L1-Constrained Fractional-Order Gradient Descent for Axial Dimension Estimation of Conical Targets
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a novel size estimation method using High-Range Resolution Profiles (HRRPs) and L1-norm gradient descent. It accurately determines target size in complex environments, reducing outlier sensitivity and improving recognition performance.
Area Of Science
- Electromagnetic scattering
- Target recognition
- Signal processing
Background
- High-Range Resolution Profiles (HRRPs) contain crucial structural information for target recognition.
- Accurate size estimation is vital for effective target identification in complex electromagnetic environments.
- Traditional methods often struggle with outlier sensitivity and computational efficiency.
Purpose Of The Study
- To develop a robust and efficient size estimation method for ballistic targets using HRRPs.
- To improve target recognition performance by accurately inverting actual target size from projected size.
- To overcome the limitations of traditional L2-norm methods in handling noisy data.
Main Methods
- Established an analytical relationship model between projected and actual target size using geometrical optics.
- Constructed an L1-norm error function for robust optimization.
- Employed an adaptive order-adjusting fractional-order gradient descent with dynamic order-switching for improved computational efficiency and accuracy.
Main Results
- The proposed method achieves accurate size inversion in complex electromagnetic environments.
- Demonstrated reduced sensitivity to outliers compared to traditional L2-norm methods.
- Maintained actual size inversion error below 3.7% even with a 0.4 m measurement error.
Conclusions
- The L1-norm variable fractional-order gradient descent method offers a significant advancement in HRRP-based target size estimation.
- The method provides a robust and computationally efficient solution for target recognition in challenging conditions.
- This approach enhances the utilization of structural information from HRRPs for improved performance.
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