集群无人机区域导航的定位信号特征提取模型  

Feature Extraction Model of Positioning Signal for Swarm UAV Area Navigation

作　　者：李彬 丁国斌 LI Bin;DING Guobin(Digital Grid Research Institute,China Southern Power Grid,Guangzhou 510663,China;Guangzhou Zhixun Information Technology Co.,Ltd.,Guangzhou 510700,China)

机构地区：[1]南方电网数字电网研究院有限公司,广东广州510663 [2]广州致讯信息科技有限责任公司,广东广州510700

出　　处：《微型电脑应用》2022年第8期110-112,共3页Microcomputer Applications

摘　　要：在构建无人机区域导航定位信号特征提取模型时,如果没有及时获取定位信号,将会导致模型的构建时间长,精准度低、识别率差,因此,提出基于GM-CBMeMBer算法的集群无人机区域导航的定位信号特征提取模型构建方法。该方法先利用GM-CBMeMBer算法对集群无人机区域导航时的随机噪声及滤波进行改善,计算出无人机导航时的衰减因子和新息协方差,再将计算后的滤波进行高斯项剪枝合并以此获取定位信号。最后将定位信号进行特征的提取压缩,构建定位信号的特征提取模型。测试结果表明,所提方法的模型构建时间短、提取特征的精准度好、识别率高。If the location signal feature extraction model of UAV regional navigation is not obtained in time,it may lead to long construction time,low accuracy and poor recognition rate of the model.Therefore,a feature extraction model construction method of location signal of cluster UAV regional navigation based on GM-CBMeMBer algorithm is proposed.Firstly,GM-CBMeMBer algorithm is used to improve the random noise and filtering of UAV cluster regional navigation,and the attenuation factor and innovation covariance of UAV cluster regional navigation are calculated.Then the Gaussian term of the filter is pruned and combined to obtain the positioning signal.Finally,the location signal is extracted and compressed to construct the feature extraction model of the location signal.The test results show that the model construction time of the proposed method is short,the accuracy of feature extraction is good,and the recognition rate is high.

关 键 词：GM-CBMeMBer算法 集群无人机 区域导航 定位信号 特征提取模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]