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首页> 外文期刊>Journal of surveying engineering >Land-Vehicle INS/GPS Accurate Positioning during GPS Signal Blockage Periods
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Land-Vehicle INS/GPS Accurate Positioning during GPS Signal Blockage Periods

机译：GPS信号阻塞期间的陆上INS / GPS精确定位

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In the last decade, the demand for accurate land-vehicle navigation (LVN) in several applications has grown rapidly. In this context, the idea of integrating multisensor navigation systems was implemented. For LVN, the most efficient multisensor configuration is the system integrating an inertial navigation system (INS) and a global positioning system (GPS), where the GPS is used for providing position and velocity and the INS for providing orientation. The optimal estimation of the system errors is performed through a Kalman filter (KF). Unfortunately, a major problem occurs in all INS/GPS LVN applications that is caused by the frequent GPS signal blockages. In these cases, navigation is provided by the INS until satellite signals are reacquired. During such periods, navigation errors increase rapidly with time due to the time-dependent INS error behavior. For accurate positioning in these cases, some approaches, known as bridging algorithms, should be used to estimate improved navigation information. In this paper, the main objective is to improve the accuracy of the obtained navigation parameters during periods of GPS signal outages using different bridging methods. As a first step, three different KF approaches will be used, including the linearized, extended, and unscented KF algorithms for the INS/GPS integration. Two land-vehicle kinematic data sets with different-quality INSs are used with several induced GPS outages, and then two bridging approaches are implemented. The first method is to apply different backward smoothing algorithms postmission that are associated with the different used KF approaches. The second bridging method is a near real-time approach based on developing an INS error model to be applied only during GPS signal blockages. After applying each bridging method, the results showed remarkable improvement of position errors regardless of the KF used.
机译：在过去的十年中，在几种应用中对精确的陆地车辆导航（LVN）的需求迅速增长。在这种情况下，实现了集成多传感器导航系统的想法。对于LVN，最有效的多传感器配置是集成了惯性导航系统（INS）和全球定位系统（GPS）的系统，其中GPS用于提供位置和速度，而INS用于提供方向。系统误差的最佳估计是通过卡尔曼滤波器（KF）进行的。不幸的是，由于频繁的GPS信号阻塞，在所有INS / GPS LVN应用中都会出现一个主要问题。在这些情况下，INS将提供导航，直到重新获取卫星信号为止。在此期间，由于时间相关的INS错误行为，导航错误随时间迅速增加。为了在这些情况下进行精确定位，应使用一些称为桥接算法的方法来估计改进的导航信息。本文的主要目的是使用不同的桥接方法来提高GPS信号中断期间获得的导航参数的准确性。第一步，将使用三种不同的KF方法，包括用于INS / GPS集成的线性化，扩展和无味KF算法。将两个具有不同质量INS的陆地车辆运动学数据集与几次GPS中断一起使用，然后实施两种桥接方法。第一种方法是应用与不同使用的KF方法相关联的不同后向平滑算法postmission。第二种桥接方法是基于实时INS误差模型的近实时方法，该误差模型仅适用于GPS信号阻塞。应用每种桥接方法后，无论使用何种KF，结果均显示出位置误差的显着改善。

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