CAJ | 학술논문

针对LOS/NLOS混合条件下对机动目标的鲁棒跟踪问题，提出一种基于AR预测模型的交互式多模型（Interacting Multiple Model，IMM）跟踪算法（ARIMM）。该算法利用AR预测模型对运动状态建模，针对LOS与NLOS条件下观测噪声的分布不同分别使用无迹卡尔曼滤波器（Unscented Kalman Filter，UKF）和改进的无迹卡尔曼滤波器（Robust Unscented Kalman Filter，RUKF），通过IMM方法估计出移动台的位置，利用该位置更新AR模型的参数，使AR模型与真实运动状态更加匹配，实现精确跟踪。仿真结果表明，在LOS/NLOS混合条件下，与传统的UKF和RUKF算法相比，该算法对机动目标跟踪的鲁棒性更好。
침대LOS/NLOS혼합조건하대궤동목표적로봉근종문제，제출일충기우AR예측모형적교호식다모형（Interacting Multiple Model，IMM）근종산법（ARIMM）。해산법이용AR예측모형대운동상태건모，침대LOS여NLOS조건하관측조성적분포불동분별사용무적잡이만려파기（Unscented Kalman Filter，UKF）화개진적무적잡이만려파기（Robust Unscented Kalman Filter，RUKF），통과IMM방법고계출이동태적위치，이용해위치경신AR모형적삼수，사AR모형여진실운동상태경가필배，실현정학근종。방진결과표명，재LOS/NLOS혼합조건하，여전통적UKF화RUKF산법상비，해산법대궤동목표근종적로봉성경호。
In view of the problem of robust tracking of maneuvering target under LOS/NLOS condition, an IMM algo-rithm based on AR prediction model is proposed(ARIMM). AR prediction model is adopted to model the motion state, and UKF and RUKF are utilized separately for the reason that the state LOS and NLOS have different distribution of observation noise, and the IMM filter is used to estimate the position of BS, and the position is used to update the current parameters in AR prediction model and the AR model is made more matched with the true motion state, therefore the algo-rithm can perform precisely tracking. Simulation result demonstrates that the proposed algorithm performs better robust-ness under LOS/NLOS condition compared with the traditional UKF and RUKF.