CAJ | 학술논문

为了提高基于前视声呐的水下多目标跟踪精度，在粒子滤波跟踪的基础上，采用多特征自适应线索融合策略，通过在线调整特征融合方法计算粒子权值，提取出每个粒子对应模板的多个特征，包括形状与亮度特征、不变矩数字特征和灰度共生矩阵数字特征。采用自适应融合策略对粒子的各个特征权值进行融合得到最终权值，特征线索良好时采用乘性融合策略，否则采用基于模糊逻辑的加权融合策略。采用2组前视声呐水池试验序列图像，通过与传统融合策略进行对比试验，验证了自适应融合策略的有效性，对于实现水下智能机器人的自主跟踪具有重要的意义。
위료제고기우전시성눌적수하다목표근종정도，재입자려파근종적기출상，채용다특정자괄응선색융합책략，통과재선조정특정융합방법계산입자권치，제취출매개입자대응모판적다개특정，포괄형상여량도특정、불변구수자특정화회도공생구진수자특정。채용자괄응융합책략대입자적각개특정권치진행융합득도최종권치，특정선색량호시채용승성융합책략，부칙채용기우모호라집적가권융합책략。채용2조전시성눌수지시험서렬도상，통과여전통융합책략진행대비시험，험증료자괄응융합책략적유효성，대우실현수하지능궤기인적자주근종구유중요적의의。
In order to improve the accuracy of underwater multi-object tracking based on the forward looking sonar, on the basis of particle filter tracking, the multi-feature adaptive clue fusion method was used to switch fusion meth-ods by adjusting features online to calculate the particle weight. Particles were initialized. Then multiple features of the template corresponding to every particle were extracted, including the basic object shape and intensity features, digital features of moment invariants and digital features of the gray level co-occurrence matrix. The final particle weight was obtained by fusing every feature weight using the adaptive fusion method. Multiplicative fusion was a-dopted when the features worked well;otherwise weighted sum fusion based on fuzzy logic was adopted. Sequence images through a tank experiment were used to verify the effects of the adaptive fusion method, in contrast to the traditional fusion methods. The images were obtained by using the forward looking sonar, describing two cross mo-tions. The tracking ability using the adaptive fusion method was found to be better. This method has significant ef-fectiveness for automatically tracking autonomous underwater vehicles.