Effects of model errors on signal reconstruction using a sensor array

Sensor arrays are frequently used to separate and reconstruct superimposed signals arriving from different directions. The authors study 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. A signal estimation technique is analyzed which is based on the multiple signal classification (MUSIC) algorithm. Formulas are derived for the signal-to-interference and signal-to-noise ratios as a function of the model errors.<>