Locating and Grading of Lidar-Observed Aircraft Wake Vortex Based on Convolutional Neural Networks
<p>Information of experiments for wake vortex observation based on PCDL. (<b>a</b>) Location of ZUUU and ZSQD. (<b>b</b>) Sketch map of wake vortex observation experiments at ZUUU. (<b>c</b>) Sketch map of wake vortex observation experiments at ZSQD. (<b>d</b>) Lidar position and scanning mode of wake vortex observation experiments at ZUUU. (<b>e</b>) Lidar position and scanning mode of wake vortex observation experiments at ZSQD.</p> "> Figure 2
<p>Flow chart of the main methodology of aircraft wake vortex locating and grading algorithm.</p> "> Figure 3
<p>Images of wake vortex observed at ZSQD at 00:47 on 27 April 2020, LST, where the brighter color represents a larger value. (<b>a</b>) The pseudo-color images of radial velocity (RV). (<b>b</b>) The pseudo-color images of spectrum width (SW); (<b>c</b>) The gray-scale images of RV. (<b>d</b>) The gray-scale images of SW. (<b>e</b>) The stacked image of radial velocity and spectrum width (RV-SW).</p> "> Figure 4
<p>RV-SW images after marking the regions of the wake vortices with dashed boxes, where the brighter color represents a larger value. (<b>a</b>,<b>b</b>) RV-SW images with marked wake vortex regions at ZSQD. (<b>c</b>,<b>d</b>) RV-SW images with marked wake vortex regions at ZUUU.</p> "> Figure 5
<p>The structure of the WV locating model based on YOLO v4 with different color blocks representing different layers. The color blocks marked with * are the results of combining different layers, the structure of which are explained in the lower part of the diagram marked with *.</p> "> Figure 6
<p>The WV region data set after extracting the wake vortex feature from the RV-SW data set, where the brighter color represents a larger value. (<b>a</b>) Example of Grade 0. (<b>b</b>) Example of Grade 1. (<b>c</b>) Example of Grade 2. (<b>d</b>) Example of Grade 3.</p> "> Figure 7
<p>The structure of the deep learning model used for wake vortex grading based on WV region images with different color blocks representing different layers. The color blocks marked with * are the results of combining different layers, the structure of which are explained in the lower part of the diagram marked with *.</p> "> Figure 8
<p>The images and results of the wake vortex under stable meteorological condition. (<b>a</b>) Spectrum width image of the wake vortex. (<b>b</b>) Radial velocity image of the wake vortex. (<b>c</b>) Results of WV locating and grading models when applied on the wake vortex, where the brighter color represents a larger value.</p> "> Figure 9
<p>The images and results of the wake vortex under strong turbulence condition. (<b>a</b>) Spectrum width image of the wake vortex. (<b>b</b>) Radial velocity image of the wake vortex. (<b>c</b>) Results of horizontal location of the wake vortex when using the analytical algorithm, where the light blue line represents <math display="inline"><semantics> <mrow> <mi>S</mi> <mi>W</mi> <mo>_</mo> <mi>i</mi> <mi>n</mi> <mi>d</mi> </mrow> </semantics></math>, the light pink line represents <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>V</mi> <mo>_</mo> <mi>i</mi> <mi>n</mi> <mi>d</mi> </mrow> </semantics></math>, and the black line represents <math display="inline"><semantics> <mrow> <mi>S</mi> <mi>R</mi> <mo>_</mo> <mi>i</mi> <mi>n</mi> <mi>d</mi> </mrow> </semantics></math>, the red pentagram represents the location of the vortex core as identified by the algorithm. (<b>d</b>) Results of WV locating and grading models when applied on the wake vortex, where the brighter color represents a larger value.</p> "> Figure 10
<p>The confusion matrix of the WV grading model.</p> "> Figure 11
<p>The value of EDR<sup>1/3</sup> of typical dates at ZUUU and ZSQD.</p> ">
Abstract
:1. Introduction
2. Methodology
2.1. Experiments and Data
2.2. Wake Vortex Retrieval Algorithms
2.2.1. Data Pre-Processing
2.2.2. Wake Vortex Locating
2.2.3. Wake Vortex Grading
2.2.4. Evaluation Metrics
3. Results
3.1. Measurement Cases
3.1.1. Wake Vortex under Stable Meteorological Condition
3.1.2. Wake Vortex under Strong Turbulence Condition
3.2. Statistical Evaluation
3.2.1. Performance of WV Locating Model
- The WV locating model performs well when the training set and the test set are built based on the same data set. The F1-score and mAP of model 1 are 0.97 and 99.4% when applied to the ZUUU test set and 0.60 and 82.8% when applied to the ZSQD test set. It can be seen that model 1 has good performance on the ZUUU test set but does not perform well on the ZSQD test set. The Precision value of 97.8% means that most of the wake vortices identified using the CNN algorithm are consistent with the reference values in the data set, but the Recall value of 43.8% shows that more than half of the wake vortex cannot be identified.
- A new model can be obtained by adding data from a specified airport to build a new training set or continually training based on a current model, showing better performance at the specified airport. Model 3 was trained on the ZSQD data set based on model 1, and the Precision value decreases while the Recall value increases when applying to the ZSQD test set, meaning that the missing wake vortex images of the model are reduced, at the cost of a slight increase in the misidentification rate.
- The model trained on a mixed data set performs better on each test set compared to the model continually trained on the added data based on a current model. Although both model 3 and model 4 were trained based on all data of the two airports, model 4 trained on RV-SW data set 3 performed better with F1-scores of 0.98 and 0.93 and mAPs of 99.9% and 98.6% when applied on the ZUUU test set and ZSQD test set, respectively.
