MDL Algorithm for Source Enumeration Using Random Matrix Theory

According to the problem that the traditional information theoretic criteria has a performance decline in the case of large number of array sensors but relatively small number of sample snapshots, a novel MDL algorithm for source enumeration based on random matrix theory is proposed. The method combines some features on the distribution of eigenvalues in random matrix theory with the density of the observed data to construct a new MDL criterion based on the traditional MDL criterion framework, and then it is used for source enumeration. Simulation results and theoretical analysis show that it is consistent. Regardless of the number of array sensors, it has a high detection probability in the case of small sample snapshots number. As a result, it has a wide range of applications. Furthermore, it has a comparable computational cost compared with the traditional MDL method.