Target threat assessment using improved SVM
李 姜 LI Jiang, 郭立红 GUO Li-hong · Optics and Precision Engineering · 2014
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 c and core function g 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.