1 October 1990 Embedded knowledge-based system for automatic target recognition
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The development of a reliable Automatic Target Recognition (ATE) system is considered a very critical and challenging problem. Existing ATE Systems have inherent limitations in terms of recognition performance and the ability to learn and adapt. Artificial Intelligence Techniques have the potential to improve the performance of ATh Systems. In this paper, we presented a novel Knowledge-Engineering tool, termed, the Automatic Reasoning Process (ARP) , that can be used to automatically develop and maintain a Knowledge-Base (K-B) for the ATR Systems. In its learning mode, the ARP utilizes Learning samples to automatically develop the ATR K-B, which consists of minimum size sets of necessary and sufficient conditions for each target class. In its operational mode, the ARP infers the target class from sensor data using the ATh K-B System. The ARP also has the capability to reason under uncertainty, and can support both statistical and model-based approaches for ATR development. The capabilities of the ARP are compared and contrasted to those of another Knowledge-Engineering tool, termed, the Automatic Rule Induction (ARI) which is based on maximizing the mutual information. The AR? has been implemented in LISP on a VAX-GPX workstation.
© (1990) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
A. Omar Aboutalib, A. Omar Aboutalib, "Embedded knowledge-based system for automatic target recognition", Proc. SPIE 1306, Sensor Fusion III, (1 October 1990); doi: 10.1117/12.21625; https://doi.org/10.1117/12.21625


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