针对卡尔曼滤波器对系统模型依赖性强、鲁棒性差和跟踪机动目标能力有限的问题，提出了一种新的利用混合模糊逻辑和标准卡尔曼滤波器的联合算法。
The Kalman filter has been commonly used in target tracking, however its performance may be degraded in presence of maneuver, low robustness and strong model dependence.
通过拟合实验数据表明，1：N自适应卡尔曼滤波器与快速傅里叶变换和标准加入法相结合，用于补偿系统的模型误差和进行重叠峰的分辨，效果良好。
The neopoloarographic synthetic data test indicated that 1 :N adaptive Kalman filter can compensate for modelling errors of system and resolve over- lapped response signals.
通过拟合实验数据表明，1：N自适应卡尔曼滤波器与快速傅里叶变换和标准加入法相结合，用于补偿系统的模型误差和进行重叠峰的分辨，效果良好。
The neopoloarographic synthetic data test indicated that 1 :N adaptive Kalman filter can compensate for modelling errors of system and resolve over- lapped response signals.
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