Railway Track Recognition Based on Radar Cross-Section Statistical Characterization Using mmWave Radar
Abstract
:1. Introduction
- The RCS data set of track settlement detection under the actual scenario is constructed. The statistical feature extraction and analysis method based on RCS data set is proposed, and the multi-class DAG-SVM based recognition algorithm is applied to achieve accurate track recognition in a dynamic and static mixed multi-object scene.
- The ACZT algorithm is adopted to achieve the high-precision distance estimation between the track and radar for complex multi-target scenarios under the requirement of lower bandwidth 1.5 GHz, realizing the distance estimation accuracy of 0.5 mm.
- A complete and feasible high-precision detection method for track settlement is designed, and the practical experiment platform is established to verify the proposed method by using the common commercial-grade millimeter-wave radar. Simultaneously, the influence of different track condition on the detection performance has been investigated thoroughly.
2. Relatred Work
3. System Framework and Problem Formulation
4. Track Recognition Based on RCS Statistical Feature Data Set and Classifier
4.1. Definition and Influencing Factors of RCS
4.2. RCS Data Set Construction and Statistical Feature Extraction
4.3. Track Recognition Based on DAG-SVM and Decision Tree
5. Track Settlement Measurement Based on FMCW Technology with High Precision Target Distance Estimation
5.1. Basics of FMCW Radar Operation and Range Estimation
5.2. High Precision Distance Estimation Based on ACZT Algorithm
6. Experiment
6.1. Performance Evaluation of Railway Track Recognition Method
6.2. Performance Evaluation of Railway Track Settlement Measurement Method
6.2.1. Impact of the Distance between Radar and Track on Settlement Measurement
6.2.2. Impact of the SNR on Settlement Measurement
6.2.3. Impact of the Vibration on Settlement Measurement
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
FMCW | frequency-modulated continuous wave |
RCS | radar cross section |
DAG-SVM | directed acyclic graph-support vector machine |
FFT | fast Fourier transform |
SNR | signal to noise rate |
CRLB | Cramér–Rao lower bound |
ACZT | adaptive chirp-z-transform |
References
- Chen, J.; Zhou, Y. Dynamic Responses of Subgrade under Double-Line High-Speed Railway. Soil Dyn. Earthq. Eng. 2018, 110, 1–12. [Google Scholar] [CrossRef]
- Zou, J.; Zhu, Y.; Xu, Y.; Li, Q.; Meng, L.; Li, H. Mobile precise trigonometric levelling system based on land vehicle: An alternative method for precise levelling. Surv. Rev. 2017, 49, 249–258. [Google Scholar] [CrossRef]
- Liu, B.; Lu, Z.; Chen, L.; Kong, W.; Sui, X. Accuracy Analysis of the InSAR Altimeter in Relative Elevation Measurements of the Sea Surface. IEEE Access 2021, 9, 27783–27789. [Google Scholar] [CrossRef]
- Engels, G.; Aranjuelo, N.; Arganda, I.; Nieto, M.; Otaegui, O. 3D Object Detection from LiDAR Data using Distance Dependent Feature Extraction. arXiv 2020, arXiv:2003.00888. [Google Scholar]
- Li, L.; Cao, X.Y.; Sun, Q.H.J.; Jia, B.S.; Dong, X. A new 3D laser-scanning and GPS combined measurement system. Comptes Rendus Geosci. 2019, 351, 508–516. [Google Scholar]
- Chen, Y.F. Subgrade Settlement Monitoring System for High speed Railway Operation Line. Railw. Investig. Surv. 2017, 3, 28–31. [Google Scholar]
- Li, Y.; Zhang, W.; Tian, B.; Lin, W.; Liu, Y. Scattering Model-Based Frequency-Hopping RCS Reconstruction Using SPICE Methods. Remote Sens. 2021, 13, 3689. [Google Scholar] [CrossRef]
- Wu, Q.; Chen, J.; Lu, Y.; Zhang, Y. A Complete Automatic Target Recognition System of Low Altitude, Small RCS and Slow Speed (LSS) Targets Based on Multi-Dimensional Feature Fusion. Sensors 2019, 19, 5048–5060. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Patel, J.S.; Francesco, F.; David, A. Review of radar classification & RCS characterisation techniques for small UAVs or drones. IET Radar Sonar Navig. 2018, 12, 911–919. [Google Scholar]
