A Novel Subspace-Averaging Direction of Arrival Estimation Technique

This paper presents a preliminary study of a novel subspace-averaging direction of arrival estimation technique (SADE), which jointly utilizes both the noise and signal subspaces to estimate the direction of arrival (DOAs) instead of just the former in a wide range of Signal to Noise (SNR) scenarios. The work was carried out by exploiting the common noise subspace properties with a modified covariance matrix. To further reduce the computational load, this work employs a simple polynomial root solving technique to determine the DOAs. From the simulation results of varying snapshot values and under Additive White Gaussian Noise (AWGN), the SADE technique manages to attain 99.84% of the Cramer Rao Bound (CRB) at >15dB SNR. In addition, from the simulation of varying antenna array elements, when the number of elements is less than 8, the SADE technique performs with a Root Mean Squared Error (RMSE) of 9.5% of the true direction compared to 15.6% and 16.7% of root-MUSIC and ESPRIT respectively with the potential application such as in a low-cost intelligent transportation localization system that requires high DOA estimation accuracy without the need for high hardware costs.