Asynchronous Multisonar Data Integration Approach Used to Detect Sparse Targets

Abstract

Due to the advantage of the distributed multisensor detection system which makes sonar detect further and more accurate estimation of the target state, distributed multisensor data fusion algorithms are widely applied to the sonar detection system. However, on the one hand, the most asynchronous algorithms focus on how to convert asynchronous data fusion into synchronous data fusion. On the other hand, sonar detection system suffers from more serious asynchronous data problems (such as more serious random delay and packet loss defaults) than radar and other fields. Therefore, the traditional asynchronous fusion method has some limitations. When the targets are sparse, this paper proposed a novel asynchronous multisonar data integration approach, in which the Gaussian mixture probability hypothesis density (GMPHD) filter is used to filter clutter for local sonar sensor. Then, the Gaussian mixture model (GMM) algorithm is used to model asynchronous data over a period of time. Finally, all local sonar detection data are integrated into a surveillance region image to help to detect the target. Several simulation tests and a sea test are presented in this paper to test this approach performance.