复杂生物传感网络的节点优化定位模型仿真

Node Optimization Localization Model Simulation of Complex Biological Sensor Network

根据复杂生物传感器网络节点的定位和数据监测的需要,构建生物传感网络的模型,设计了基于强跟踪滤波的集中式扩维量化融合算法.该算法采用预加重方法补偿节点之间的系统功率衰减,通过动态跟踪信号功率的变化,得到传感器融合中心最终的节点定位状态信息矩阵;通过强跟踪滤波,提高抗干扰能力,实现传感网络节点定位模型改进.结果表明,采用该算法进行复杂生物传感网络节点定位,能有效提高节点定位的准确性,定位误差较小,定位时间较快,稳健性和抗干扰性较好.

According to the need of the positioning of complex biological sensor network node and data monitoring,a biological sensor network model is constructed,the fusion augmenting quantization algorithm is designed based on strong tracking filtering algorithm,the algorithm uses pre attenuation through the change tracking signal power dynamic,sensor fusion center is computed to get the final node localization state information matrix,with strong tracking filter,it can improve anti-interference ability,achieve node localization in sensor network model for improvement.Simulation results show that this algorithm is used for complex biological sensor network node location,it can effectively improve the accuracy of node positioning,positioning error is small,it has fast positioning time,the robustness and anti-interference performance is better.