CAJ | 학술논문

针对无序量测(OOSM)情况下的机动微弱目标检测与跟踪问题,提出一种基于OOSM和多模粒子滤波(OOSM-MMPF)的检测前跟踪(TBD)算法.该算法通过直接利用OOSM对粒子权重进行更新,并在此基础上对粒子集进行重采样,从而实现OOSM情况下的目标状态更新.由于充分利用了OOSM包含的信息,该算法可以有效提高机动微弱目标的正确检测概率与跟踪精度.仿真结果表明,该算法可以有效处理OOSM问题,实现对机动弱目标的有效检测和跟踪,其算法性能接近顺序量测滤波时的MMPF算法性能.
침대무서량측(OOSM)정황하적궤동미약목표검측여근종문제,제출일충기우OOSM화다모입자려파(OOSM-MMPF)적검측전근종(TBD)산법.해산법통과직접이용OOSM대입자권중진행경신,병재차기출상대입자집진행중채양,종이실현OOSM정황하적목표상태경신.유우충분이용료OOSM포함적신식,해산법가이유효제고궤동미약목표적정학검측개솔여근종정도.방진결과표명,해산법가이유효처리OOSM문제,실현대궤동약목표적유효검측화근종,기산법성능접근순서량측려파시적MMPF산법성능.
To address the problem of maneuvering weak target detection and tracking with out-of-sequence measurements (OOSM),a maneuvering weak target track-before-detect method is proposed based on the OOSM and multiple model particle filter (OOSM-MMPF). By updating the weights of particles directly with the OOSM and performing a resampling step on the particle set,the target state is updated with the OOSM. Due to make full use of the information contained in the OOSM,the proposed method can improve the correct detection probability and tracking performance of target efficiently. Simulation results demonstrate that the proposed method can solve the problem of OOSM and detect and track maneuvering weak target effectively with the performance of target tracking is close to that of MMPF with in-sequence measurements.