基于Chan与改进麻雀搜索算法的协同定位算法

Co-Location Algorithm Based on Chan and Improved Sparrow Search Algorithm

针对基于测距的无线定位技术中位置解算算法精度不高、计算效率低的问题,提出一种基于Chan与改进麻雀搜索算法的协同定位算法。首先,将Chan算法运用于到达时间(TOA)定位模型估算位置初值;其次,采用SPM复合混沌映射初始化、黄金正弦策略、自适应权重因子、柯西-t扰动以及弹射边界处理改进麻雀搜索算法,有效提高算法的全局搜索能力和收敛精度;最后,在位置初值进行改进麻雀搜索算法迭代计算得到最终位置估计。仿真和实验结果表明,所提算法可提高无线定位精度和定位速度。

Aiming at the problem of low precision of position calculation algorithm and low calculation efficiency in ranging-based wireless positioning technology,a co-location algorithm based on Chan and improved sparrow search algorithm is proposed.The algorithm first applies the Chan algorithm to the time of arrival(TOA)positioning model to estimate the initial value of the position,and then uses the SPM composite chaotic map initialization,golden-sine strategy,adaptive weight factor,Cauchy-t disturbance and ejection boundary processing to improve the sparrow search algorithm,which effectively improves the global search ability and convergence accuracy of the algorithm.Finally,the ISSA algorithm iterative calculation is performed on the initial position value to obtain the final position estimate.The simulation and experimental results show that the algorithm improves the accuracy and speed of wireless positioning.