A weak target detecting method based on multistatic information fusion

Abstract

The detecting performance of active sonar have been affected by the weak targets scattering and serious noise background. If artificial reducing the threshold to get high detecting capability, the false alarm probability will be very high. Therefore, this paper propose a weak target detecting method based on multistatic information fusion. After getting more information about the target from different positions and extracting the target feature vectors to make a fusion, a better recogniting performance will be present. The target scattering centers are extracted by CLEAN technique, which could avoid extracting the false scattering centers, and adapt in low signal to noise ratio (SNR). The neural network classifier is used to make a fusion and recognize the target. Simulation results show that this method has better target identification performance and low false alarm probability, demonstrating the target features fusion has high value for active sonar detecting in the future.