舰艇预警数据融合仿真系统设计与实现

在舰艇侦察监视系统中,数据融合已成为获得可靠战场感知的一项关键技术。本文结合我们所建立的舰艇预警数据融合仿真测试系统,阐述数据融合系统的设计与实现问题,对系统的数据融合层次化的黑板结构,以及实现中的数据配准、航迹关联和融合、属性融合和身份类型识别、态势估计、性能评估等关键技术进行了讨论。     

Range profile target recognition using sparse representation based on feature space

A novel method is presented to improve the recognition rate of warhead in this paper. Firstly, a tool for electromagnetic calculation, like CST Microwave Studio, is used to simulate the frequency response of the electromagnetic scattering. Secondly, the echo and further the range profile are acquired from the frequency response by further processing. Thirdly, a set of discriminative features is extracted from the range profiles of the target. Fourthly, these features are used to construct a dictionary for the sparse representation classifier. Finally, the sample of the target can be classified by solving the sparsest coefficients. Since the reconstruction result is determined by a linear combination of the training samples, this method has a good robustness for the variable features. By formulating the problem within a feature-based sparse representation framework, the presented method combines the discriminative features of each sample during the sparse recovery process rather than in a postprocessing manner. Moreover, based on the feature representation space rather than a single feature or image pixel, the constructed dictionary exhibits both strong expressive and discriminative powers that can enhance the classification performance of the test sample. A series of test results based on the simulated data demonstrates the effectiveness of our method.