- Sasakawa, D.; Honma, N.; Nakayama, T.; Iizuka, S. Human Activity Identification by Height and Doppler RCS Information Detected by MIMO Radar. IEICE Trans. Commun. 2019, 102, 1270–1278. [Google Scholar] [CrossRef]
- Lee, Y.; Choo, H.; Kim, S.; Kim, H. RCS based target recognition with real FMCW radar implementation. Microw. Opt. Technol. Lett. 2016, 58, 1745–1750. [Google Scholar] [CrossRef]
- Wang, T.; Bi, W.J.; Zhao, Y.L.; Xue, W.C. Radar target recognition algorithm based on RCS observation sequence—Set-valued identification method. J. Syst. Sci. Complex. 2016, 29, 573–588. [Google Scholar] [CrossRef]
- Lee, S.; Lee, B.; Lee, J.; Kim, S. Statistical Characteristic-Based Road Structure Recognition in Automotive FMCW Radar Systems. IEEE Trans. Intell. Transp. Syst. 2019, 20, 2418–2429. [Google Scholar] [CrossRef]
- Lee, J.; Kim, D.; Jeong, S.; Ahn, G.C.; Kim, Y. Target classification scheme using phase characteristics for automotive FMCW radar. Electron. Lett. Inst. Eng. Technol. 2016, 52, 2061–2063. [Google Scholar] [CrossRef]
- Hyun, E.; Jin, Y.S.; Hwang, S.O. Human-vehicle classification scheme using doppler spectrum distribution based on 2D range-doppler FMCW radar. J. Intell. Fuzzy Syst. 2018, 35, 6035–6045. [Google Scholar] [CrossRef]
- Ding, C.; Chae, R.; Wang, J.; Zhang, L.; Hong, H.; Zhu, X.; Li, C. Inattentive Driving Behavior Detection Based on Portable FMCW Radar. IEEE Trans. Microw. Theory Tech. 2019, 67, 4031–4041. [Google Scholar] [CrossRef]
- Ciattaglia, G.; Santis, A.; Disha, D.; Spinsante, S.; Castellini, P.; Gambi, E. Performance Evaluation of Vibrational Measurements through mmWave Automotive Radars. Remote Sens. 2021, 13, 98. [Google Scholar] [CrossRef]
- Herzel, F.; Kissinger, D.; Ng, H.J. Analysis of Ranging Precision in an FMCW Radar Measurement Using a Phase-Locked Loop. Circuits Syst. I Regul. Pap. IEEE Trans. 2017, 65, 783–792. [Google Scholar] [CrossRef]
- Yamaguchi, K.; Saito, M.; Akiyama, T.; Kobayashi, T.; Matsue, H. A 24 GHz band FMCW radar system for detecting closed multiple targets with small displacement. In Proceedings of the 2015 Seventh International Conference on Ubiquitous and Future Networks, Sapporo, Japan, 7–10 July 2015. [Google Scholar]
- Piotrowsky, L.; Jaeschke, T.; Kueppers, S.; Siska, J.; Pohl, N. Enabling High Accuracy Distance Measurements With FMCW Radar Sensors. IEEE Trans. Microw. Theory Tech. 2019, 67, 5360–5371. [Google Scholar] [CrossRef]
- Scherr, S.; Ayhan, S.; Fischbach, B.; Bhutani, A.; Pauli, M.; Zwick, T. An Efficient Frequency and Phase Estimation Algorithm With CRB Performance for FMCW Radar Applications. IEEE Trans. Instrum. Meas. 2015, 64, 1868–1875. [Google Scholar] [CrossRef]
- Scherr, S.; Afroz, R.; Ayhan, S.; Thomas, S.; Jaeschke, T.; Marahrens, S.; Bhutani, A.; Pauli, M.; Pohl, N.; Zwick, T. Influence of Radar Targets on the Accuracy of FMCW Radar Distance Measurements. IEEE Trans. Microw. Theory Tech. 2017, 65, 3640–3647. [Google Scholar] [CrossRef]
- Arab, H.; Dufour, S.; Moldovan, E.; Akyel, C.; Tatu, S.O. A 77-GHz Six-Port Sensor for Accurate Near-Field Displacement and Doppler Measurements. Sensors 2016, 18, 2565–2584. [Google Scholar] [CrossRef] [Green Version]
- Bhutani, A.; Marahrens, S.; Gehringer, M.; Göttel, B.; Pauli, M.; Zwick, T. The Role of Millimeter-Waves in the Distance Measurement Accuracy of an FMCW Radar. Sensors 2019, 19, 3938–3954. [Google Scholar] [CrossRef] [Green Version]
- Ioffe, A.; Doerr, W.; Yan, H.H.; Vu, D.H.; Arage, A.H. RCS characteristics of street curbs and the applications in automotive radar classification. In Proceedings of the 2016 European Radar Conference (EuRAD), London, UK, 5–7 October 2016. [Google Scholar]
