Jet Engine Modulation (JEM) spectral characteristics can be used with deep learning neural networks to enhance Automatic Target Recognition (ATR) capability. The conventional approaches for ATR using JEM lines include the estimations of the blade count, frequency of rotation, symmetry of the spectral lines, delta spectrum from multiple compression stages, and other special features. These JEM features are being compared with baseline features stored in a database using nearest neighbor classification for a best match. The existing feature extraction logics are data driven and tuned to a limited data set. Therefore, we developed a JEM ATR with deep learning algorithm to identify the signal scattered returns from the engine structure in periodic modulation. The JEM ATR deep learning algorithm enables the optimization of rotating blades in jet engines modulation pattern by self-training through reinforcement learning as this is an incredible breakthrough for artificial intelligence. The JEM models include four high-fidelity targets with thirty epochs of deep learning optimizer runs. Initially, our JEM target recognition results in a confusion matrix to validate Model_A determined that Target 1, Target 2, and Target 3 are 100% primary targets over 400 training samples. Target 4 has a 21.3% chance of being false target over 400 training samples for Model_A. Subsequently, when the optimizer hyperparameters and other parameters are fine-tuned with more training and sampling sessions, the ATR accuracy increased to 100% for all four targets with Model_P. Our proposed method can drastically improve the accuracy of automatic target recognition capability for radar systems using JEM deep learning algorithms.
