SINGLE SENSOR DETECTION AND CLASSIFICATION OF MULTIPLE SOURCES BY HIGHER-ORDER SPECTRA

The authors consider the detection and classification of multiple non- Gaussian linear sources by superposition of their waveforms available from a single sensor whose measurements are possibly corrupted by addi tive Gaussian noise. It is shown that by using multiple frequency lags of the trispectrum of single sensor measurements, it is possible to f orm a trispectral matrix C that possesses the same structure as the ar ray covariance matrix of narrowband multisensor measurements. Conseque ntly, techniques that are applicable to narrowband array processing ca n be adapted for the analysis of single sensor data; the rank of C rev eals the number of sources, and a multiple signal characterisation (MU SIC)-like method can be used for source classification using a directo ry of candidate source spectra. Simulations are included to illustrate the proposed methods.