A New Method to Bound the Integrity Risk for Residual-Based ARAIM.
Peng Zhao1, Mathieu Joerger2, Xiao Liang3
1Arizona State University, Tempe, AZ, USA.
IEEE Transactions on Aerospace and Electronic Systems
|February 14, 2022
Summary
This study improves Advanced Receiver Autonomous Integrity Monitoring (ARAIM) by developing a tighter integrity risk bound for Residual-Based (RB) methods. The new approach accurately accounts for both fault and bias vectors, enhancing navigation safety.
Area of Science:
- Navigation Systems
- Aerospace Engineering
- Signal Processing
Background:
- Advanced Receiver Autonomous Integrity Monitoring (ARAIM) is crucial for modern navigation systems.
- Residual-Based (RB) methods are used for integrity risk bounding in ARAIM.
- Existing RB methods have limitations in accounting for worst-case nominal bias vectors.
Purpose of the Study:
- To develop a novel, tight integrity risk bound for Residual-Based (RB) Advanced Receiver Autonomous Integrity Monitoring (ARAIM).
- To address the limitations of previous methods by accounting for both fault and nominal bias vectors simultaneously.
- To improve the accuracy and reliability of integrity risk assessment in GNSS navigation.
Main Methods:
- Development of a new bounding method in parity space for RB-ARAIM.
- Derivation of a direct relationship between mean estimation error and the RB test statistic non-centrality parameter.
- Inclusion of both multi-satellite fault vectors and all-in-view nominal bias vectors in the bounding process.
Main Results:
- A tighter integrity risk bound for RB-ARAIM is established.
- The proposed method effectively accounts for worst-case scenarios involving both faults and nominal biases.
- Evaluated ARAIM performance demonstrates significant improvement over previous approaches.
Conclusions:
- The new parity space bounding method enhances the accuracy of integrity risk assessment in RB-ARAIM.
- This advancement contributes to safer and more reliable autonomous navigation systems.
- The findings provide a more robust framework for integrity monitoring in the presence of bounded errors and faults.
Related Concept Videos
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
190
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
190
01:10Propagation of Uncertainty from Systematic Error
967
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
967
01:15Systematic Error: Methodological and Sampling Errors
3.6K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
3.6K
01:17Design Example: Analyzing Capacity Contours for Flood Risk Assessment
131
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
131
00:59Propagation of Uncertainty from Random Error
1.2K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.2K
01:19Quality Assurance
257
Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
257