一种基于SBL和APIT的混合定位算法

A Hybrid Localization Algorithm Based on SBL and APIT

由于子区域分割粒度的限制,基于阶次序列的定位算法(Sequence-based localization,SBL)存在边缘区域节点定位误差较大和不能保证平均定位误差性能的问题。针对这些问题,提出了一种基于SBL和APIT的混合定位算法,利用APIT信标三角形切割SBL算法子区域,减小子区域面积,实现了分割粒度的细化。通过预先进行系统训练,优化了混合算法的加权系数,进一步提升了算法的定位精度。仿真结果表明,相比于原算法,所提出的混合算法有效地提升了边界区域节点的定位精度,其平均定位误差降低了17.9%,使基于阶次序列的定位算法的定位精度得到了有效改善。

Restricted by the partition granularity of sub-regions,the sequence-based localization algorithm（ SBL） has defects that the estimated location errors of for nodes on the edge of a region are greater than others and that the average location error is not optimal.To solve these problems,a hybrid localization algorithm based on SBL and APIT is proposed.The sub-regions of SBL are divided into smaller pieces by applying beacon triangles of APIT.Therefore,the area of sub-regions is cut and the partitioning granularity is finer.By pre-training,the weighting coefficient of the hybrid algorithm is optimized,which improves the location accuracy.Simulation results show that,compared with the original algorithm,a set of more accurate locations is obtained for nodes on the edge of the region with the proposed hybrid algorithm,and the average location errors are reduced by 17.9%.The location accuracy of sequence-based localization algorithm has been effectively improved.