Abstract: Genetically adaptive neural network systems and methods provide environmentally adaptable classification algorithms for use, among other things, in multi-static active sonar classification. Classification training occurs in-situ with data acquired at the onset of data collection to improve the classification of sonar energy detections in difficult littoral environments. Accordingly, in-situ training sets are developed while the training process is supervised and refined. Candidate weights vectors evolve through genetic-based search procedures, and the fitness of candidate weight vectors is evaluated. Feature vectors of interest may be classified using multiple neural networks and statistical averaging techniques to provide accurate and reliable signal classification.

Abstract: A real-time signal processing engine robustly detects, localizes, tracks and classifies ground targets based on radar signals from a multistatic radar system. The system differentiates between different targets based on an optimized cost function, which can include the total returned normalized pulse energy. The local transmitters/receivers can communicate with each other via the transmitted radar signals.