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Integrity Monitoring for All-Source Navigation Enhanced by Kalman Filter-Based Solution Separation

机译：基于Kalman滤波器的解决方案分离增强了全源导航的完整性监控

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摘要

Integrity is a popular and effective index as a measure of trust for navigation system to place in the correct position. The classical snapshot-based integrity monitoring methods have a widely and mature application in global navigation satellite system (GNSS) assessment. However, they cannot meet the integrity evaluation requirements for multi-sensor integration such as all-source navigation due to its recursive estimation and measurement diversity of sensors, which directly limits it's use in safety-critical applications. We propose a new Kalman filter based solution separation (KFSS) method for the integrity monitoring of multi-sensor integrated navigation systems. The traditional EKF update estimation is remodeled as a weighted least square form to involve the system propagation into the new measurement vector, which reconstructed as a 'pseudo-snapshot' model. The integrity risk caused by the system propagation is considered as one fault hypothesis in the following fault detection and protection level determination. Then, the integrity evaluation is executed in positioning domain enhanced by solution separation with sensor exclusion. The above two operations have indispensable roles and inseparable relationship from the aspect of integrity functional realization. The performance of a tightly coupled integration simulation, a loosely coupled multi-sensor integration simulation and an actual kinematic vehicle experiment verified the feasibility and superiority of the proposed method. The KFSS structure can detect fault in propagation period and step fault, ramp fault and simultaneous faults in observations effectively. The protection levels can be reduced positively both in horizontal and vertical directions, which is positive to bound the position error more accurately and reduce the redundant space effectively. It is of great significance for tighter integrity requirements.
机译：完整性是一种流行且有效的指数，作为导航系统的信任衡量标准，放置在正确的位置。基于古典快照的完整性监控方法在全球导航卫星系统（GNSS）评估中具有广泛和成熟的应用。但是，由于其递归估计和传感器的测量分集，它们不能满足多传感器集成的完整性评估要求，例如传感器的递归估计和测量分集，直接限制它在安全关键型应用中的使用。我们提出了一种新的基于卡尔曼滤波器的解决方案分离（KFSS）方法，用于多传感器集成导航系统的完整性监控。传统的EKF更新估计被重新改造为加权最小二乘形式，以涉及系统传播到新的测量向量中，该测量向量被重建为“伪快照”模型。系统传播引起的完整性风险被认为是以下故障检测和保护级别确定的一个故障假设。然后，通过用传感器排除的解决方案分离，在定位域中执行完整性评估。以上两项操作具有不可或缺的作用和与完整性功能实现的方面不可分割的关系。紧密耦合的集成模拟的性能，松散耦合的多传感器集成模拟和实际运动车辆实验验证了所提出的方法的可行性和优越性。 KFSS结构可以有效地检测传播周期和步骤故障，斜坡故障和同时断层的故障。保护水平可以在水平和垂直方向上肯定地减小，这对于更准确地结合位置误差并有效地降低冗余空间。对于更严格的完整性要求，这具有重要意义。

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