A soft-failure detection and identification algorithm for the integrated navigation system of lunar lander

Kang, Chul Woo; Park, Chan Gook

doi:10.1177/0954410015619444

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Position estimation accuracy from multi-IMU approaches can significantly exceed the single IMU case, as indicated by [3], which compared satellite launcher position estimation and showed the three IMU case reduced error by 54% relative to the single IMU case. [4] applied the [3] framework to an integrated navigation system that includes a traditional inertial navigation system (INS) with auxiliary IMU sensors.…”

Section: Previous Workmentioning

confidence: 99%

Multi-IMU Based Alternate Navigation Frameworks: Performance & Comparison for UAS

Patel

Faruque

2022

IEEE Access

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On-board state estimation is a persistent challenge to fielding unmanned aerial systems (UAS), particularly when global positioning system (GPS) measurements are not available. The dominant estimation tool remains the extended Kalman filter (EKF) applied to an inertial measurement unit (IMU). The growing availability of low-cost commercial-grade IMUs raises questions about how to best improve sensor readings into an estimate when measurements are available from multiple IMUs. This paper evaluates four different approaches to attitude estimation from multiple IMU measurements and their application in high dynamic motion. The four approaches are fusion of measurements (virtual IMU), fusion of state estimates (Federated KF), feedback fusion state estimate (Feedback Federated KF), and an EKF design incorporating the additional measurements (Augmented KF); these correspond to fusion before, within, or after state estimation. The performance of the approaches are quantified for varying IMU number theoretically and experimentally. The experiments use on-board autopilot hardware implementations of the estimators during maneuvers in a motion capture volume and flights on-board an Unmanned Aerial Systems (UAS) implementation, with the peak and root-mean-square (RMS) errors used to quantify accuracy. The RMS error results indicate that the feedback federated Kalman Filter using five IMUs returns 38% compared to general federated Kalman Filter using 37% accuracy improvement over a single IMU. This improvement compares to a 19% improvement for virtual IMU and 9% improvement for the Augmented KF respectively. These results indicate that both the Federated KF approach achieves the lowest RMS error relative to the virtual IMU and augmented KF approaches and inform the design of multi-IMU UAS pose estimators.

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Section: Previous Workmentioning

confidence: 99%

Multi-IMU Based Alternate Navigation Frameworks: Performance & Comparison for UAS

Patel

Faruque

2022

IEEE Access

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“…There are several feature selection approaches available such as information gain, mutual information, and the chi-square test [4][5][6]. Among them, the chi-square, also written as χ 2 , test statistic can be also used for abnormal signal detection [7,8]. The chi-square test statistic is developed from the difference between two variables such as the original state and its filtered estimate, and then compared with a precomputed threshold to detect abnormal signals.…”

Section: Introductionmentioning

confidence: 99%

Selective Finite Memory Structure Filtering Using the Chi-Square Test Statistic for Temporarily Uncertain Systems

Kim

2019

Applied Sciences

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In this paper, a finite memory structure (FMS) filtering with two kinds of measurement windows is proposed using the chi-square test statistic to cover nominal systems as well as temporarily uncertain systems. First, the simple matrix form for the FMS filter is developed from the conditional density of the current state given finite past measurements. Then, one of the two FMS filters, the primary FMS filter or the secondary FMS filter, with different measurement windows is operated selectively according to the presence or absence of uncertainty, to obtain a valid estimate. The primary FMS filter is selected for the nominal system and the secondary FMS filter is selected for the temporarily uncertain system, respectively. A declaration rule is defined to indicate the presence or absence of uncertainty, operate the suitable one from two filters, and then obtain the valid filtered estimate. A test variable for the declaration rule is developed using a chi-square test statistic from the estimation error and compared to a precomputed threshold. In order to verify the proposed selective FMS filtering and compare with the existing FMS filter and the infinite memory structure (IMS) filter, computer simulations are performed for a selection of dynamic systems including a F404 gas turbine aircraft engine and an electric motor. Simulation results confirm that the proposed selective FMS filtering works well for nominal systems as well as temporarily uncertain systems. In addition, the proposed selective FMS filtering is shown to be remarkably better than the IMS filtering for the temporarily uncertain system.

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A Minor Fault Detection Algorithm Based on Sliding-Window Accumulated Parity Vector

Wang

Liu

et al. 2018

Lecture Notes in Electrical Engineering

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A soft-failure detection and identification algorithm for the integrated navigation system of lunar lander

Cited by 3 publications

References 18 publications

Multi-IMU Based Alternate Navigation Frameworks: Performance & Comparison for UAS

Multi-IMU Based Alternate Navigation Frameworks: Performance & Comparison for UAS

Selective Finite Memory Structure Filtering Using the Chi-Square Test Statistic for Temporarily Uncertain Systems

A Minor Fault Detection Algorithm Based on Sliding-Window Accumulated Parity Vector

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