Research on Autonomous Detection Method of Underwater Unmanned Vehicle

Abstract

Autonomous detection technology is the key technology of the intelligent underwater unmanned vehicle (UUV), and it is the prerequisite for the unmanned vehicle to perform underwater early warning, target tracking and other tasks autonomously. This paper proposes a constant false alarm rate (CFAR) detection method based on the azimuth-time two-dimensional reference window combined ordered truncated average algorithm, which is suitable for the multitarget detection of passive sonar on UUV. Firstly, an OTA algorithm is used to estimate the statistics characteristics of background noise, and equalize the undulating background. Secondly, the azimuth-time two-dimensional reference window is used to solve the problem of too few reference samples for detection of one-dimensional reference window and inaccurate estimation of the noise statistics characteristics. Finally, the constant false alarm detector is constructed by using the mean value and variance of the background noise, and the tracking before detection (TBD) technology is adopted to realize the automatic detection and tracking of multiple targets in the undulating background. The method proposed in this paper can well solve the problem that the current CF AR detection algorithm is prone to missing reports and false alarms when the near-field interference is strong and the target quantity is unknown. The test results on the lake show that the method still has a good autonomous detection effect on multiple targets under the self-noise interference of the UUV.