Abstract

The multiple signal characterization (MUSIC) algorithm is an implementation of the signal subspace approach to compute parameter estimates of multiple point-source signals from the observed voltages received on an array of M antennas. In it, the solution to the multiple source direction finding (DF) problem is provided by the intersection of the signal subspace (obtained from the received data) and the array manifold (obtained via array calibration or prior knowledge of array directional characteristics). The MUSIC algorithm was implemented to experimentally verify the performance of the signal subspace approach to DF under very general scenarios and conditions which are regarded as difficult to impossible in traditional systems. The results of those experiments are described herein. The experimental system consisted of an eight-element antenna array 13 wavelengths in diameter, an eight-channel receiver and digitizer, and a minicomputer with disk storage to process the digitized data. With ideal instrumentation, the MUSIC algorithm provides performance that, as the amount of data collected increases without limit, is asymptotically ideal. However, with finite precision and finite data collection, the performance of even an ideal system can be a sensitive function of source and scenario parameters. Tests demonstrated the resolution of three sources all within one beamwidth (5 \deg ), even when the closer two were spaced less than 0.2 beamwidths. Sources that were polarized differently could be resolved at closer spacings. Experimental DF accuracy was limited by the oncalibrated scattering of source energy from the test range support tower and from the ground. The measured direction of arrival of one source changed by less than 0.01 beamwidths as the other two sources were switched on and off in all combinations. In general, results indicated that all parameters of a source can be measured and the signal waveform can be recovered as well in the presence of other sources less than a beamwidth away.

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