基于改进衰减记忆滤波的组合导航算法研究

Research on Integrated Navigation Algorithm Based on Improved Fading-memory Filter

关于优化组合导航系统定位精度问题,由于惯导系统为非线性系统,存在滞后和噪声特性,影响系统定位精度,传统卡尔曼滤波器滤波一段时间后,系统预测误差方差阵逐渐趋于零,状态估计过分依赖旧量测值,从而导致滤波发散,系统定位精度差。目前采用在预测误差方差阵中引入标量衰减因子来抑制发散,但该标量因子是不变量,难以修正所有状态估计异常的情况。为有效提高新量测值对预测值的修正作用,研究了一种改进的衰减记忆滤波算法,通过引入可变加权系数来抑制发散。经数值仿真结果表明,新算法的滤波效果相比卡尔曼滤波和带标量因子的衰减记忆滤波有较明显的改善,提高了系统的定位精度,对工程应用有一定参考价值。

In integrated navigation system,as the model of the inertial navigation system is nolinear and the statistical characteristics of the noise is uncertain,the predicted covariance matrix gradually becomes zero in Kalman filter algorithm,and the predicted estimates brought by past measurement rises relatively,which will lead to filtering divergence and influence the positioning precision.To address the divergent problems of the Kalman filter,the predicted covariance matrix was pre-multiplied by a fading factor,which resulted in the fading-memory Kalman filter.But the modification brought by the scalar factor was limited.An improved fading-memory filter algorithm was studied to rise the predicted estimates brought by the new measurement.The scalar factor was replaced by a time-variant one.As a result,the effect of the new algorithm was validated by the simulation result and the positioning precision of integrated navigation system was improved,and it has practical reference value in engineering application.