CAJ | 학술논문

为提高传感器节点自定位精度,满足不同网络规模应用的需要,提出了合成蒙特卡罗定位算法。算法采用混合采样箱方法提高采样成功率,在锚节点密度较低时,先利用多跳锚节点方法收集锚节点信息,然后再运用采样箱方法计算未知节点位置,解决了采样速度慢和锚节点密度低时的应用受限问题。仿真实验结果表明,此算法可以不受锚节点密度限制,且提高了定位精度,具有自适应性和实用性。
위제고전감기절점자정위정도,만족불동망락규모응용적수요,제출료합성몽특잡라정위산법。산법채용혼합채양상방법제고채양성공솔,재묘절점밀도교저시,선이용다도묘절점방법수집묘절점신식,연후재운용채양상방법계산미지절점위치,해결료채양속도만화묘절점밀도저시적응용수한문제。방진실험결과표명,차산법가이불수묘절점밀도한제,차제고료정위정도,구유자괄응성화실용성。
Synthesized Monte-Carlo Localization（SMCL） was proposed for improving the accuracy of sensor node self-localization and satisfying the applications need of varied network scale. The Mixture Monte-Carlo Box method was adopted to increase valid sampling rate. When the anchor node density low,Multi-hop anchor node method was utilized to collect anchor nodes information, and then to calculate the position of node with Monte-Carlo Box. SMCL solved slow sampling speed and the constraint of low anchor node density. The simulation results showed that SMCL might overcome the limit of anchor node density and improve the accuracy of node localization. It is self-adaptive and practical.