Spectral Peak Estimation for Signal DOA Using Spatial Smoothing Method

This document presents a method for spectral peak estimation of signal Direction of Arrival (DOA) using spatial smoothing technique. The spatial smoothing approach employs signal processing in the spatial domain to enhance direction estimation accuracy, particularly when signal peaks are not distinctly observable. This technique works by dividing the sensor array into overlapping subarrays and averaging their covariance matrices, which helps decorrelate coherent signals and improve spatial resolution. Implementation typically involves calculating the smoothed covariance matrix using array signal processing functions like `svd()` for singular value decomposition or `eig()` for eigenvalue analysis in MATLAB. In addition to spatial smoothing, several other DOA estimation methods exist, including Maximum Likelihood Estimation (MLE), Covariance Matrix Decomposition techniques, and Super-resolution Spectral Estimation methods. However, these approaches may encounter limitations under certain conditions such as beam deviation, multipath effects, and noise interference. The Maximum Likelihood method, for instance, can be computationally intensive when implemented with optimization functions like `fminsearch()` in MATLAB. Therefore, practical applications require careful selection of the most appropriate method based on specific scenario characteristics to achieve optimal DOA estimation results. Code implementation often involves trade-offs between computational complexity using functions like `fft()` for Fourier analysis and estimation accuracy through proper parameter tuning in array processing algorithms.