Two New Estimation Algorithms for Sensor Gain and Phase Errors Based on Different Data Models

Abstract

Gain and phase errors sensitivity is an issue common to all high-resolution direction-of-arrival estimators. In this paper, we first provide the conventional and improved data models. Second, based on the conventional data model, an algorithm called estimation algorithm for the conventional data model (EACDM) is proposed to estimate the gain and phase errors existing in the sensor array for the uniform linear array (ULA). Finally, based on the improved data model, another algorithm called estimation algorithm for the improved data model (EAIDM) is proposed to estimate the gain and phase errors for the ULA. Derivation of these two proposed algorithms (EACDM and EAIDM) is similar. Moreover, the two proposed algorithms perform independently of phase errors, namely, the estimation accuracy is not affected regardless of how large the phase errors are. Furthermore, the computational complexity of the two proposed algorithms is lower than the conventional estimation method. Computer simulations are shown to verify the efficacy of the two proposed algorithms.