摘要
为避免由于系统噪声统计特性不准确所导致的滤波性能下降问题,改进了一种基于新息的系统噪声方差调整方法,并将其与扩展卡尔曼滤波、Unscented卡尔曼滤波和差分滤波相结合,形成自适应非线性卡尔曼滤波。将此方法应用到非线性测量光电跟踪系统中,并与采用基本非线性卡尔曼滤波进行性能对比。仿真实验结果证明该方法可以实时调整系统噪声方差,有效地避免由于系统噪声统计特性不准确所带来的滤波性能下降的问题,而且其性能明显优于基本非线性卡尔曼滤波。
A new system noise covariance modification algorithm is proposed in order to avoid the problem of degraded performance of the filter due to the incorrect statistics of the system noise. Combined with the Extended Kalman Filter （EFK）, Unscented Kalman Filter （UKF） and Divided Difference Filter （DDF）, adaptive nonlinear Kalman filters are developed. The algorithm is applied in nonlinear measurement electro-optical tracking system and the performances of the adaptive nonlinear Kalman filter is compared with the basic nonlinear Kalman filters. The Matlab simulation results show that the filter can modify system noise covariance in real time, efficiently avoid the above problem and the performance outperforms the basic nonlinear Kalman filters.
出处
《光电工程》
CAS
CSCD
北大核心
2008年第7期17-21,27,共6页
Opto-Electronic Engineering
基金
长春光机所三级创新项目(20070102)
