“当前”半马尔科夫模型及自适应跟踪算法

“Current” half a Markov model and adaptive tracking algorithm

通过分析基于"当前"统计模型的自适应卡尔曼滤波算法的不足之处,在"当前"统计模型的基础上引入空气阻力系数和非零加速度,提出了"当前"半马尔可夫统机动模型,从而更符合机动目标运动的实际情况;基于此模型提出了改进的自适应卡尔曼滤波算法。仿真结果表明,改进的"当前"半马尔可夫卡尔曼滤波算法收敛速度更快,跟踪误差更小。

Through the analysis of the ＂current＂ statistical model based on adaptive Kalman filtering algorithm deficiency, in the ＂current＂ statistical model on the basis of the air resistance coefficient and nonzero acceleration, this paper proposes ＂current＂ semi-Markovian series motor model, the model is more in line with the actual conditions of maneuvering target. Based on this model, it puts forward some improvement adaptive Kalman filter. The simulation results show that the improved ＂current＂ semi-Markovian Kalman filtering algorithm convergence speed is faster, and state estimation is more precise.