CAJ | 학술논문

　　目标跟踪系统的数学模型或统计特性的不确定性，往往会导致机动目标跟踪精度降低甚至跟踪发散。在综合平方根求容积卡尔曼滤波算法和改进的Sage-Husa估计器的基础上，提出一种自适应求容积平方根卡尔曼滤波算法。该算法通过实时估计未知系统噪声，抑制由于噪声统计特性未知时变而导致的滤波误差，从而实现机动目标的自适应跟踪。仿真结果表明，在系统噪声未知时变，且与先验系统噪声存在一定差异时，自适应平方根求容积卡尔曼滤波器能有效地改进标准平方根求容积卡尔曼滤波器的跟踪精度和跟踪稳定性。
　　목표근종계통적수학모형혹통계특성적불학정성，왕왕회도치궤동목표근종정도강저심지근종발산。재종합평방근구용적잡이만려파산법화개진적Sage-Husa고계기적기출상，제출일충자괄응구용적평방근잡이만려파산법。해산법통과실시고계미지계통조성，억제유우조성통계특성미지시변이도치적려파오차，종이실현궤동목표적자괄응근종。방진결과표명，재계통조성미지시변，차여선험계통조성존재일정차이시，자괄응평방근구용적잡이만려파기능유효지개진표준평방근구용적잡이만려파기적근종정도화근종은정성。
Considering low accuracy even divergence of maneuvering target tracking due to inaccurate tracking model and uncertain statistical property,we proposed an adaptive Square Root Cubature Kalman Filter (SCKF) based on the standard SCKF and modified Sage-Husa estimator.The proposed algorithm can estimate the statistical parameters of unknown system noises online,and restrain the tracking error caused by unknown system noises effectively;hence it is applied to maneuvering target tracking .The simulation results show that:comparing with SCKF algorithm ,the adaptive SCKF can provide better accuracy and stability for maneuvering target tracking when the unknown or time variant system noises is some different from the prior noise knowledge .