- Ünaldı, S.; Bodur, H.; Çimen, S.; Çakır, G. RCS reduction of reflectarray using new variable size FSS method. AEU Int. J. Electron. Commun. 2020, 117, 153098. [Google Scholar] [CrossRef]
- Jing, S. Target identity recognition method based on RCS distribution and spatial location. Procedia Comput. Sci. 2019, 147, 632–637. [Google Scholar] [CrossRef]
- Liu, G.; Yang, C.; Liu, S.; Xiao, C.; Song, B. Feature Selection Method Based on Mutual Information and Support Vector Machine. Int. J. Pattern Recognit. Artif. Intell. 2021, 35, 2150021. [Google Scholar] [CrossRef]
- Chang, C.Y.; Chang, C.W.; Kathiravan, S.; Lin, C.; Chen, S.T. DAG-SVM based infant cry classification system using sequential forward floating feature selection. Multidim Syst. Sign Process 2017, 28, 961–976. [Google Scholar] [CrossRef]
- Zhang, C.; Qing, A.; Meng, Y. The Application of Chirp Z-Transform in Fast Computation of Antenna Array Pattern. Appl. Comput. Electromagn. Soc. J. 2019, 34, 1685–1693. [Google Scholar]
- Sampath, A.K.; Gomathi, D.N. Decision tree and deep learning based probabilistic model for character recognition. J. Cent. South Univ. 2017, 24, 2862–2876. [Google Scholar] [CrossRef]
- Hu, C.; Wu, Y.; Huang, L.; Yan, G. Unitary root-MUSIC based on tensor mode-R algorithm for multidimensional sinusoidal frequency estimation without pairing parameters. Multidim Syst. Sign Process 2020, 31, 491–501. [Google Scholar] [CrossRef]
- Lee, J.S.; Yoon, T.M.; Lee, K.B. Bearing fault detection of IPMSMs using zoom FFT. J. Electr. Eng. Technol. 2016, 11, 1235–1241. [Google Scholar] [CrossRef] [Green Version]
- Wang, H.K.; Cao, D.X.; Liu, Y.Q.; Wang, T. Vibration test analysis of the curved track model under train operation. Chin. J. Appl. Mech. 2020, 37, 701–706. [Google Scholar]
Algorithms | ACZT | RootMUSIC | ZoomFFT |
---|---|---|---|
Computation amounts |
Parameters | Value |
---|---|
Starting frequency (GHz) | 77 |
Bandwidth (GHz) | 1.5 |
TX Antenna Gain (dB) | 36 |
RX Antenna Gain (dB) | 42 |
SNR (dB) | 15 |
Number of TX Antennas used | 1 |
Number of RX Antennas used | 1 |
Vehicle speed(m/s) | 3.3 |
Number of mmwave radar ADC bits | 16-bits |
ADC sampling frequency | 5 MHz |
number of ADC samples collected during “ADC Sampling Time” | 512 |
Chirp number in a frame | 500 |
Total Chirp time(chirp time+idle time) (us) | 660 (100 + 560) |
Trainning Time | Prediction Speed | |
---|---|---|
DAG-SVM | 2.6799 s | 57,000 obs/s |
Decision Tree | 0.84401 s | 270,000 obs/s |
List | Problems and Possible Solutions |
---|---|
1 | P:Selection of base points and observation points |
S: Refer to manual measurement method | |
2 | P:Power supply |
S: Utilize solar energy and batteries | |
3 | P:Calibration of millimeter-wave radar |
S: Combination of automatic calibration and regular manual calibration | |
4 | P:Transfer of Settlement Detection Data |
S: Integration of Radar and Communication |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Li, S.; Ding, J.; Liu, W.; Li, H.; Zhou, F.; Zhu, Z. Railway Track Recognition Based on Radar Cross-Section Statistical Characterization Using mmWave Radar. Remote Sens. 2022, 14, 294. https://doi.org/10.3390/rs14020294
Li S, Ding J, Liu W, Li H, Zhou F, Zhu Z. Railway Track Recognition Based on Radar Cross-Section Statistical Characterization Using mmWave Radar. Remote Sensing. 2022; 14(2):294. https://doi.org/10.3390/rs14020294
Chicago/Turabian StyleLi, Shuo, Jieqiong Ding, Weirong Liu, Heng Li, Feng Zhou, and Zhengfa Zhu. 2022. "Railway Track Recognition Based on Radar Cross-Section Statistical Characterization Using mmWave Radar" Remote Sensing 14, no. 2: 294. https://doi.org/10.3390/rs14020294