Stagewise Additive Modeling using a Multi-class Exponential loss function (SAMME) is a multi-class AdaBoost algorithm. To further improve the performance of SAMME, the influence of using weighed error rate and pseudo loss on SAMME algorithm was studied, and a dynamic weighted Adaptive Boosting (AdaBoost) algorithm named SAMME with Resampling and Dynamic weighting (SAMME.RD) algorithm was proposed based on the classification of sample's effective neighborhood area by using the base classifier. Firstly, it was determined that whether to use weighted probability and pseudo loss or not. Then, the effective neighborhood area of sample to be tested in the training set was found out. Finally, the weighted coefficient of the base classifier was determined according to the classification result of the effective neighborhood area based on the base classifier. The experimental results show that, the effect of calculating the weighted coefficient of the base classifier by using real error rate is better. The performance of selecting base classifier by using real probability is better when the dataset has less classes and its distribution is balanced. The performance of selecting base classifier by using weighed probability is better when the dataset has more classes and its distribution is imbalanced. The proposed SAMME.RD algorithm can improve the multi-class classification accuracy of AdaBoost algorithm effectively.

As to the issue that the weight expansion for hardest samples can cause imbalance when updating the training sample in AdaBoost algorithm, an improved approach based on the Loss Function (LF) of the different patterns, namely, LF-AdaBoost, was proposed. The weight tuning was affected not only by the training error, but the performance of base classifiers for different classes, thus avoiding the excessive concentration phenomenon. The results based on UCI data sets and different base classifiers have shown that the approach can improve the speed of convergence and overcome the imbalance, as well as promote the generalization ability of ensemble classifier.

To deal with the uncertainty of multi-sensor observations, a new data association algorithm based on intuitionistic fuzzy clustering was proposed. The clustering algorithm of improved Intuitionistic Fuzzy C-Means (IFCM) was applied to data association in the proposed algorithm. Firstly, the observed data and predicted data were made to be intuitionistic fuzzy. Then the weighted distance between intuitionistic fuzzy sets was calculated to acquire membership degrees of observation and track. Finally, the highest degree of membership was sought successively to associate observation and track. The simulation results show that the presented algorithm can associate data with the fuzzy observations effectively.