基于粒子群优化算法的多因子自适应滤波

Multi Adaptive Kalman Filtering with Particle Swarm Optimization

在抗差多因子自适应滤波的基础上,提出基于粒子群优化智能算法进一步搜索自适应因子的优化值,提高自适应因子的可靠性。在基于状态不符值构造的自适应因子的基础上,构造适应性函数,采用粒子群优化算法搜索更有效的自适应多因子。利用动态导航数据进行验证,结果表明,基于粒子群优化的多因子自适应滤波能更有效地控制异常影响,提高动态导航精度。

The key problem of adaptive navigation is to determine the adaptive factors, in order to control the outlying effects of dynamic model errors. The optimal adaptive factors, however, are difficult to be obtained. On the base of multi adaptive robust Kalman filtering, a new kind of multi adaptive robust filtering, which uses particle swarm optimization to determine the factors, is proposed. The adaptive factors optimized by particle swarm optimization have higher reliability than those from current methods. First, multi adaptive factors are computed according to difference of the predicted state and calculated one then particle swarm optimization is employed to look for more accurate factors if the reasonable fitting function is chosen. An actual dynamic GPS data set is employed to test the new adaptive filtering procedure. It is shown that multi adaptive robust filtering with particle swarm optimization can control the influence of outliers more efficiently, and improve the accuracy of navigation.