参数自主学习的车辆运动约束新模型及其惯性推算误差抑制分析

准确、连续、可靠的位置信息是车载导航应用的基础条件,在不增加额外传感器的前提下,集成GNSS与MEMS及车载CAN总线传感器,并融入车辆运动约束信息,是最为简单有效且低成本的车载多源导航方案。在车辆运动约束中,合理配置相关参数是约束条件能否充分发挥作用的关键,本文重点针对车辆非完整性约束,采用多元回归和深度学习方法,构建了参数自主学习的车辆运动约束模型。同时,提出了在观测域直接学习侧向/垂向速度参数的新思路,相比原有方差域调参方法具有更好的约束效果。实测分析表明,相比于方差域调整参数的传统方法,在观测域进行参数自主学习的新模型具有显著的精度提升,采用多元回归模型的惯性推算误差在水平位置上减小了69.6%~81.2%,而利用深度学习则减小了60.0%~77.3%,同时,水平相对定位精度分别改善了75.2%和65.0%,新模型能够有效提升GNSS失效时车载定位精度维持能力。

A new vehicle motion constraint model with parameter autonomous learning and analysis on inertial drift error suppression

Accurate, continuous and reliable location information is the basic condition for in-vehicle navigation applications. Under the premise of not adding other sensors, integrating GNSS, MEMS, on-board CAN sensors and vehicle motion constraint information is the most practical and low-cost vehicle multi-fusion navigation scheme. In the vehicle motion constraints, reasonable configuration of the relevant parameters is the key to making the constraints work fully. Thus, focusing on the vehicle non-integrity constraints, this article uses multiple regression and deep learning methods to build a new vehicle motion constraint model with parameter autonomous learning. Moreover, a new idea of directly learning lateral/vertical velocity parameters in the observation domain is proposed, which has better constraint effect than the old variance domain parameter adjustment method. The experiments show that compared with the traditional method of adjusting parameters in the variance domain, the new model with parameter autonomous learning in the observation domain has a significant improvement in accuracy. The inertial estimation error using the multivariate regression models is reduced by 69.6%~81.2% in the horizontal position, while the use of deep learning is reduced by 60.0%~77.3%. At the same time, the horizontal relative positioning accuracy is improved by 75.2% and 65.0% respectively, the new model can effectively improve the maintenance ability of vehicle positioning accuracy when GNSS failure.