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结合自适应常加速模型(ACA)、改进输入估计(MIE)和强跟踪滤波器,提出一种新的自适应目标跟踪模型和算法.该算法通过扩展ACA模型状态矢量和改进状态噪声协方差调整方法,利用MIE和强跟踪滤波器,实现了机动加速度方差和状态预测协方差依据残差信息的实时完全自适应调整,在缺乏目标加速度先验知识的情况下,能够实时高精度跟踪目标突变状态、弱机动和非机动状态.仿真实验表明,相比ACA模型和MIE,该算法具有更好的机动状态和非机动状态跟踪性能.
A new adaptive target tracking model and algorithm is proposed by combining adaptive constant acceleration model (ACA), improved input estimation (MIE), and strong tracking filter. This algorithm extends the ACA model state vector and improves the state noise covariance adjustment Method, using MIE and strong tracking filter, the maneuvering acceleration variance and state prediction covariance can be completely adaptively adjusted based on the residual information in real time. Without the prior knowledge of target acceleration, the target sudden change state can be tracked with high accuracy in real time , Weak maneuvering and non-maneuvering states.The simulation results show that the proposed algorithm has better performance of maneuvering and non-maneuvering than the ACA model and MIE.