Array Signal Processing: Multidimensional Model Order Selection Criterion with Enhanced Identifiability

The multidimensional model order selection criterion with improved identifiability is a crucial tool in statistical modeling and data analysis. This code implementation enables users to evaluate and compare different statistical models through algorithmic parameter optimization, determining the optimal model order that best fits observed data. Key functions include likelihood computation, penalty term calculation for model complexity, and identifiability enhancement mechanisms that prevent overfitting. The implementation typically involves comparing information criteria such as AIC (Akaike Information Criterion) or BIC (Bayesian Information Criterion) across multiple dimensions, with modifications to improve model distinguishability in complex datasets. The algorithm may incorporate dimension-specific penalty adjustments and covariance matrix analysis for array signal processing applications. This leads to enhanced accuracy in statistical inference, improved prediction capabilities, and deeper insights into underlying data generation processes. The code architecture supports high customizability through modular design, allowing researchers to adapt threshold parameters, dimensionality settings, and optimization methods for specific applications. With its robust algorithmic foundation and versatility, this multidimensional model order selection tool serves as an invaluable asset for signal processing researchers and data analysts working with complex multidimensional datasets.