DOA Estimation Based on Maximum Likelihood Method

This text describes a Direction of Arrival (DOA) estimation algorithm based on maximum likelihood methodology, which differs from subspace-based DOA algorithms by requiring multi-dimensional search operations for computation. Although this algorithm involves greater computational load, it can achieve more accurate direction estimation. Implementation typically involves signal preprocessing steps to enhance estimation precision, such as noise covariance matrix estimation and signal normalization. The algorithm also requires model parameter adjustments to adapt to different signal environments and noise levels, including likelihood function formulation and optimization constraint settings. Consequently, practical application demands thorough experimentation and validation to determine feasibility and effectiveness, involving performance testing under various Signal-to-Noise Ratio (SNR) conditions and array configurations. Key implementation considerations include optimization algorithms for multidimensional search (e.g., gradient descent or genetic algorithms) and statistical hypothesis testing for model validation.