基于神经网络的自组织传感器网络设计

多传感器目标跟踪是信息融合的一个重要研究内容。尽管已经有许多的融合算法，但目前对跟踪传感器的配置问题研究还很少，而这对于设计一个成功的UGS网络系统是必要的。本文设计了一种神经元阈值可调的自适应Hopfield网络，可以自组织地从整个网络中选取合适数目的传感器组成跟踪器，使整个系统的精度足够高，而使用的传感器数目尽可能少。仿真显示了算法的有效性。

Design of the Self-organizing Sensor Networks Based on Neural Networks

Multisensor target tracking is an important part of the sensor data fusion. Although a lot of fusion algorithms have been put forward, very few research is taken on the configuration of the sensor network, which is necessary for a successful UGS network system. We designed an adaptive Hopfield network with adjustable neural thresholds, which can select suitable sensors to form a target tracker. The system maintained high enough tracking precision with sensors as few as possible. Simulation results proved the effectiveness of the algorithm.