水下无人航行器自主检测方法研究

自主检测技术是实现水下无人航行器(Underwater Unmanned Vehicle,UUV)智能化的关键技术,是无人航行器能够自主执行水下预警、目标跟踪等任务的前提。针对当前基于均值类和有序统计类恒虚惊(Constant False Alarm Rate,CFAR)技术的自主检测方法在背景起伏严重、多目标情况下,背景噪声统计特性估计不准确、自主检测性能下降的问题,文章提出了一种基于方位-时间二维参考窗联合有序截断平均算法的自主检测方法。首先,该方法设计了一种方位-时间二维参考窗,解决了一维参考窗检测参考样本过少、噪声统计量估计不准的问题;其次,采用有序截断平均算法估计背景噪声统计量,对起伏背景进行均衡;最后,利用背景噪声均值和方差构造恒虚警检测器,采用检测前跟踪技术,实现起伏背景下、多目标自动检测与跟踪。湖上试验结果表明,在水下无人航行器的自噪声干扰下,该方法对多目标依然具有较好的自主检测效果。

Research on UUV autonomous detection method

Autonomous detection technology is the key technology to realize underwater intelligent unmanned vehicle, and it is the premise that the unmanned vehicle can independently perform underwater early alert and target tracking. Aiming at the problem that the performance of the existing autonomous detection method based on the mean level constant false alarm rate (CFAR) and the ordered statistics CFAR is degraded under the condition of background fluctuation and multi-target, which is due to the inaccurate estimation of background noise statistical characteristics, an autonomous detection method based on the azimuth-time two-dimensional reference window associating the ordered truncation average (OTA) algorithm is proposed. In this method, the azimuth-time two-dimensional reference window is designed to solve the problem of noise inaccurate estimation caused by less reference samples in one-dimensional reference window, and the ordered truncate average algorithm is used to estimate the background noise statistics and to normalize the fluctuating background. Then, a constant false alarm detector is constructed by using the mean value and variance, and a tracking-before-detection technique is adopted to achieve multi-target automatic detection and tracking in the fluctuating background. The lake-test results show that the autonomous detection method has a good effect on multi-target detection under the interference of UUV self-noise.