A novel sampling methodology exploiting the least correlated-columns for angle of arrival estimation

Abstract

Estimating Angle of Arrival (AOA) parameter from the measured data has gained much attention, particularly in the 5G-and-beyond wireless communication networks. With these communication networks, it is highly expected that the size and number of the data sets are huge and much larger than the rival traditional networks. To this end, the way of sampling and dealing with collected data in the array signal processing stage will have a significant impact on the signal estimation parameters. Thus, this manuscript proposes a novel sampling methodology to extract the collected information optimally by exploiting the least correlated and dependent columns within the measured Covariance Matrix (CM). The suggested method, Least Correlated Column Sampling (LCCS), is then adopted to implement a robust AOA estimation algorithm called Least Correlated Column Projection Matrix (LCCPM). A theoretical analysis is derived and presented to justify that the proposed technique minimizes correlations between adjacent columns and then compared with current sampling methodologies in terms of the position selection and corresponding correlation. The analytical results show that the LCCS methodology selects the least dependent columns among the rival methods. This, in turn, improves the AOA algorithm performance and enables it to localize closely-separated sources with high accuracy. To verify the theoretical claims and reveal the advantages of the suggested approach, a numerical example is given and then intensive Monte Carlo simulations are conducted over a wide range of scenarios where the proposed method performance is systemically compared with existing techniques. The obtained results show that the proposed algorithm is superior and outperforms all the preceding methodologies in terms of higher estimation resolution, lower Root Mean Square Error (RMSE), and higher Probability of Successful Detections (PSDs). The results also illustrate that the proposed method achieves higher noise immunity, less sensitivity to correlated signals, and detects a higher number of angles in comparison with conventional and recently-developed methods.