摘要
针对集群无人机导航定位信号通信过程中,易混入实际随机噪声,而传统GM-CBMeMBer滤波算法处理会导致滤波器发散的问题,提出了一种用于集群无人机定位信号的自适应GM-CBMeMBer滤波算法。首先,构建对应的数学模型,通过观测模型和量测模型对信号进行跟踪、滤波。在此基础上,利用随机有限集和衰减因子实现对噪声的动态处理和进一步预测,结合预测值进行迭代更新,直到滤波过程结束。同时,引入高斯项的剪枝合并来提高滤波精度。实验结果表明,改进算法与传统算法相比较,在集群无人机定位航迹上的杂波点有所减少,总体平均误差降低了26.6%。同时,方法简单易行,便于工程实现。
In view that actual random noise is easy to be mixed into the navigation and positioning signals of clustering UAVs(unmanned aerial vehicle)during the signal communication,and traditional GM-CBMeMBer filtering algorithm would cause the filter to diverge,an adaptive GM-CBMeMBer filtering algorithm for clustering UAV positioning signals is proposed.Firstly,the signals are processed,and the corresponding mathematical model is established.Then,the signals are tracked and filtered by the observation model and the measurement model.On this basis,the dynamic processing and further prediction of noise are realized by using the random finite set and the attenuation factor,and the iterative prediction update is p erformed in combination with the predicted values until the filtering process ends.Meanwhile,the pruning combination of Gaussian terms is introduced to improve the filtering precision.Experimental results show that,compared with the traditional algorithm,the proposed algorithm not only reduces the number of clutter points on the trajectories,but also reduces the overall average error by 26.6%.Besides,the proposed algorithm is simple and easy to implement.
作者
黄鹤
郭璐
许哲
王会峰
孟芸
代亮
HUANG He;GUO Lu;XU Zhe;WANG Huifeng;MENG Yun;DAI Liang(Chang’an University,Xi’an 710064,China;Xi’an ASN Technology Group Company,Xi’an 710064 710075,China;UAV National Engineering Research Center,Xi’an 710072,China;The 20th Research Institute,Xi’an 710068,China)
出处
《中国惯性技术学报》
EI
CSCD
北大核心
2019年第4期492-498,共7页
Journal of Chinese Inertial Technology
基金
国家重点研发计划项目（2018YFB1600600）
国家自然科学基金（61701044）
装备预研领域基金（61403120105）
陕西省自然科学基础研究计划面上项目（2019JM-611）
陕西省创新人才推进计划-青年科技新星项目（2019KJXX-028）
陕西省交通运输厅科技项目（17-33T,17-16K）
陕西省博士后科学基金项目（2017BSHEDZZ40）
长安大学中央高校基本科研业务费专项资金项目（300102328204,300102329401,300102329502,300102329501）
