Intelligent Signal Detection under Spatially Correlated Noise

Detecting the signal of the antenna array is a major problem in theoretical research and practical application. In this paper, several new methods are given for the number of signals detection at first, secondly, a new method called Principal Component Analysis of Signal Estimation (PCASE) will be introduced which can simultaneously detect the number of signals and the direction of arrival. In recent decades, the signal detection method based on the information theory criterion has been widely studied. The problem has been adequately solved under the assumption of uncorrelated white noise. However, considering the actual situation of wireless communication, the noise is spatially correlated or the noise information is unknown. In this case, traditional methods such as Akaike's information criteria (AIC), minimum descriptive length (MDL) and sparse and parameter approach (SPA) will often lead to a wrong estimation. Therefore, this paper introduces an improved eigenvalue correction method for the number of signals, and applies it to two new methods: The improved eigenvalue gradient method (Im-EGM) and the improved eigen-increment stop rule (Im-EISR), and studies a new estimation algorithm based on signal cancellation (SC). In addition, previous algorithms for estimating the direction of arrival (such as MUSIC, ESPRIT) require the number of known signals to estimate the direction of arrival. Therefore, this paper proposes a new method called PCASE, which can estimate the number of signals and the direction of arrival at the same time. This method combines the SPA and the Principal Component Analysis method (PCA) in machine learning. Compared with the existing methods, the accuracy of these new methods is verified by Monte Carlo simulation.