# Model Order Selection Rules For Covariance Structure Classification

## TL;DR

This paper develops adaptive rules for classifying covariance matrix structures in radar, using model order selection criteria to improve detection accuracy amid uncertainties.

## Contribution

It introduces a framework applying MOS techniques like AIC, TIC, and BIC for covariance structure classification in radar signal processing.

## Key findings

- Effective model selection rules demonstrated
- Comparison of MOS techniques discussed
- Improved classification accuracy shown

## Abstract

The adaptive classification of the interference covariance matrix structure for radar signal processing applications is addressed in this paper. This represents a key issue because many detection architectures are synthesized assuming a specific covariance structure which may not necessarily coincide with the actual one due to the joint action of the system and environment uncertainties. The considered classification problem is cast in terms of a multiple hypotheses test with some nested alternatives and the theory of Model Order Selection (MOS) is exploited to devise suitable decision rules. Several MOS techniques, such as the Akaike, Takeuchi, and Bayesian information criteria are adopted and the corresponding merits and drawbacks are discussed. At the analysis stage, illustrating examples for the probability of correct model selection are presented showing the effectiveness of the proposed rules.