- Models trained on more diverse data sets have higher generalization ability. As mentioned above, the observation environment at ZSQD was more complicated than that at ZUUU, which means the data set established at the former airport is more diverse than the data set formed based on the latter. The F1-score and mAP of model 2 applied on the ZUUU test set are 0.88 and 89.7%, higher than the test results of model 1 applied on the ZSQD test set, for which the parameters are 0.60 and 82.8%.
3.2.2. Performance of WV Grading Model
4. Discussion
5. Conclusions
- A deep learning method for wake vortex locating and grading is presented based on stacked radial velocity and spectrum width images, obtained from FD raw data. Three data sets are built under different meteorological and topographical conditions (ZUUU and ZSQD airports) and used for the training and testing of four WV locating models, to evaluate model performances under different conditions. The WV grading model is trained and tested based on the ZUUU data set, to verify the effectiveness of the model.
- The WV locating models are trained based on different data sets, showing certain differences in the test sets. The performance of the models can be summarized as follows: The WV locating model performs well when the training set and the test set are built based on the same data set. A new model can be obtained by adding data from a specified airport to build a new training set or continually training based on a current model, showing better performance at the specified airport. The model trained on a mixed data set performs better on each test set compared to the model continually trained on the added data based on a current model. Models trained on more diverse data sets have higher generalization ability.
- The WV grading model is verified to be effective for the classification of WV region images. The removal of the non-WV region could amplify wake vortex features, to improve the accuracy of the model. According to the confusion matrix and statistical evaluation parameters, the predicted results are in good agreement with the wake vortex reference values, based on the analytical algorithm and empirical correction.
- The deep learning method in this paper has good performance not only when applied to the well-structured wake vortex but also to wake vortices under strong turbulence conditions, where the analytical algorithm cannot recognize the structure of the wake vortex. The deep learning algorithm and the analytical algorithm have been verified for consistency. The former exhibits better robustness and a faster processing speed of approximately 15 s.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameters | ZUUU | ZSQD |
---|---|---|
Landing | Take-Off | |
Scanning mode | RHI | RHI |
Scanning speed | ~1°/s | ~2°/s |
Azimuth angle | 90° | 260° |
Elevation angle range | 0~10° | 2~35° |
Elevation angle resolution | 0.2° | 0.4° |
Scanning duration | ~10 s | ~17 s |
Data Sets | Corresponding Model | Airport/No. |
---|---|---|
RV-SW data set 1 | WV locating model | ZUUU/1000 |
RV-SW data set 2 | WV locating model | ZSQD/1000 |
RV-SW data set 3 | WV locating model | ZUUU/1000 + ZSQD/1000 |
WV region data set | WV grading model | ZUUU/2000 |
Parameter | Value |
---|---|
Input size | 416 × 416 |
Classes | 1 |
Learning rate | 1 × 10−4 |
Batch size | 4 |
Iterations | 6400 |
Intensity Grade | Circulation Value | Number of WV |
---|---|---|
Grade 3 | More than 600 | 500 |
Grade 2 | 350~600 | 500 |
Grade 1 | 100~350 | 500 |
Grade 0 | Less than 100 or disturbances | 500 |
Parameter | Value |
---|---|
Input size | 96 × 96 |
Classes | 4 |
Learning rate | 5 × 10−4 |
Batch size | 8 |
Iterations | 4800 |
Model | Training and Validation Set | Test Set (Airport/No.) | Precision | Recall | F1 | mAP |
---|---|---|---|---|---|---|
1 | RV-SW data set 1(ZUUU) | ZUUU/200 | 99.5% | 94.1% | 0.97 | 99.4% |
ZSQD/200 | 97.8% | 43.8% | 0.60 | 82.8% | ||
2 | RV-SW data set 2 (ZSQD) | ZUUU/200 | 92.0% | 84.7% | 0.88 | 89.7% |
ZSQD/200 | 96.2% | 87.1% | 0.91 | 96.7% | ||
3 | ZUUU model + ZSQD data set | ZUUU/200 | 99.5% | 94.1% | 0.97 | 99.4% |
ZSQD/200 | 93.6% | 57.7% | 0.71 | 89.4% | ||
4 | RV-SW data set 3 (ZUUU + ZSQD) | ZUUU/200 | 99.0% | 97.5% | 0.98 | 99.9% |
ZSQD/200 | 98.3% | 87.6% | 0.93 | 98.6% |
Model | Data Set | Grade | Precision | Recall | F1 |
---|---|---|---|---|---|
WV grading model | WV region data set (ZUUU) | 0 | 99.0% | 98.0% | 0.98 |
1 | 97.9% | 95.0% | 0.96 | ||
2 | 98.0% | 100.0% | 0.99 | ||
3 | 97.0% | 96.0% | 0.96 |
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Zhang, X.; Zhang, H.; Wang, Q.; Liu, X.; Liu, S.; Zhang, R.; Li, R.; Wu, S. Locating and Grading of Lidar-Observed Aircraft Wake Vortex Based on Convolutional Neural Networks. Remote Sens. 2024, 16, 1463. https://doi.org/10.3390/rs16081463
Zhang X, Zhang H, Wang Q, Liu X, Liu S, Zhang R, Li R, Wu S. Locating and Grading of Lidar-Observed Aircraft Wake Vortex Based on Convolutional Neural Networks. Remote Sensing. 2024; 16(8):1463. https://doi.org/10.3390/rs16081463
Chicago/Turabian StyleZhang, Xinyu, Hongwei Zhang, Qichao Wang, Xiaoying Liu, Shouxin Liu, Rongchuan Zhang, Rongzhong Li, and Songhua Wu. 2024. "Locating and Grading of Lidar-Observed Aircraft Wake Vortex Based on Convolutional Neural Networks" Remote Sensing 16, no. 8: 1463. https://doi.org/10.3390/rs16081463