摘要
通过分析CS-Jerk模型的缺陷,提出一种改进的"当前"统计Jerk模型算法。该算法根据新息的统计学特性,即新息协方差矩阵的迹服从卡方分布,构造活化函数,由活化函数生成修正因子,自适应更新CS-Jerk模型中的最大、最小机动加加速度以及机动频率,进而自适应调整状态噪声协方差矩阵和滤波增益矩阵,减小了目标状态估计误差。与经典的Jerk模型、CS-Jerk模型相比,改进算法有效地提高了对强机动目标的跟踪精度,弥补了CS-Jerk模型算法的不足,仿真结果验证了算法的可行性。
Analysis was made to the defects of CS-Jerk model, based on which an improved CS-Jerk algorithm was proposed. According to the statistical characteristics of innovation that the innovation covariance matrix trace is in a chi-square distribution, an activation function was constructed, which could produce activation factor. Then the maximum / minimum acceleration and maneuvering frequency of CS-Jerk model could update adaptively, and thus to adjust the state covariance matrix and filter gain matrix adaptively. Therefore, the error of target state estimation was decreased. Compared with traditional Jerk model and CS-Jerk model, the improved algorithm has higher accuracy in tracking highly maneuvering target. Simulation results show the feasibility of the algorithm.
出处
《电光与控制》
北大核心
2016年第3期11-15,共5页
Electronics Optics & Control
基金
航空科学基金(20135184007)
关键词
强机动目标
统计分析
活化函数
CS-Jerk模型
highly maneuvering target
statistical analysis
activation function
CS-Jerk model
