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Existing pursuer-evader (PE) game algorithms do not provide good real-time solutions for situations with the following complexities: (1) multi-pursuer multi-evader, (2) multiple evaders with superior control resources such as higher speeds, and (3) jamming confrontation between pursuers and evaders. This paper introduces a real-time decentralized approach, in which decentralization strategy reduces computational complexity in multi-pursuer multievader situations, cooperative chasing strategy guarantees capture of some superior evaders, and min-max double-sided jamming confrontation provides optimal jamming-estimation strategies under adversarial noisy environments. Extensive simulations confirm the efficiency of this approach.

Enhancements of the encoding strategy of a discrete bidirectional associative memory (BAM) reported by B. Kosko (1987) are presented. There are two major concepts in this work: multiple training, which can be guaranteed to achieve recall of a single trained pair under suitable initial conditions of data, and dummy augmentation, which can be guaranteed to achieve recall of all trained pairs if attaching dummy data to the training pairs is allowable. In representative computer simulations, multiple training has been shown to lead to an improvement over the original Kosko strategy for recall of multiple pairs as well. A sufficient condition for a correlation matrix to make the energies of the training pairs be local minima is discussed. The use of multiple training and dummy augmentation concepts are illustrated, and theorems underlying the results are presented.

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