| In the 1960s of the last century, SVM arose from statistical learning theory, the aim being to solve only the problem of interest without solving a more difficult problem as an intermediate step. SVM are based on the structural risk minimization principle, closely related to regularization theory. This principle incorporates capacity control to prevent over-fitting and thus is a partial solution to the bias-variance trade-off dilemma. SVM were first suggested by Vapnik for classification and have recently become an area of intense research owing to developments in the techniques and theory coupled with extensions to regression and density estimation, and had good performance.The kalman filter is a real-time recursion algorithm and all the system states are in the time domain space, therefore, it is appropriate for estimating multi-dimensional stochastic process. Moreover, there is no need to save each system state in memory and we deal with the estimates online, making them trend to real states regularly. In addition, the kalman filter uses the statistical property of the system noise and the observation noise to process the signal and the kalman filter applies the system observation as the input of the filter and the estimation (system state or parameter) as the output. Not only it may carry on the process to the steady uni-dimensional stochastic process, but also it can estimate the non-steady multi-dimensional stochastic process, therefore its application is very widespread.The combination of GPS and inertial navigation system (INS) is the best integrated navigation, and both INS and GPS are global, all-round and full-time navigation equipments. They can provide very completed navigation data, and supplement the shortcomings of each other, which can supply higher accuracy than each works alone. As for INS, integrated navigation corrects the inertial sensors for improving the accuracy, and with the help of INS, GPS enhances the ability of positioning and tracking, which protects the GPS receiver from interference.In the first three chapters, we present the basic principal of SVM, kalman filter and GPS/INS integrated navigation. In the chapter four, we propose a novel resampling SVM algorithm, which is inspired by GA and SMOTE. This method is based on using the mutation and crossover operators of DE to over-sample the minority class to lessen the imbalance ratio and then clustering for both classes to delete redundant or noisy samples. Thus, by combining over-sampling and data cleaning technique, the useful samples are remained, improving the computational efficiency.In chapter five, we present an online optimized method named support vector regression self-adaption kalman filter algorithm (SVREKF). This method uses SVR for adjusting the observation covariance matrix online according to the current system observations. Moreover, using the adjustment factor to update the noisy system dynamically in order to make trend to actual noise and improve the accuracy of estimation. Providing system noise is the zero-mean Gaussian white noise, we recognize that the ratio of the theoretical residual covariance matrix and the actual residual covariance matrix is 1. If the ratio is far away from 1, then it illustrates the observation noise changes, which should adjust the noisy covariance matrix so that the ratio returns to 1.The innovation of this thesis can be grouped into two points. (ⅰ) Propose a novel resampling SVM algorithm application in imbalanced datasets problems, and then make experiments on UCI standard datasets. The results show that our method is an efficient way to solve imbalanced datasets problems, compared with standard SVM, SMOTE-SVM and DE-SVM under the criterion of F-measure and ROC Area (AUC). (ⅱ) Present a support vector regression self-adaption kalman filter application in vehicle-mounted GPS/INS integrated navigation, and make comparison with extend kalman filter and fuzzy self-adaption kalman filter for verifying the performance of this algorithm. |
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