A Hybrid Algorithm of LSTM and Factor Graph for Improving Combined GNSS/INS Positioning Accuracy during GNSS Interruptions
<p>Pre-integration schematic diagram.</p> "> Figure 2
<p>GNSS/INS Combined Positioning Factor Graph Model.</p> "> Figure 3
<p>LSTM-Assisted Model Structure Diagram.</p> "> Figure 4
<p>Basic LSTM Structure.</p> "> Figure 5
<p>Vehicle with navigation system equipment.</p> "> Figure 6
<p>Road Test Trajectory.</p> "> Figure 7
<p>Section 1 Trajectory Comparison.</p> "> Figure 8
<p>Section 1 East Error.</p> "> Figure 9
<p>Section 1 North Error.</p> "> Figure 10
<p>Section 2 Trajectory Comparison.</p> "> Figure 11
<p>Section 2 East Error.</p> "> Figure 12
<p>Section 2 North Error.</p> "> Figure 13
<p>Section 3 Trajectory Comparison.</p> "> Figure 14
<p>Section 3 Eastward Error.</p> "> Figure 15
<p>Section 3 Northward Error.</p> ">
Abstract
:1. Introduction
2. Information Fusion Methods Based on Factor Graphs
2.1. IMU Pre-Integration Factor Node
2.2. GNSS Factor Nodes
2.3. Factor Graph Model
3. LSTM Neural Network Prediction Assisted Positioning Method
3.1. Neural Network Prediction Model Description
3.2. LSTM Model
4. Experimental Validation and Analysis
4.1. Experimental Setup and Data Acquisition
4.2. Analysis of Experimental Results
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Parameter Name | Value |
---|---|
Learning Rate | 0.005 |
Learning Rate Decay Factor | 0.5 |
Number of Hidden Units | 100 |
Number of Epochs | 200 |
Optimizer | Adam |
Sensor | Parameters | Accuracy |
---|---|---|
IMU | Gyroscope Bias | 20°/hr |
Gyroscope Random Walk | ||
Accelerometer Bias | 50 mg | |
Sampling Frequency | 100 Hz | |
GNSS | Position Accuracy | 2 m |
Sampling Frequency | 1 Hz |
Section | Start Time (s) | Interruption Duration (s) | Section | Start Time (s) | Interruption Duration (s) |
---|---|---|---|---|---|
2 | 1765 | 6 | 3 | 2696 | 17 |
2 | 1928 | 5 | 3 | 2758 | 9 |
2 | 1944 | 7 | 3 | 2813 | 2 |
2 | 1968 | 7 | 3 | 2897 | 2 |
2 | 2240 | 5 | 3 | 2985 | 5 |
2 | 2283 | 4 | 3 | 3002 | 27 |
2 | 2416 | 9 | 3 | 3038 | 6 |
3 | 2525 | 4 | 3 | 3085 | 6 |
3 | 2545 | 6 | 3 | 3109 | 6 |
3 | 2598 | 3 | 3 | 3147 | 4 |
3 | 2612 | 15 | 3 | 3215 | 8 |
3 | 2628 | 2 | 3 | 3231 | 4 |
3 | 2635 | 8 |
Section | Algorithm | Orientation | RMSE (m) | Maximum Error (m) |
---|---|---|---|---|
1 | LSTM-PI-FGO | east | 0.27 | 1.31 |
north | 0.33 | 1.41 | ||
FGO | east | 0.69 | 2.74 | |
north | 0.78 | 1.98 | ||
2 | LSTM-PI-FGO | east | 0.27 | 0.90 |
north | 0.37 | 1.19 | ||
FGO | east | 0.73 | 2.81 | |
north | 1.10 | 4.46 | ||
3 | LSTM-PI-FGO | east | 0.79 | 3.41 |
north | 0.79 | 3.89 | ||
FGO | east | 2.40 | 16.08 | |
north | 2.16 | 21.02 |
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Liu, F.; Zhao, H.; Chen, W. A Hybrid Algorithm of LSTM and Factor Graph for Improving Combined GNSS/INS Positioning Accuracy during GNSS Interruptions. Sensors 2024, 24, 5605. https://doi.org/10.3390/s24175605
Liu F, Zhao H, Chen W. A Hybrid Algorithm of LSTM and Factor Graph for Improving Combined GNSS/INS Positioning Accuracy during GNSS Interruptions. Sensors. 2024; 24(17):5605. https://doi.org/10.3390/s24175605
Chicago/Turabian StyleLiu, Fuchao, Hailin Zhao, and Wenjue Chen. 2024. "A Hybrid Algorithm of LSTM and Factor Graph for Improving Combined GNSS/INS Positioning Accuracy during GNSS Interruptions" Sensors 24, no. 17: 5605. https://doi.org/10.3390/s24175605