Survey of neural network technology for automatic target recognition

Area of Science:

• Computer Science
• Artificial Intelligence
• Robotics

Background:

• Automatic Target Recognition (ATR) systems have historically faced challenges in complex environments.
• Advancements in neural network technology offer potential solutions to improve ATR capabilities.
• Previous ATR efforts provide a foundation for exploring new computational approaches.

Purpose of the Study:

• To review current neural network technologies relevant to ATR.
• To identify key areas of neural network development that can significantly impact ATR.
• To discuss the integration of these technologies into future ATR systems.

Main Methods:

• Review of existing literature on ATR and neural networks.
• Analysis of neural network developments in collective computation, learning algorithms, expert systems, and neurocomputer hardware.
• Discussion of how these advancements address specific ATR issues and needs.

Main Results:

• Neural network technologies in collective computation, learning algorithms, expert systems, and neurocomputer hardware show significant promise for ATR.
• These technologies can provide crucial tools for developing improved ATR algorithms and computational hardware.
• Specific applications include early vision, feature extraction, higher vision, and adaptive learning for ATR.

Conclusions:

• Neural network advancements are poised to revolutionize automatic target recognition.
• The integration of collective computation, advanced learning, expert systems, and neurocomputer hardware is key to future ATR success.
• Continued research in these areas will drive the development of more robust and efficient ATR systems.