Kalman Filter-Based GNSS Integrity Monitoring

This paper evaluates the performance of an integrity monitoring algorithm of global navigation satellite systems (GNSS) for the Kalman filter (KF), termed KF receiver autonomous integrity monitoring (RAIM). The algorithm checks measurement inconsistencies in the range domain and requires Schmidt KF (SKF) as the navigation processor. First, realistic carrier-smoothed pseudorange measurement error models of GNSS are integrated into KF RAIM, overcoming an important limitation of prior work. More precisely, the error covariance matrix for fault detection is modified to capture the temporal variations of individual errors with different time constants. Uncertainties of the model parameters are also taken into account. Performance of the modified KF RAIM is then analyzed with the simulated signals of the global positioning system and navigation with Indian constellation for different phases of aircraft flight. Weighted least squares (WLS) RAIM used for comparison purposes is shown to have lower protection levels. This work, however, is important because KF-based integrity monitors are required to ensure the reliability of advanced navigation methods, such as multi-sensor integration and vector receivers. A key finding of the performance analyses is as follows. Innovation-based tests with an extended KF navigation processor confuse slow ramp faults with residual measurement errors that the filter estimates, leading to missed detection. RAIM with SKF, on the other hand, can successfully detect such faults. Thus, it offers a promising solution to developing KF integrity monitoring algorithms in the range domain. The modified KF RAIM completes processing in time on a low-end computer. Some salient features are also studied to gain insights into its working principles.

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Sequential Integrity Monitoring for Kalman Filter Innovations-based Detectors

Proceedings of the 31st International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2018) ◽

10.33012/2018.15975 ◽

2018 ◽

Cited By ~ 7

Author(s):

Cagatay Tanil ◽

Samer Khanafseh ◽

Mathieu Joerger ◽

Boris Pervan

Keyword(s):

Kalman Filter ◽

Integrity Monitoring

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Self-contained GPS integrity monitoring using a censored Kalman filter

10.31274/rtd-180813-8838 ◽

1988 ◽

Author(s):

Paul William McBurney

Keyword(s):

Kalman Filter ◽

Integrity Monitoring

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Integrity monitoring for Kalman filter-based localization

The International Journal of Robotics Research ◽

10.1177/0278364920960517 ◽

2020 ◽

Vol 39 (13) ◽

pp. 1503-1524

Author(s):

Guillermo Duenas Arana ◽

Osama Abdul Hafez ◽

Mathieu Joerger ◽

Matthew Spenko

Keyword(s):

Kalman Filter ◽

Time Window ◽

Satellite System ◽

Navigation Satellite ◽

Robot Localization ◽

Sensor Faults ◽

Prior Work ◽

Integrity Monitoring ◽

Integrity Risk ◽

Global Navigation Satellite

The problem of quantifying robot localization safety in the presence of undetected sensor faults is critical when preparing for future applications where robots may interact with humans in life-critical situations; however, the topic is only sparsely addressed in the robotics literature. In response, this work leverages prior work in aviation integrity monitoring to tackle the more challenging case of evaluating localization safety in Global Navigation Satellite System (GNSS)-denied environments. Localization integrity risk is the probability that a robot’s pose estimate lies outside pre-defined acceptable limits while no alarm is triggered. In this article, the integrity risk (i.e., localization safety) is rigorously upper bounded by accounting for both nominal sensor noise and other non-nominal sensor faults. An extended Kalman filter is employed to estimate the robot state, and a sequence of innovations is used for fault detection. The novelty of the work includes (1) the use of a time window to limit the number of monitored fault hypotheses while still guaranteeing safety with respect to previously occurring faults and (2) a new method to account for faults in the data association process.

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A Holistic Approach to Guarantee the Reliability of Positioning Based on Carrier Phase for Indoor Pseudolite

Applied Sciences ◽

10.3390/app10041199 ◽

2020 ◽

Vol 10 (4) ◽

pp. 1199 ◽

Cited By ~ 1

Author(s):

Yinzhi Zhao ◽

Jiming Guo ◽

Jingui Zou ◽

Peng Zhang ◽

Di Zhang ◽

...

Keyword(s):

Kalman Filter ◽

High Precision ◽

Carrier Phase ◽

Holistic Approach ◽

Indoor Positioning ◽

Test Statistics ◽

Error Sources ◽

Integrity Monitoring ◽

Dynamic Experiments ◽

Probability Of Missed Detection

The integrity monitoring algorithm based on pseudorange observations has been widely used outdoors and plays an important role in ensuring the reliability of positioning. However, pseudorange observations are greatly affected by the error sources such as multipath, clock drift, and noise in indoor pseudolite system, thus the pseudorange observations cannot be applied to high-precision indoor positioning. In general, double differenced (DD) carrier phase observations are used to obtain a high-precision indoor positioning result. What’s more, the carrier phase-based integrity monitoring (CRAIM) algorithm is applied to identify and exclude potential faults of the pseudolites. In this article, a holistic method is proposed to ensure the accuracy and reliability of positioning results. Firstly, if the reference pseudolite operates normally, extended Kalman filter is used for parameter estimation on the premise that the number of common pseudolites meets positioning requirements. Secondly, the innovation sequence in the Kalman filter is applied to construct test statistics and the corresponding threshold is determined from the Chi distribution with a given probability of false alert. The pseudolitehorizontal protection level (HPL) is calculated by the threshold and a prior probability of missed detection. Finally, compared the test statistics with the threshold to exclude the faultypseudolite for the reliability of positioning. The experiment results show that the proposed method improves the accuracy and stability of the results through faults detection and exclusion. This method ensures accuracies at the centimeter level for dynamic experiments and millimeter levels for static ones.

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