A Classification Method of Vehicle Targets Based on Micro-Doppler Effect and Auto Regression Model

LI Kai-ming; ZHANG Qun; LUO Ying; DING Shuai-shuai; GUO Ying

doi:10.3969/j.issn.0372-2112.2018.04.006

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A Classification Method of Vehicle Targets Based on Micro-Doppler Effect and Auto Regression Model

更新时间：2025-07-16

- A Classification Method of Vehicle Targets Based on Micro-Doppler Effect and Auto Regression Model
- Acta Electronica Sinica Vol. 46, Issue 4, Pages: 805-813(2018)
- 作者机构：
  
  1. 空军工程大学信息与导航学院,陕西,西安,710077
  2. 复旦大学电磁波信息科学教育部重点实验室,上海,200433
  3. 信息感知技术协同创新中心,陕西,西安,710077
  4. 空军工程大学信息与导航学院,陕西,西安,710077
  5. 复旦大学电磁波信息科学教育部重点实验室,上海,200433
  6. 信息感知技术协同创新中心,陕西,西安,710077
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61471386, No.61571457, No.61701530);Youth Nova Program for Science and Technology of Shaanxi Province (No.2016KJXX-49)
- DOI：10.3969/j.issn.0372-2112.2018.04.006
  CLC： TN957
- Published Online：25 April 2018，
  
  Published：2018
- 稿件说明：
移动端阅览
LI Kai-ming, ZHANG Qun, LUO Ying, et al. A Classification Method of Vehicle Targets Based on Micro-Doppler Effect and Auto Regression Model[J]. Acta Electronica Sinica, 2018, 46(4): 805-813.
DOI：

LI Kai-ming, ZHANG Qun, LUO Ying, et al. A Classification Method of Vehicle Targets Based on Micro-Doppler Effect and Auto Regression Model[J]. Acta Electronica Sinica, 2018, 46(4): 805-813. DOI： 10.3969/j.issn.0372-2112.2018.04.006.

摘要

轮式车辆和履带式车辆的分类是地面目标识别的难点之一.车轮旋转和履带的运动是典型的微动，其产生的微多普勒特征可作为两类车辆目标分类的重要依据.首先，针对短驻留条件下两类车辆目标的雷达回波，分析了两类目标不同微动导致的微多普勒特征差异；其次，基于目标回波短时平稳相关的性质，建立了目标回波的AR模型，采用前后向预测方法得到相应的AR模型系数，提出基于AR模型系数的车辆目标分类方法，并给出AR模型阶数的判定方法，对比了前后向预测系数特征与前向预测系数特征的可分性.最后，结合两类目标的实测数据，在回波预处理的基础上，通过提取实际回波数据的AR模型系数实现了车辆目标的分类，验证了方法的有效性和稳健性.

Abstract

The classification of the wheeled vehicles and tracked vehicles is one of the difficulties of vehicles recognition. Rotation of the wheels and running of the track are the typical forms of micro-motion

the micro-Doppler signatures of the micro-motion can be the crucial proof for classification of the two kinds of vehicles. Firstly

the micro-Doppler difference of different micro-motion of the two vehicles are analyzed based on the echoes under radar with short dwell time;Secondly

based on the short-time stability and correlation of the echoes

the Auto Regression (AR) model of echoes is established

the forward and backward prediction method is applied in the extraction of AR model coefficients

which are used in the classification of the two kinds of vehicles

and the selection of the order of AR model is operated

the separation abilities between forward and backward prediction coefficients and forward prediction coefficients are compared;At last

after the pre-processing of the measured data

the classification of the two vehicles is accomplished with high accuracy based on the extraction of AR model coefficients. The effectiveness and robustness of the method are proved by the simulation results.

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