计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2015年
4期
91-95
,共5页
无线传感器网络%蒙特卡罗%定位%采样优化%自适应
無線傳感器網絡%矇特卡囉%定位%採樣優化%自適應
무선전감기망락%몽특잡라%정위%채양우화%자괄응
wireless sensor networks%Monte Carlo%localization%samping optimization%adaptive
针对蒙特卡罗定位算法采样效率低和采样次数多等缺陷，在SOMCL算法的基础上提出一种基于自适应采样优化的定位算法LAASO。该算法采用锚盒子与预测区域进一步优化采样区域，通过采样区域的大小自适应确定样本数目，利用SOMCL算法中的曲线拟合对样本权值进行优化。仿真测试表明，当速度变化率为25 m/s，且最大速度小于60 m/s时，相比MCL算法和SOMCL算法，LAASO算法定位精度分别提高了40%和36%，采样次数分别降低为20%和31.5%，且更适应于高速运行环境。
針對矇特卡囉定位算法採樣效率低和採樣次數多等缺陷，在SOMCL算法的基礎上提齣一種基于自適應採樣優化的定位算法LAASO。該算法採用錨盒子與預測區域進一步優化採樣區域，通過採樣區域的大小自適應確定樣本數目，利用SOMCL算法中的麯線擬閤對樣本權值進行優化。倣真測試錶明，噹速度變化率為25 m/s，且最大速度小于60 m/s時，相比MCL算法和SOMCL算法，LAASO算法定位精度分彆提高瞭40%和36%，採樣次數分彆降低為20%和31.5%，且更適應于高速運行環境。
침대몽특잡라정위산법채양효솔저화채양차수다등결함，재SOMCL산법적기출상제출일충기우자괄응채양우화적정위산법LAASO。해산법채용묘합자여예측구역진일보우화채양구역，통과채양구역적대소자괄응학정양본수목，이용SOMCL산법중적곡선의합대양본권치진행우화。방진측시표명，당속도변화솔위25 m/s，차최대속도소우60 m/s시，상비MCL산법화SOMCL산법，LAASO산법정위정도분별제고료40%화36%，채양차수분별강저위20%화31.5%，차경괄응우고속운행배경。
Due to limitations of Monte Carlo localization algorithm, such as low sampling efficiency and big sampling number, LAASO algorithm is proposed based on adaptive sampling optimization. Anchor box and prediction area are used to further optimize sampling area. Sampling number is adaptive defined by sampling area. Curve fitting in SOMCL algorithm is take to optimize the weight of samples. Simulation test results show, in the condition that speed change ratio is 25 m/s and the maximum speed is less than 60 m/s, location accuracy of nodes is respectively increased by 40%and 36%than that of MCL and SOMCL, while sampling number is decreased by 20%and 31.5%compared with that of MCL and SOMCL. LAASO is more suitable for high operation environment.
