Signal Subspace Processing Of Experimental Radio Data

Abstract

The research related to this paper was concerned with the application of EigenVector EigenValue ( EVEV ) signal processing techniques to experimental data. The signal subspace methods of Schmidt (called MUSIC), Johnson, and Pisarenko were considered and compared with results of conventional beamformers. Almost all oral and written papers regarding these EVEV processors involve theoretical studies, possibly using simulated data and incoherent noise, but not experimental data. Contrary to that trend, we have reported behavior of EVEV processors using experimental data in this and other papers. The data used here are predominantly due to an HF radio experiment, but the distribution of eigenvalues is also reported for acoustic data. The paper emphasizes two general subtopics of signal subspace processing. First, the eigenvalues of sampled covariance matrices are examined and related to those of incoherent noise. These results include actual data, all of which we found were not Gaussian incoherent noise. A new test related to the ratio of eigenvalues is developed. The MDL and AIC criteria give misleading results with actual noise. Second, directional responses of EVEV and conventional processors are compared using HF radio data that has high signal-to-noise ratio in the non-Gaussian noise. MUSIC is found to have very favorable directional characteristics.