摘要
在非合作的认知无线网络节点定位技术中,目标节点的位置信息均通过对测量方程组求解而获得,求解算法的优劣将直接影响目标节点的定位精度.针对传统算法仅利用少数位置已知的节点对目标节点进行定位,从而导致节点定位精度不高的问题,论文提出了一种改进的多节点协同解算算法.该算法利用接收信号强度差RSSD测量值得到定位方程组后,首先通过泰勒级数多元变量展开法估计方程组中位置未知节点的坐标信息,从而增加参与定位的节点数量;然后通过共轭梯度与泰勒级数展开法相结合的方法协同估计目标节点的位置坐标.仿真测试了不同测量误差条件下的定位误差,结果表明,与传统的解算算法相比,论文提出的协同解算算法提高了对目标节点的定位精度,具有更好的定位性能.
In the cognitive wireless network node positioning technology of non-cooperation, It directly affect the accuracy of positioning that solving measurement equations for the position information of the target node. Traditional localization algorithm only using the known node to locate the target. It cannot be achieved more precise positioning for insufficient location information. According to this problem, the paper proposed an improved cooperative calculating algorithm. This algorithm adding the distances information between unknown nodes and known nodes by established Taylor series multivariable expansion model. Than it combined with conjugate gradient and Taylor series expansion method to cooperative estimate the location coordinates of the target node. This method increased number of nodes which participate in positioning,thus improve the accuracy of positioning. To evaluate the performance of this algorithm,simulations test the impact of different distance measurement error on positioning error. Simulation results shows that the improved algorithm has achieved better performance on positioning accuracy compared with Traditional calculating algorithra.
出处
《小型微型计算机系统》
CSCD
北大核心
2017年第10期2216-2220,共5页
Journal of Chinese Computer Systems
基金
国家自然科学基金项目(61273302)资助
关键词
协同解算算法
RSSD
泰勒级数多元变量展开
共轭梯度
cooperative calculating algorithm
RSSD
taylor series multivariable expansion
conjugate gradient
