Effects of model errors on waveform estimation using the MUSIC algorithm

Sensor arrays are frequently used to separate and reconstruct superimposed signals arriving from different directions. The paper studies the effect of model errors, i.e., differences between the assumed and actual array response, on the quality of the reconstructed signals. Model errors are the limiting factor of array performance when the observation time is sufficiently long. The authors analyze a signal estimation technique which is based on the MUSIC algorithm. Formulas are derived for the signal-to-interference and signal-to-noise ratios as function of the model errors. By evaluating these formulas for selected test cases they gain some insights into the sensitivity of the signal estimation problem to model uncertainty