This study uses several different memory-based neural networks to discriminate radar targets based on their early-time, aspect-dependent response, and demonstrates that target discrimination can be accomplished in a high-noise environment with great reliability. The difficulty of locating the beginning response point in practice prompts the use of FFT frequency spectrum magnitudes as aspect process patterns since a time shift is implicated in the phase of the spectrum. The effects of analog data and bipolar data with different quantization levels on network performances are investigated. Especially promising is the Recurrent Correlation Accumulation Adaptive Memory-Generalized Inverse (RCAAM-GI) cascade neural network. This network uses a dynamic memory structure to accumulate the converging information and has a stability criterion to allow us to define the final stable state. It can be considered as a real-time adaptive learning network with contamination observability and flexible decision strategy. From the simulation results, the network demonstrates computation space efficiency, and high noise tolerance.
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