To cope with the problems of insufficient level of intelligence in attempting combat missions under tactical edge conditions, high command, and control delay, and difficulty in sharing data across domains, the joint intelligent tacti-cal edge cloud service model is proposed, the definition and formal description of joint intelligence are given, and the synergistic mechanism of the joint intel-ligent cloud service model is analyzed, to provide theoretical references for distributed combat at the tactical edge under intelligent conditions.
In the future, information-based warfare will gradually become swarm intelligence and unmanned, which puts forward higher requirements for battlefield environment situational awareness based on artificial intelligence. In this paper, based on the requirement of the intelligent system of unmanned combat application traction, in view of the limited information interaction and observation ability of Multi-agent Cooperative reconnaissance and perception mission for battlefield environment situation, analysis the current mainstream of Multi-agent Reinforcement Learning method, put forward a kind of Distributed Reinforcement Learning method of online knowledge migration, provide solutions for the problems of the topology changes dynamically of Coordination Graphs and corresponding extensibility, computational complexity problem caused by agent moving in the battlefield environment, effectively improve the collaborative perception reconnaissance search ability of intelligent unmanned cluster.
To achieve a high embedding capacity (EC) without any distortion in the directly decrypted result, a reversible data hiding in encrypted images (RDH-EI) scheme based on full bit-plane compression (FBPC) is proposed. FBPC is designed to vacate as much room as possible before image encryption. To enrich the adjacent redundancy within the most significant bit (MSB) planes, we use flip prediction on the first MSB and then replace the other MSB planes with the XOR result between that plane and its higher level one successively. Hilbert curve scanning is introduced to reduce the dimensionality of the planes to obtain eight highly redundant bit sequences. Huffman coding is then utilizing to compress the bit sequences. After FBPC, a compressed image is obtained. Stream cipher with self-feedback is adapted to encrypt the image. Additional data could be embedded into the reserved space of the encrypted domain. Compared with existing RDH-EI algorithms, the proposed scheme could have a higher EC with no distortion in the directly decrypted image due to the high compression performance of FBPC. Experimental results on the BOWS-2 and BOSSbass datasets demonstrate that the average EC can reach 3.56 and 3.71 bits per pixel, respectively.
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