摘要
Singer模型使用标准卡尔曼滤波器对机动目标进行跟踪,当系统模型不准确或噪声统计特性不确定时,容易引起滤波发散或跟踪精度下降等问题。针对这种情况,本文提出了一种采用自适应渐消卡尔曼滤波的Singer模型算法(AKF Singer),通过引入渐消因子来抑制滤波器的记忆长度,自适应的调整新息权重和滤波器增益,从而避免发散。仿真结果表明,本文所提算法能够有效抑制滤波发散,相比于传统Singer模型,具有更好的跟踪稳定性和更高的跟踪精度。
Considering the divergence and poor precision due to inaccurate modeling of the system or noise, an adaptive fading kalman filter based on singer model （AKF -Singer）is proposed. It adopts a fading factor to restrain the memory length of kalman filter. Simulation shows that the proposed algorithm can effectively restrain the divergence of filtering, and is more steady and more precise compared to traditional singer model
出处
《火控雷达技术》
2015年第3期37-40,50,共5页
Fire Control Radar Technology
