Statistical Performance Analysis of MDL Source Enumeration in Array Processing

TL;DR

This paper provides a more accurate performance analysis of the MDL source enumeration method in array processing, addressing discrepancies between theory and simulation, and demonstrating its robustness under different signal models.

Contribution

The paper introduces an improved analytical approach for the probability of missed detection in MDL source enumeration, aligning theory more closely with simulation results.

Findings

Proposed analysis outperforms existing theoretical models.

MDL performance is similar under deterministic and stochastic models.

Simulation confirms the accuracy of the new analysis.

Abstract

In this correspondence, we focus on the performance analysis of the widely-used minimum description length (MDL) source enumeration technique in array processing. Unfortunately, available theoretical analysis exhibit deviation from the simulation results. We present an accurate and insightful performance analysis for the probability of missed detection. We also show that the statistical performance of the MDL is approximately the same under both deterministic and stochastic signal models. Simulation results show the superiority of the proposed analysis over available results.