CAJ | 학술논문

目前目标跟踪算法采用的交互多模型，大多是通过固定模型之间的切换来完成目标跟踪，这容易出现模型集与目标真实运动不匹配问题，降低目标跟踪的精度。同时，现在大部分观测平台都能提供多传感器量测，这要求跟踪算法能对不同量测信息进行高效数据融合。针对上述问题，提出一种基于自适应变结构多模型和信息滤波的跟踪算法，它由少量模型构成模型集，通过在线更新模型集参数以自适应目标真实运动，采用无迹卡尔曼信息滤波融合多传感器量测信息，实现对目标的跟踪。仿真结果表明，该算法可以有效融合多传感器量测信息，自适应匹配目标真实运动，实现对目标稳定的高精度跟踪。
목전목표근종산법채용적교호다모형，대다시통과고정모형지간적절환래완성목표근종，저용역출현모형집여목표진실운동불필배문제，강저목표근종적정도。동시，현재대부분관측평태도능제공다전감기량측，저요구근종산법능대불동량측신식진행고효수거융합。침대상술문제，제출일충기우자괄응변결구다모형화신식려파적근종산법，타유소량모형구성모형집，통과재선경신모형집삼수이자괄응목표진실운동，채용무적잡이만신식려파융합다전감기량측신식，실현대목표적근종。방진결과표명，해산법가이유효융합다전감기량측신식，자괄응필배목표진실운동，실현대목표은정적고정도근종。
Most current maneuvering target tracking algorithms use Interacting Multiple Model (IMM) by switching fixed models, which can easily cause problems like model-mismatching and thus resulting in inaccurate tracking. Mean-while, multiple sensors are now generally available to provide multi-measurements for target tracking, provoking the needs to efficiently utilize them. To solve these problems, a new algorithm that consists of less models and integrates adap-tive Variable Structure Multi-Model(VSMM)and Information Filtering(IF)is presented, which accomplishes better estimation of maneuvering target’s trajectory through updating the model-set’s parameters in real time and fusing multi-measurements by unscented Kalman IF. Simulation results prove that this new algorithm can efficiently fuse multi-measurements, adaptively match target’s real model and reach a high level of target tracking.