Attribute measure theory based error propagation analyses in integrated identification system

With the increasingly complex battlefield situations, uncertain factors in the identification process are increasing and the stability of the identification results consequently decreases. To overcome this drawback, multi-sensor data fusion based integrated identification has been proposed during the identification process. However, the restriction of the performance of the sensors causes a certain bias of the acquired target information; it is therefore difficult to obtain accurate identification results. In this paper, we propose an error propagation model for the integrated identification process based on the attribute measure theory and the analysis of error sources in integrated identification system. The new model is designed to characterize the impact of data bias on identification results quantita-tively. Numerical experiments are given to describe not only the application of the target recognition method in integrated identification, but also the error propagation process from the feature level to decision-making. The effectiveness of the proposed model is demon-strated as well. This study could be a guide for error controls and sensor selection in integrated identification; it will be also useful to optimize the integrated identification system.