School of Automation & MOE key laboratory of Information Fusion Technology, Northwestern Polytechnical University, Xi'an 710072, China.
MOE key laboratory of Micro and Nano Systems for Aerospace, Northwestern Polytechnical University, Xi'an 710072, China.
Sensors (Basel). 2018 Apr 18;18(4):1255. doi: 10.3390/s18041255.
A biomimetic distributed infection-immunity model (BDIIM), inspired by the immune mechanism of an infected organism, is proposed in order to achieve a high-efficiency wake-up control strategy based on multi-sensor fusion for target tracking. The resultant BDIIM consists of six sub-processes reflecting the infection-immunity mechanism: occurrence probabilities of direct-infection (DI) and cross-infection (CI), immunity/immune-deficiency of DI and CI, pathogen amount of DI and CI, immune cell production, immune memory, and pathogen accumulation under immunity state. Furthermore, a corresponding relationship between the BDIIM and sensor wake-up control is established to form the collaborative wake-up method. Finally, joint surveillance and target tracking are formulated in the simulation, in which we show that the energy cost and position tracking error are reduced to 50.8% and 78.9%, respectively. Effectiveness of the proposed BDIIM algorithm is shown, and this model is expected to have a significant role in guiding the performance improvement of multi-sensor networks.
受感染生物的免疫机制启发，提出了一种仿生分布式感染-免疫模型（BDIIM），旨在实现基于多传感器融合的高效唤醒控制策略，以进行目标跟踪。所得的 BDIIM 由六个反映感染-免疫机制的子过程组成：直接感染（DI）和交叉感染（CI）的发生概率、DI 和 CI 的免疫/免疫缺陷、DI 和 CI 的病原体数量、免疫细胞产生、免疫记忆和免疫状态下的病原体积累。此外，还建立了 BDIIM 与传感器唤醒控制之间的对应关系，形成协同唤醒方法。最后，在仿真中制定了联合监视和目标跟踪，结果表明，能量消耗和位置跟踪误差分别降低到 50.8%和 78.9%。证明了所提出的 BDIIM 算法的有效性，该模型有望在指导多传感器网络性能提升方面发挥重要作用。
