This paper investigates the ability of the depolarization degree, derived from the characteristic polarization states at the resonant frequency set, to identify corner or swept, i.e. dihedral, changes in same-class ta...This paper investigates the ability of the depolarization degree, derived from the characteristic polarization states at the resonant frequency set, to identify corner or swept, i.e. dihedral, changes in same-class targets by a metallic wire example. A well-estimated depolarization degree requires a robust extraction of the fundamental target resonance set in two orthogonal sets of fully co-polarized and cross-polarized polarization channels, then finding the null polarization states using the Lagrangian method. Such depolarization degree per resonance mode has the potential to form a robust feature set because it is relatively less sensitive to onset ambiguity, invariant to rotation, and could create a compact, recognizable, and separable distribution in the proposed feature space. The study was limited to two targets with two swept changes of fifteen degrees within normal incidence;under a supervised learning approach, the results showed that the identification rate converging to upper-bound (100%) for a signal-to-noise ratio above 20 dB and lower-bound around (50%) below −10 dB.展开更多
The paper assesses how multiple-static scattering mitigates the effect of late-time onset on the robustness of the extracted resonance modes in the context of radar target classification. The assessment exploits the m...The paper assesses how multiple-static scattering mitigates the effect of late-time onset on the robustness of the extracted resonance modes in the context of radar target classification. The assessment exploits the mode distribution vs onset shift to verify the sensitivity of the mode’s extraction to the selected onset, especially higher-order, to onset. However, within some bistatic directions, the modes have enhanced energies with lesser specular energy, making the modes estimate less sensitive to onset shifts. Also, the mode distribution per bistatic and polarization configuration has demonstrated different onset windows of accurate and consistent mode extraction. Notably, the distribution of the mode energy distribution reveals that classification performance degrades with poorly selected onset.展开更多
文摘This paper investigates the ability of the depolarization degree, derived from the characteristic polarization states at the resonant frequency set, to identify corner or swept, i.e. dihedral, changes in same-class targets by a metallic wire example. A well-estimated depolarization degree requires a robust extraction of the fundamental target resonance set in two orthogonal sets of fully co-polarized and cross-polarized polarization channels, then finding the null polarization states using the Lagrangian method. Such depolarization degree per resonance mode has the potential to form a robust feature set because it is relatively less sensitive to onset ambiguity, invariant to rotation, and could create a compact, recognizable, and separable distribution in the proposed feature space. The study was limited to two targets with two swept changes of fifteen degrees within normal incidence;under a supervised learning approach, the results showed that the identification rate converging to upper-bound (100%) for a signal-to-noise ratio above 20 dB and lower-bound around (50%) below −10 dB.
文摘The paper assesses how multiple-static scattering mitigates the effect of late-time onset on the robustness of the extracted resonance modes in the context of radar target classification. The assessment exploits the mode distribution vs onset shift to verify the sensitivity of the mode’s extraction to the selected onset, especially higher-order, to onset. However, within some bistatic directions, the modes have enhanced energies with lesser specular energy, making the modes estimate less sensitive to onset shifts. Also, the mode distribution per bistatic and polarization configuration has demonstrated different onset windows of accurate and consistent mode extraction. Notably, the distribution of the mode energy distribution reveals that classification performance degrades with poorly selected onset.
