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首页> 外文会议>ASME international design engineering technical conferences and computers and information in engineering conference 2014 >ENHANCED FULL-STATE ESTIMATION AND DYNAMIC-MODEL-BASED PREDICTION FOR ROAD-VEHICLES
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ENHANCED FULL-STATE ESTIMATION AND DYNAMIC-MODEL-BASED PREDICTION FOR ROAD-VEHICLES

机译：道路车辆的增强全态估计和基于动态模型的预测

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摘要

In this paper, we address the enhanced state estimation and prediction system for automobile applications by fusing relatively low-cost and noisy Inertial Navigation System (INS) sensing with Global Positioning System (GPS) measurements. An unscented Kalman filter is used to merge multi-rate measurements from GPS and INS sensors together with a high-fidelity vehicle-dynamics model for state-predictions. The high-fidelity motion model (including suspension-effects) for the vehicle motion trajectory on uneven terrain is captured by a 20-state system of nonlinear differential equations. Computer simulation results illustrate the effectiveness of sensor-fusion (building upon the merger of an inexpensive INS sensing with GPS based measurements) to accurately estimate the full system-state. The relative ease of implementation, accuracy and predictive performance with low-cost sensing will facilitate its use in various electronic control and safety-systems, such as Electronic Stability Program, Anti-lock Brake Systems, and the Lateral Dynamic Stability Control.
机译：在本文中，我们通过将成本相对较低且噪声较大的惯性导航系统（INS）传感与全球定位系统（GPS）测量相融合，来解决汽车应用中的增强状态估计和预测系统问题。一个无味的卡尔曼滤波器用于合并来自GPS和INS传感器的多速率测量值以及用于状态预测的高保真车辆动力学模型。在20个状态的非线性微分方程系统中，捕获了不平坦地形上车辆运动轨迹的高保真运动模型（包括悬架效应）。计算机仿真结果说明了传感器融合的有效性（基于廉价的INS传感与基于GPS的测量的结合），可以准确估算整个系统状态。具有低成本感测的相对容易实现，准确性和预测性能将促进其在各种电子控制和安全系统中的使用，例如电子稳定程序，防抱死制动系统和横向动态稳定控制。

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