Monostatic surface wave radar is vulnerable by the threat of stealth targets and establishment of near space bistatic radar is the most achievable way to solve this problem. The positioning principle and accuracy in near space bistatic radar is discussed in this paper. The geometrical dilution of precision expression of single measurement subset is calculated. The position precision contours of measurement subsets are obtained and which subset can be chosen for positioning at different area is pointed out. Simulation results show that different subset has different position accuracy and a high accuracy subset distributing picture is presented. Research of this paper provides a theoretical base of detecting and tracking for near space bistatic radar.
For the complexity and nonlinearity of the input characteristics in network intrusion detection system, a feature extraction method for network intrusion detection based on RS-KPCA is studied. Firstly, the Rough Set (RS) theory is used to select the valuable features, while the unnecessary features are removed. Then, the features of the intrusion detection sample data are extracted by the kernel principal component analysis (KPCA) algorithm. The number of new features is determined by the cumulative contribution rate. Simulation results show that this method can effectively remove the interference features, and has the advantages of obvious principal component feature and concentrated contribution rate, compared with PCA. Overall, the proposed method can effectively integrate the nonlinear features of the original data, reduce the dimension, and improve the intrusion detection performance.
An intrusion detection method based on RS-LSSVM is studied in this paper. Firstly, attribute reduction algorithm based on the generalized decision table is proposed to remove the interference features and reduce the dimension of input feature space. Then the classification method based on least square support vector machine (LSSVM) is analyzed. The sample data after dimension reduction is used for LSSVM training, and the LSSVM classification model is obtained, which forms the ability of detecting unknown intrusion. Simulation results show that the proposed method can effectively remove the unnecessary features and improve the performance of network intrusion detection.
The optimal deployment model and algorithm for near space bistatic radar network are discussed in this paper. Firstly, the coverage rate is analyzed, and the optimal deployment model is established. Then, the standard particle swarm algorithm is improved. Initial position and velocity of every particle are created by the chaos algorithm, and the individual and global extremes are disturbed with some probability in the optimization, which are advantageous in achieving a faster and globalized search of particles. Finally, the improved particle swarm algorithm is used to solve the optimal deployment issue. Simulation results demonstrate the validity of the proposed model and algorithm.
Data association is one of the most important issues in multi-target tracking for radar network. In order to meet the high real-time requirement of the multi-target tracking system in the future, a data association method based on time restraint is presented. Firstly, the modified association probability matrix between the observations and tracks is calculated, and the optimal association model is established. Then, the time restraint based auction algorithm is proposed to solve the association issue. The design and flow of this algorithm is offered, and the simulation of the method is designed. Simulation results demonstrate that the proposed method has an ideal association performance in the restrained time.
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