Backscattering Analysis at ATR on Rough Surfaces by Ground-Based Polarimetric Radar Using Coherent Decomposition
Abstract
:1. Introduction
1.1. Problem and Tasks
- Select an object decomposition theorem for feature space design and test an acceptable scattering mechanism after calibration
- Conduct data calibration considering the effect of rough ground surface on object recognition
- Explore supervised learning as an ATR tool for polarimetric radar.
1.2. Structure of the Article
2. Theoretical Part
2.1. Assumptions
- Kennaugh matrix dichotomy (Kennaugh–Huynen scattering matrix)
- Decomposition of the covariance matrix (Freeman and Durden)
- Coherent scattering matrix decomposition (Pauli, Krogager, Cameron).
- The transmitted electromagnetic wave is a plane monochromatic wave with constant frequency, amplitude, and initial phase in time.
- In the propagation of a polarized wave, there are additive and multiplicative interferences.
- Polarimetric radar generates a signal with strict linear (vertical and horizontal) and strict circular (right and left) polarization.
- The classification procedure involves obtaining labeled data on detected objects; hence it is a supervised learning task.
2.2. Polarized Wave Model
- The Jones calculus. This is a mathematical description of a fully polarized wave in which the Jones vectors and linear elements of the Jones matrix (Equation (1)) determine the polarization:
- Mueller calculus. A mathematical description of arbitrarily polarized scattering is given by the Stokes vector, which is expressed as follows:
- The Mueller calculus has only a phenomenological interpretation and is not related to the electromagnetic theory, whereas the Jones calculus derives directly from this theory.
- The Jones calculus allows for the absolute phase, while the Mueller calculus does not consider the phase at all.
- The elements of the Jones matrix correspond to the radiation amplitude, while the elements of the Muller matrix are related to the scattering intensity.
3. Polarimetric Decomposition of Feature Space
3.1. Pauli Decomposition
3.2. Krogager Decomposition
3.3. Cameron Decomposition
3.4. Assessment of Feature Space for the Learning Algorithm
4. Data and Calibration
- Linear polarization (vertical and horizontal plane)
- Circular polarization (right and left rotation).
4.1. Linear Polarization Data
4.2. Circular Polarization Data
5. Supervised Learning for Polarimetric Recognition
5.1. Modeling Polarimetric Recognition
5.2. Comparison with Similar Methods
6. Results and Discussion
6.1. Influence of Different Target Profiles and Weather Conditions
6.2. Estimation of Binary Classifiers
7. Conclusions
Funding
Conflicts of Interest
Appendix A. The Coefficients Chart Depending on the Profile (Truck)
Appendix B. Confusion Matrix of the True Positive Rate of Classification
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Parameter | Value |
---|---|
Distance to target | 150 m |
Sight angle | flat |
Angles of profile | 360° (periodicity 15°) |
Radar resolution (range) | 0.5 m |
Radar resolution (azimuth) | 1.8° |
Time registration | 10 ms |
Type of Polarization | F1 Score | MCC |
---|---|---|
Linear (Car) | 0.6282 | 0.3775 |
Linear (Truck) | 0.7206 | 0.3775 |
Circular (Car) | 0.2667 | 0.1260 |
Circular (Truck) | 0.6667 | 0.1260 |
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Kvasnov, A.V. Backscattering Analysis at ATR on Rough Surfaces by Ground-Based Polarimetric Radar Using Coherent Decomposition. Sensors 2023, 23, 3614. https://doi.org/10.3390/s23073614
Kvasnov AV. Backscattering Analysis at ATR on Rough Surfaces by Ground-Based Polarimetric Radar Using Coherent Decomposition. Sensors. 2023; 23(7):3614. https://doi.org/10.3390/s23073614
Chicago/Turabian StyleKvasnov, Anton V. 2023. "Backscattering Analysis at ATR on Rough Surfaces by Ground-Based Polarimetric Radar Using Coherent Decomposition" Sensors 23, no. 7: 3614. https://doi.org/10.3390/s23073614