摘要
在外弹道数据处理中,奇异点处理、特征点求取与随机误差削弱都是精度估计的关键环节.本文首先利用小波变换在处理奇异点、特征点、噪声消除方面的优势,对观测数据进行基于小波变换的分解、融合、重构处理,剔除奇异点,查找特征点,削弱随机误差.其次利用节点自由分布B样条描述导弹运动轨迹,使该弹道确定方法转化为关于求解导弹轨道样条表示参数和测量系统误差的多模融合的非线性优化问题,采用非线性最优化方法,进而得到待估参数的最优估计,完成弹道的最佳逼近.仿真结果表明,该技术应用在奇异点处理、特征点提取与随机误差削弱方面效果较好,多模融合算法能减少计算量,且能切实提高参数估计精度.
Singularity disposing, salient point gaining and random error weakening are key processes for estimating precision during the data processing for ballistic trajectory. Firstly, the predominance of wavelet transform on dealing with singularity, salient point and random error, was used to conduct a decomposition, fusion and reconstitution of the observation data base on wavelet transform , which can eliminate singularity, identify salient point and weaken random error. Secondly, the B-spine function with knots free distributing was used to describe the missile movement state, which makes the principle of the trajectory determination process become a nonlinear optimization problem of multi model fusion with the parameters of missile trajectory expression coefficients and observation system error coefficients. By introducing the model structure to determinate the selection rule of optimal fusion weight, the optimal estimation for the to-be estimated parameters was obtained, and more the precise trajectory determination was gained. Finally, the simulation calculation shows that this method has some advantages in disposing singularity, gaining salient point and weakening random error, which can reduce the calculation amount and increase the ultimate precision of trajectory determination effectively.
出处
《动力学与控制学报》
2013年第2期182-186,共5页
Journal of Dynamics and Control
关键词
小波变换
样条分频
信息重构
数据融合
wavelet transform
spine frequency division
information reconstruction
data fusion
