학술논문

Object Recognition With Natural Resonance Annihilation Using New N-Pulse Waveforms and Kernel Density Discrimination Measure
Document Type
Periodical
Source
IEEE Transactions on Antennas and Propagation IEEE Trans. Antennas Propagat. Antennas and Propagation, IEEE Transactions on. 72(3):2686-2696 Mar, 2024
Subject
Fields, Waves and Electromagnetics
Aerospace
Transportation
Components, Circuits, Devices and Systems
Resonant frequency
Feature extraction
Notch filters
Libraries
Finite impulse response filters
IIR filters
Fitting
Curve fitting
electromagnetic scattering
feature extraction
Gaussian noise
infinite impulse response (IIR) digital filters
natural resonances
notch filters
polarization
probability
radar target recognition
Language
ISSN
0018-926X
1558-2221
Abstract
Radar object recognition based on aspect-independent unique natural resonance features has been widely investigated. There are two main challenges: first, robust techniques to extract resonance features, and second, a synthesis of discriminant signals to achieve automatic quantitative object discrimination. In literature, various time-domain-based techniques are explored, where the discriminant signals are synthesized to make the late-time energy zero, once the target response is convolved with the true pulse from the waveform library. This approach is required to have an accurate estimation of a late-time index, along with its main focus on annihilating resonance information. However, to achieve accurate object recognition, there is a need to have a nonzero or maximum response other than true discriminant pulses. Thus, to fulfill the two critical tasks, natural resonance annihilation and getting a nonzero response for nonresonant components, we proposed the synthesis of frequency-domain-modified notch-filter discriminant waveforms known as N-pulses. Moreover, we proposed the statistical quantitative discrimination measure of the kernel peak-density discrimination number (KDN). This article investigates N-pulses by considering the effect of polarization variations along with parametric analysis. Moreover, the simulation analysis for canonical dielectric-coated objects is conducted followed by the noise analysis.