-stable noise
Signal Processing - Signal processing with heavy-tailed models
Performance of RBF neural networks for array processing in impulsive noise environment
Digital Signal Processing
Estimation of the parameters of sinusoidal signals in non-Gaussian noise
IEEE Transactions on Signal Processing
Robust direction of arrival (DOA) estimation using RBF neural network in impulsive noise environment
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Subspace algorithm based on MSWF in the presence of impulsive noise
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
| Hi-index | 35.69 |
We propose several classes of fractional lower order moment (FLOM)-based matrices that can be used with MUSIC to estimate the DOAs of independent circular signals embedded in additive SαS (symmetric α stable) noise (e.g., sea clutter). We run simulations with different choices of the FLOM parameter p for our FLOM-based matrices and conclude that when the noise is SαS with unknown α≠2, FLOM-multiple signal classification (MUSIC) with p close to unity yields good performance. The performance of FLOM-MUSIC and robust covariation-based (ROC)-MUSIC are similar. Three scenarios that contain circular signals (phase modulation (PM), circularly symmetrical Gaussian, and quaternary phase-shift keying (QPSK)) and one scenario that contains noncircular signals (binary phase-shift keying (BPSK)), all embedded in the same SαS noise, are tested. These simulation results reveal that the scenario containing BPSK signals leads to poor performance, indicating that FLOM-MUSIC is presently limited to circular signals