摘要
多源信息融合能够扩展声呐系统的探测范围,提高目标识别的可靠性和鲁棒性。文中针对声呐目标探测中使用全频谱特征信息进行目标关联所需传输带宽大,目标威胁度判别的先验概率不确定,无法在概率论框架内进行融合处理等问题,提出了一种基于多源信息融合的声呐目标非威胁度评估方法。该方法以低频声呐目标作为威胁目标的排除对象,结合高频阵、非声传感器等输出的目标信息,通过对多源信息预处理、目标航迹关联以及关联信息融合等进行低频声呐目标的非威胁度评估,达到排除非威胁目标、提高警戒效率的目的。在目标航迹关联中,提出了一种改进的灰色关联算法,能够区分2个航迹变化趋势一致但相距较远的不同目标。在缺乏威胁目标判别先验概率条件下,针对概率论方法不能有效应用于信息融合处理的问题,提出了基于Dempster-Shafer证据理论的多源信息融合方法,给出了非威胁目标可信度。最后利用低频阵、高频阵及自动识别系统(AIS)等多源信息的海试数据验证了文中方法的可行性。
Multi-source information fusion can expand the detection range of a sonar system and improve the reliability and robustness of target recognition.To solve the problems of large transmission bandwidth,uncertain priori probability of target threatening degree discrimination,and inability to perform fusion processing within the framework of probability theory in the sonar target association using full spectrum feature information,a non-threatening degree assessment method for sonar targets based on multi-source information fusion is proposed in this paper.The method takes low-frequency sonar target as the exclusion of threatening target,combines the target information output from high-frequency array and non-acoustic sensor,and performs non-threatening degree assessment of low-frequency sonar target through multi-source information preprocessing,target track association,and association information fusion,so as to eliminate non-threatening targets and improve alert efficiency.For target track association,an improved gray association algorithm is proposed,which can distinguish two different targets with the same track change trend but far away from each other.In the absence of a priori probability of threatening target discrimination,a multi-source information fusion method based on Dempster-Shafer evidence theory is proposed to solve the problem that probability theory cannot be effectively applied to information fusion processing,and the credibility of non-threatening targets is given.The proposed method is verified by sea trial data of multi-source information,including low-frequency array data,high-frequency array data and automatic identification system(AIS)data.
作者
周彬
王庆
权恒恒
ZHOU Bin;WANG Qing;QUAN Heng-heng(Science and Technology on Sonar Laboratory,Hangzhou 310023,China;Hangzhou Applied Acoustics Research Institute, Hangzhou 310023,China)
出处
《水下无人系统学报》
北大核心
2018年第5期439-443,共5页
Journal of Unmanned Undersea Systems
基金
中国科协青年人才托举工程第三届(2017~2019年度)项目资助(2017QNRC001)
