摘要
为了进一步提高无线传感器网络的定位精度,通过考虑未知传感器之间的距离信息,构建了多元变量Taylor级数展开的定位模型。在对该模型求解过程中,首先利用三边测距法得到未知传感器的初始位置,再采用加权最小二乘法计算其最优值作为未知传感器的估计位置。为评价该算法的性能,推导了定位结果的Cramer-Rao下界(CRLB)。仿真测试了不同距离测量误差和已知传感器数目对定位误差的影响,以及算法的累积分布函数(CDF)。仿真结果表明,该算法有效地提高了定位精度,且定位误差非常接近CRLB。
In order to further improve positioning accuracy in wireless sensor networks,this paper established a multivariable Taylor series expansion localization model by considering distances between unknown sensors. In the process of the model solution,firstly it used trilateral ranging method to get initial values of unknown nodes. Then,it calculated the optimal values as the estimated location of unknown nodes by the weighted least squares method. To evaluate the performance of this algorithm,this paper derived the Cramer-Rao lower bound（ CRLB） of positioning result. Simulations test the impact of different distance measurement error and the known sensors number on positioning error,and the cumulative distribution function of algorithms.Simulation results show that the proposed algorithm can effectively improve positioning accuracy,and the positioning error is very close to the CRLB.
出处
《计算机应用研究》
CSCD
北大核心
2016年第6期1853-1856,1881,共5页
Application Research of Computers
基金
国家自然科学基金面上项目(61473179)
山东省高校科技计划资助项目(J11LG24)
