摘要
分析了基于"当前"统计模型的跟踪算法中,机动频率对滤波算法的影响.提出一种模糊自适应跟踪算法,该算法根据量测新息及其变化率通过模糊推理机制调整"当前"统计模型中的机动频率,以适应不同的目标机动模式.针对直角坐标系下量测模型为非线性方程,采用转换坐标卡尔曼滤波对目标状态进行估计.仿真结果表明:该算法无论跟踪机动目标还是非机动目标,其精度都要优于常规的基于"当前"统计模型的跟踪算法.
The effect of maneuvering frequency in current statistical model to filter＇ s performance is analyzed. Based on fuzzy inference, the maneuvering frequency is adjusted on-line according to the measurement innovation and the change of measurement innovation, and an adaptive tracking algorithm based on current statistical model is proposed. Considering that the measurement equation is nonlinear under right anger coordinate, the debiased converted measurement kalman filter （DCMKF） is adapted to deal with the nonlinear tracked-target problem. The result of simulation shows that the fuzzy algorithm performs better than the conventional algorithm based on current statistical model does.
出处
《战术导弹技术》
2009年第5期56-61,共6页
Tactical Missile Technology
