Target threat assessment using improved SVM

LI Jiang; GUO Li-hong

doi:10.3788/OPE.20142205.1354

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Target threat assessment using improved SVM

更新时间：2020-08-13

- Target threat assessment using improved SVM
- Optics and Precision Engineering Vol. 22, Issue 5, Pages: 1354-1362(2014)
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春,中国,130033
  2. 中国科学院大学北京,中国,100049
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20142205.1354
  CLC： TP391.9
- Received：03 January 2013，
  
  Revised：03 March 2013，
  
  Published：25 May 2014
- 稿件说明：
移动端阅览
李姜, 郭立红,. 基于改进支持向量机的目标威胁估计[J]. 光学精密工程, 2014,22(5): 1354-1362

LI Jiang, GUO Li-hong,. Target threat assessment using improved SVM[J]. Editorial Office of Optics and Precision Engineering, 2014,22(5): 1354-1362
李姜, 郭立红,. 基于改进支持向量机的目标威胁估计[J]. 光学精密工程, 2014,22(5): 1354-1362 DOI： 10.3788/OPE.20142205.1354.

LI Jiang, GUO Li-hong,. Target threat assessment using improved SVM[J]. Editorial Office of Optics and Precision Engineering, 2014,22(5): 1354-1362 DOI： 10.3788/OPE.20142205.1354.

摘要

针对信息融合中目标威胁估计的特点，分析了传统目标威胁估计方法和支持向量机（SVM）的不足。采用粒子群算法（PSO）对SVM中惩罚参数

和核函数

进行优化，建立了改进的SVM（PSO_SVM）目标威胁估计模型及算法。介绍了粒子群算法和支持向量机的原理，建立了一种新的PSO_SVM目标威胁估计模型；基于该模型，实现了PSO_SVM目标威胁估计算法。为适应该算法，对数据进行了预处理，包括数据量化和归一化。交叉验证寻找最佳参数时，采用PSO算法进行优化。采集75组原始数据用于仿真实验，其中60组作为训练集，15组作为测试集。仿真实验表明，该算法预测误差为0，达到了预期目标。实验结果真实、准确地反映了实际情况，证明了该方法的有效性。

Abstract

On the basis of the characteristics of target threat assessment in information fusion

the weaknesses of traditional methods for target threat assessment and Support Vector Machine (SVM) were analyzed. By using the Particle Swarm Optimization (PSO) to optimize the penalty parameter

and core function

in the SVM

a new target threat assessment model (PSO_SVM) was established and the PSO_SVM algorithm was achieved based on the model. To satisfy the requirements of PSO_SVM algorithm

data was preprocessed

including quantification and normalization. When cross-validation method was used to find the best parameters

the POD was used for network training. 75 group data were used in simulation experiments

among them 60 group data were train sets and the others were test sets. Experimental results show that the error of the PSO_SVM method is 0

reaching the desired goal

which proves the accuracy and efficiency of the proposed method.

关键词

Keywords

references

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