CAJ | 학술논문

针对现有近似三角形内点测试（ APIT）算法在信标节点密集环境下定位精度不高、稀疏环境下覆盖率较低的问题，提出了一种混合型定位算法。该算法通过减小三角形内点测试（ PIT）时的三角形误判、选择优良的三角形，提高了信标节点密集环境下的定位精度。同时，该算法结合DV-Hop算法与两点定位法在稀疏环境下能计算出未知节点坐标的优点，提高了信标节点稀疏环境下的定位覆盖率。仿真分析表明：混合型算法有效地提高了信标节点密集环境下的定位精度和信标节点稀疏环境下的定位覆盖率。
침대현유근사삼각형내점측시（ APIT）산법재신표절점밀집배경하정위정도불고、희소배경하복개솔교저적문제，제출료일충혼합형정위산법。해산법통과감소삼각형내점측시（ PIT）시적삼각형오판、선택우량적삼각형，제고료신표절점밀집배경하적정위정도。동시，해산법결합DV-Hop산법여량점정위법재희소배경하능계산출미지절점좌표적우점，제고료신표절점희소배경하적정위복개솔。방진분석표명：혼합형산법유효지제고료신표절점밀집배경하적정위정도화신표절점희소배경하적정위복개솔。
To overcome the problems of the current APIT localization algorithm which has low localization precision under the intensive beacon nodes density environment and low localization coverage under sparse beacon nodes density environment,a mixed localization algorithm is proposed. Gy reducing error of triangle point test ( PIT)and selecting superior triangles,the localization precision is promoted under the intensive beacon nodes density. Furthermore,combination of DV-Hop and two-point localization method can calculate advantage of coordinates of unknown nodes in sparse beacon nodes density and improve localization coverage rate. According to the simulation,this mixed algorithm has effectively improved localization precision in intensive beacon nodes density and localization coverage rate in sparse beacon nodes density.