Kernel Function-$\tau$-Wigner Distribution Associated With the Linear Canonical Transform
Zhichao Zhang, Xi-Ya Shi · IEEE Signal Processing Letters · 2022
To enhance noisy linear frequency-modulated (LFM) signal processing capacity without increasing computational and parameters selective complexity, this study introduces the so-called kernel function-$\tau$-Wigner distribution (KF-$\tau$-WD) by combining the kernel function Wigner distribution (KFWD) with the$\tau$-Wigner distribution ($\tau$-WD). We obtain the computational complexity of the KF-$\tau$-WD through the computational complexity analyses of the KFWD and$\tau$-WD. We formulate its parameters selection strategy for noisy LFM signal processing in accordance with Heisenberg's uncertainty principles. Our theoretical results are also verified by numerical simulations.