基于时间序列森林的雷达高分辨距离像目标识别  

Radar high resolution range profile target recognition based on time series forest

作　　者：程巍轶 张红敏[1] 黄燕[3] CHENG Weiyi;ZHANG Hongmin;HUANG Yan(University of Information Engineering,Zhengzhou 450001;91911 troops of PLA,Sanya 572000,China;32316 troops of PLA,Urumqi 830001,China)

机构地区：[1]信息工程大学,河南郑州450001 [2]中国人民解放军91911部队,海南三亚572000 [3]中国人民解放军32316部队,新疆乌鲁木齐830001

出　　处：《指挥控制与仿真》2024年第3期137-143,共7页Command Control & Simulation

摘　　要：为了提高雷达目标识别准确率与不完备角域数据下的识别性能,提出一种基于时间序列森林的高分辨距离像(HRRP)目标识别算法。详细介绍了时间序列森林算法的基本原理和用于HRRP目标识别的基本步骤。对实测HRRP数据的实验结果表明,相比于K-最近邻(KNN)、支持向量机(SVM)、随机森林(RF)、卷积神经网络(CNN)和长短时记忆网络(LSTM)等经典的目标识别算法,本文算法具有较优的识别性能与更好的角域推广能力。在只训练1/2和1/3角域数据的条件下,对全角域数据识别率均值优于85%,较上述方法平均提升5.2%。In order to improve the recognition accuracy of radar targets and the recognition performance under incomplete angular domain data,a high resolution range profile(HRRP)recognition algorithm based on time series forest is proposed.The basic principle of time series forest algorithm and the basic steps of HRRP target recognition are introduced in detail.The experimental results of measured HRRP data show that compared with k-nearest neighbors(KNN),support vector machine(SVM),random forest(RF),convolutional neural network(CNN),long short-term memory(LSTM)and other classical target recognition algorithms,the proposed algorithm has better recognition performance and better angular domain promotion ability.Under the condition of training only 1/2 and 1/3 angular domain data,the average recognition rate of the full angular domain data is better than 85%,which is an average increase of 5.2%compared with the above method.

关 键 词：雷达自动目标识别 高分辨距离像 时间序列森林 不完备角域 

分 类 号：E911[军事] TN95[电子电信—信号与信息处理]