摘要
针对"当前"统计模型中预先设置机动频率和加速度极限值造成对目标跟踪精度不高的问题,提出一种新的参数自适应算法。该算法利用目标前后2个时刻的加速度均值代替"当前"统计模型中只利用前一时刻的加速度值作为当前时刻的加速度均值,推导出了机动频率自适应,再利用加速度方差与加速度变化量之间存在的正比线性关系,推导出了加速度方差自适应,避免了由于参数设置不合理而造成的跟踪误差。理论分析和仿真结果表明,改进算法有效提高了目标跟踪精度,仿真结果验证了改进算法的有效性。
Aiming at the problem of the relatively low accuracy of the target tracking caused by pre-set limits of maneuvering frequency and acceleration in ＂current＂statistical model,the authors propose a new parameters adaptive algorithm.Firstly,the authors according to this approach,replace target＇s last acceleration value used in＂present＂statistical model with the mean acceleration value of target＇s two consecutive moments as the present mean acceleration,and develop maneuvering frequency adaptive; secondly,the authors deduce acceleration variance adaptive based on the proportional linear relationship between the acceleration variance and the variation of acceleration. This helps us avoid tracking error influenced by unsuitable parameter setting. Meanwhile,the theoretical analyses and simulations show that above algorithm effectively improves the target tracking accuracy,and the simulations test and verify the effectiveness of this algorithm.
出处
《重庆邮电大学学报（自然科学版）》
CSCD
北大核心
2015年第1期31-36,共6页
Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition)
基金
国家自然科学基金(61272043)~~
关键词
目标跟踪
“当前”统计模型
机动频率自适应
加速度方差自适应
target tracking
＂current＂statistical model
maneuvering frequency adaptive
acceleration variance adaptive
