基于组稀疏卡尔曼滤波的多步轨迹预测方法  

A Multi-Step Trajectory Prediction Method Based on Group Sparse Kalman Filtering

作　　者：王娜[1,2,3] 罗亮 彭锟[1] 张鑫海 WANG Na;LUO Liang;PENG Kun;ZHANG Xin-hai(School of Control Science and Engineering,Tiangong University,Tianjin 300387,China;Tianjin Key Laboratory of Intelligent Control of Electrical Equipment,Tianjin 300387,China;Key Laboratory of Micro Optical Electronic Mechanical System Technology of Ministry of Education,Tianjin University,Tianjin 300072,China)

机构地区：[1]天津工业大学控制科学与工程学院,天津300387 [2]天津市电气装备智能控制重点实验室,天津300387 [3]微光机电系统技术教育部重点实验室(天津大学),天津300072

出　　处：《空军工程大学学报》2023年第6期70-77,共8页Journal of Air Force Engineering University

基　　金：天津市重点研发计划项目(19YFHBQY00040);微光机电系统技术教育部重点实验室(天津大学)开放基金(MOMST2016-4)。

摘　　要：提出一种基于组稀疏卡尔曼滤波的机动轨迹多步预测方法。首先引入组稀疏编码,通过一次学习建立简单的多步线性回归预测模型,克服了传统方法未能充分利用历史数据而导致预测精度降低的问题;再利用最小角回归算法来计算该模型的稀疏系数,进一步改善模型系数估计的准确性;然后改进了卡尔曼滤波算法,并结合上述组稀疏编码算法,来确保预测结果的精确性;最后通过与传统BP、长短时记忆网络和组稀疏编码方法的仿真比较,验证了所提方法的有效性。A multi-step trajectory prediction method based on group sparse coding Kalman filtering for mobile target is proposed in this paper.Firstly,a group sparse coding is introduced,and just at that time,a simple multistep linear prediction model is obtained by one learning,overcoming the problem that prediction accuracy is low due to the inadequate historical data with the traditional method.And then,the minimum angle regression algorithm is utilized for calculating the sparse coefficients of the above model to further improve the estimation accuracy of the model coefficients.The basic Kalman filtering algorithm is modified in combination with the group sparse coding method to ensure the precision in the prediction output.Finally,the effectiveness of the presented approach is verified by the simulation comparison among the traditional BP network,long short time memory network and the group sparse coding method.

关 键 词：多步轨迹预测 组稀疏编码 卡尔曼滤波 最小角回归 

分 类 号：TN953[电子电信—信号与信息处理]