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Autonomous target recognition has been a long-standing problem with complementary techniques based on classic subspace-based methods and recent machine cognition techniques proposed across several decades. Despite some success in both approaches, robust detection and meaningful interpretation of target features stay an open challenge. The primary bottleneck is the entanglement of multi-dimensional spectral features, some of which are intrinsic to target composition and geometry, and others originate from environmental reverberation and background clutter. This challenge is further compounded by unpredictable non-linear overlap between target features, which may themselves morph as a function of experimental conditions such as ping angle, range, frequency, and other factors. Furthermore, there is a compelling need to represent target features in a compact representation that lends itself to meaningful physical interpretation by a domain expert as well as robust interface with popular machine learning architectures. In this light, we will provide a broad overview of our recent and continuing work with braid manifolds, a morphological construct based on topological continuity of target features, that bridges this gap between physical model-based target representation and autonomous machine-learnt representation of target features. Representative results based on experimental field data on different target sizes and experimental conditions will be presented. |
