Model-Aided Data Driven Adaptive Target Detection for Channel Matrix-Based Cognitive Sonar

Data driven-based approaches including deep neural networks (DNN) have shown promise in various fields. Such techniques tend to require significant training for good convergence. Model-based approaches, however, provide practical data efficient solutions often with insightful and intuitive interpretations. A hybrid approach that employs data driven techniques aided by knowledge from model-based approaches may help reduce required training and improve convergence rates. This work investigates the potential of deep learning techniques to detect radar targets while accelerating the learning process via use of expert/domain knowledge from model-based algorithms for channel matrix-based cognitive sonar/radar. The channel matrices characterize responses from target and clutter/reverberation. The architecture of the proposed DNN exploits the insights from the model-based generalized likelihood ratio test (GLRT) statistic presented in our previous work, and hence, the resulting DNN algorithm benefits from the merits of both the model-based and data-driven approaches. Our proposed DNN architecture utilizes the secondary data for clutter channel estimation via the maximum-likelihood approach, and thus, requires little to no retraining with the changing clutter environment. We compare the detection performance of model-aided deep learning-based algorithms with that of traditional model-based techniques and pure data-driven DNN approaches using receiver operating characteristic (ROC) curves from Monte Carlo simulations.