# Statistical Analysis of the Performance of MDL Enumeration for Multiple-Missed Detection in Array Processing

## TL;DR

This paper improves the analysis of source detection in array processing by accurately predicting MDL criterion performance, especially when multiple sources are missed.

## Contribution

A novel method for evaluating MDL performance in multiple-missed detection scenarios using ratio distribution analysis.

## Key findings

- The proposed procedure accurately predicts MDL enumeration results through statistical eigenvalue analysis.
- The method effectively evaluates performance when multiple sources are underestimated.
- Simulation results confirm the superiority of the new approach over existing methods.

## Abstract

An accurate performance analysis on the MDL criterion for source enumeration in array processing is presented in this paper. The enumeration results of MDL can be predicted precisely by the proposed procedure via the statistical analysis of the sample eigenvalues, whose distributive properties are investigated with the consideration of their interactions. A novel approach is also developed for the performance evaluation when the source number is underestimated by a number greater than one, which is denoted as “multiple-missed detection”, and the probability of a specific underestimated source number can be estimated by ratio distribution analysis. Simulation results are included to demonstrate the superiority of the presented method over available results and confirm the ability of the proposed approach to perform multiple-missed detection analysis.