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Refinements of the Kalman Estimates for the Position and Velocity of a Vehicle Obtained with GPS Using Inertial Navigation System's Measurements: A Comparative Analysis

机译:使用惯性导航系统测量,使用GPS获得的车辆的位置和速度的皮卡尔曼的改进:比较分析

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Currently, the use of Global Navigation Satellite Systems-GNSS has been widely disseminated for the most different applications, from the aeronautical navigation to the car traffic, being the Global Positioning System-GPS the most used system for such objectives. New applications have presented challenges in terms of the main requirements associated to such systems, namely: precision, reliability, availability, continuity and integrity. It is because proposed solutions, such as satellite or ground-based augmentation systems, depend on signals provided by the GNSS satellite constellation. It constitutes a limitation for using such systems for position and velocity estimations. On other hand, Inertial Navigation Systems-INS, being independent of external signals, have a big potential to be applied on these circumstances; furthermore, they present characteristics that may be considered complementary to the GNSS. In this work we study refinements of the Kalman estimates for the position and velocity of a vehicle obtained with GPS using Inertial Navigation System's measurements. For that, we: 1- perform simulation of a vehicle movement using position and velocity estimates obtained from a GNSS constellation; 2- perform the simulation of a vehicle movement using accelerometer data being updated with measurements obtained from a GNSS constellation by a Kalman Filter, with uncertainties from the accelerometers and GNSS data modeled as stochastic Gaussian processes; and 3- compare the results of the simulation, discussing the possible advantages and disadvantages of the application of GNSS-INS integration techniques. We expect to show: 1- the difficulties to tuning the Kalman Filter to obtain a behavior of convergence; 2- the limitations of using GPS data without integration or Navigation; and 3- the use of accelerometer data integrated to GPS contributes for the improvement of onboard navigation systems.
机译:目前,使用全球导航卫星系统-GNSS广泛传播,从航空航行到汽车流量,是全球定位系统-GPS为这种目标的最常用的系统。新应用程序在与此类系统相关的主要要求方面提出了挑战,即:精度,可靠性,可用性,连续性和完整性。它是因为提出的解决方案,例如卫星或地面增强系统,取决于GNSS卫星星座提供的信号。它构成使用这种系统的位置和速度估计的限制。另一方面,惯性导航系统,独立于外部信号,具有很大的应用才能应用于这些情况;此外,它们存在可能被认为与GNS互补的特征。在这项工作中,我们使用惯性导航系统的测量研究使用GPS获得的车辆的位置和速度的卡尔曼估计的改进。为此,我们使用从GNSS星座获得的位置和速度估计来执行车辆运动的模拟; 2-使用由卡尔曼滤波器从GNSS星座获得的测量进行更新的加速度计数据进行车辆运动的模拟,其中来自加速度计的不确定性,并且GNSS数据建模为随机高斯过程; 3-比较模拟结果,讨论了GNSS-INS集成技术的应用的可能性和缺点。我们希望显示:1-调整卡尔曼滤波器的困难以获得融合的行为; 2-使用GPS数据而不集成或导航的限制; 3-使用集成到GPS的加速度计数据有助于改进车载导航系统。

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