一种车辆模型辅助的MEMS-SINS导航方法

针对城市环境中GNSS因遮挡导致MEMS-SINS精度快速降低的问题,在车辆运动学约束的基础上,结合四通道ABS轮速传感器和方向盘转角信息,提出一种新的适用于陆地车辆的MEMS-SINS导航方法。该方法通过分析车辆转弯和运动约束特性,构建角速度和加速度观测量,从而实现基于模型辅助的MEMS误差在线补偿;其次,ABS轮速信息与非完整约束条件结合可额外增加三维车体速度观测量,进一步维持卫星失效时组合滤波器的量测更新。跑车实验表明,在GNSS信号频繁丢失甚至长时间无法定位时,低精度MEMS惯性器件引起的快速误差积累得到有效抑制,与经典车体约束结合里程计算法相比,航向精度提高约70%,位置、速度精度也有相应的提高,验证了算法的有效性。

MEMS-SINS navigation method aided by vehicle model

In view that the frequent outages of GNSS in urban environments can quickly degrade the performance of MEMS-SINS,a new MEMS-SINS navigation method for land vehicles is proposed based on the vehicle constraints and combined with the four-channel ABS wheel speed sensors and steering angle information.By analyzing the vehicle turning and constraint characteristics,the angular velocity and acceleration are constructed as the measurements to achieve on-line compensation for MEMS's rapid drifting errors.Three-dimension vehicle-body velocity provided by ABS information and non-holonomic constraint is applied to further maintain the update of the integration Kalman filtering during GNSS outages.The road-test results demonstrate the proposed method can effectively reduce the rapid accumulation errors of SINS due to low-cost MEMS inherent bias in the circumstances of long-time outrages of GNSS.Compared with conventional body velocity constraint and odometer algorithm,the heading accuracy is improved by 70％,and the accuracies of position and velocity are also improved.