GNSS辅助捷联惯导行进间对准自适应滤波方法

In-motion alignment adaptive filter method for GNSS-aided strap-down inertial navigation system

针对车载武器系统快速发射需求,提出一种基于GNSS辅助的捷联惯导行进间对准自适应滤波方法。该方法把行进间传递对准分为粗对准与精对准两个阶段。粗对准阶段以GNSS为观测基准完成对捷联惯导姿态的粗捕获,降低初始偏差不确定性对于精对准阶段的影响。在精对准阶段,考虑到车载系统的运动特性,提出一种"水平+方位"行进间对准双滤波器并行的设计思路,利用车载系统在不同时间段的动力学特性,对三轴姿态估计进行分时解耦,实现初始姿态的高精度估计;与此同时,引入协方差成形自适应调节过程,以最小化Frobenius范数为优化指标,实现对行进间对准卡尔曼滤波器的自适应调节,增强系统鲁棒性。数值仿真表明,协方差成形自适应卡尔曼滤波方法能够有效保证系统在全运动剖面内的稳定,结合双滤波器并行方案能够有效解决行进间对准精度不高与稳定性欠佳等问题,水平对准精度优于1.5′(1σ),方位对准精度优于6′(1σ)。

In view of the rapid-launch requirement for vehicle weapon system, an in-motion alignment adaptive filtering method for GNSS-aided strap-down inertial navigation system（SINS） is proposed. The in-motion transfer alignment is composed of two stage, i.e. coarse alignment and precise alignment. In the coarse alignment stage, the coarse acquisition of the SINS＇s attitude is accomplished by taking GNSS as the observation datum, which can reduce the effect of initial deviation uncertainty on the precise alignment stage. In the precise alignment stage, the horizontal and azimuth filters work in parallel to improve the attitude estimation accuracy using three-axis attitude decoupling in the process of vehicle system movement. Meanwhile, the covariance shaping process is introduced by taking the minimum Frobenius norm as the optimization index to realize the self-adaptive in-motion alignment Kalman filter and improve the robustness of the system. Numerical simulation shows that the double-filter parallel scheme with covariance shaping adaptive Kalman filtering can effectively solve such problems as poor stability and low alignment accuracy, and the alignment accuracies are increased to 1.5′（1σ, horizontal） and 6′（1σ, azimuth）.