Model order estimators using optimal and suboptimal methods with numerical tuning

The performance of several methods to estimate the number of source signals impinging on a sensor array are compared using a traditional simulator and their performance for synthetic aperture radar tomography is discussed. All methods use a penalty term that increases with model order in order to prevent overestimation. We include both separate estimation of model selection and direction of arrival as well as joint estimation. We formulate a new penalty term, numerically tuned so that it gives optimal performance over our operating conditions, and compare this method as well. Simulation results show that the numerically tuned model selection criteria is optimal and that the typical methods do not do well for low snapshots. We also found that there is little sensitivity to SNR greater than 3 dB when the number of snapshots is high. We discuss some issues to applying the algorithms to data collected by the CReSIS radar depth sounder.