Objective   Charge Coupled Devices (CCD) is a common photoelectric sensor for acquiring image information in photoelectric warfare. In photoelectric warfare, active detection, optical performance analysis and damage status assessment of enemy CCD device are the prerequisites for effective implementation of photoelectric warfare. At present, there are few studies on CCD damage status and damage grade assessment based on the detection echo information, and the actual assessment is affected by the complex environment. The CCD damage status has a complex nonlinear relationship with the "cat's eye" echo intensity and polarization degree which can’t correctly judge whether the CCD is damaged or not based on the intensity and polarization value alone. Therefore, it is considered to use multi-source information fusion method to carry out research on CCD damage status assessment, that is, combining the characteristic information of multiple CCD to obtain the optimal estimation.

  Methods  Combined with multi-source information fusion technology and machine learning, three models of KNN, K-SVM and PNN suitable for nonlinear data classification and discrimination are used to study the assessment method of CCD damage status. Among the three assessment methods, the KNN method uses the category of the proximity point to predict the category, the K-SVM method uses the hyperplane to predict the category and the PNN method uses a posterior probability density to predict categories.

  Results and Discussions   The near- and long-distance "cat's eye" echo detection experiments were carried out respectively, and the echo intensity, polarization degree information and CCD actual damage information were used as input data to train the three models respectively (Tab.3), and the assessments of the three models were compared including the number of errors in the assessment points, the error rate and the assessment time (Fig.5-6), which show that the error rates of KNN and K-SVM fluctuate within 4%, and the error rate of PNN fluctuates within 2% during the five random test sets. The selection of test sets has a great impact on the KNN and K-SVM, but the error rate of PNN is relatively stable which does not affect the PNN. The assessment effect of different scenarios is compared by using the average value of the results of five random test sets (Tab.4), and the near-distance experiment assessment effect is close, with the average error rate of 2%-3%; the average error rate of long-distance experiment assessment is 7%-12%, in which the average error rate is the lowest and prediction time is short, but the stability is not good as K-SVM; the average error rate of mixed data assessment is 10%-14%, in which PNN has the lowest average error rate and good stability, but the prediction time is about twice that of other methods.

  Conclusions   PNN model with the optimal smoothing factor had the lowest error rate in the complex outdoor environment, considering the allowable time range of the actual assessment, the PNN model was most suitable for use based on application of CCD damage status assessment of "cat's eye" echo information. The PNN model has better comprehensive assessment effect than the other two methods and has the best stability in the comprehensive environment. The research results are an exploration of laser damage status assessment, which is conducive to improving the assessment ability of the detection target and the intelligent degree of the system, and provides a new idea for the non-contact laser active detection and assessment technology and improving the defense and strike ability of the weapon system.