MICL/EIL- An Effective Approach for Simultaneous Source Enumeration and ML Direction Finding

Abstract

Direction finding (DF) in the high-frequency (HF) band is challenging since the signal and noise environment can at best be modeled only nominally, yet the high resolution of model-based methods is typically needed. In our analytical and experimental investigation of HF/DF, we have developed a new bearing estimation method, MICL, that incorporates an identifiability constraint into the standard ML method. We have also developed a companion source enumeration method, EIL, based on estimated incremental likelihoods. We describe MICL/EIL and apply it to real HF field data, demonstrating its utility for significant, HF/DF improvements.