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The complex-conjugate pole-residue (CCPR) model has been popularly adopted because CCPR-finite-difference time domain (FDTD) can reduce the memory requirement with the help of complex conjugate property of auxiliary variables. To fully utilize CCPR-FDTD, it is of great necessity to investigate its numerical stability since the FDTD method is conditionally stable. Nonetheless, the numerical stability conditions of CCPR-FDTD have not been studied because its derivation is not straightforward. In this communication, the numerical stability conditions of CCPR-FDTD are systematically derived by combining the von Neumann method with Routh–Hurwitz criterion. It is found that the numerical stability conditions of CCPR-FDTD are the same as those of the modified Lorentz-FDTD with bilinear transform. Moreover, the numerical accuracy of CCPR-FDTD is studied, and numerical examples are employed to validate this work. [ABSTRACT FROM AUTHOR]

In this work, we present a dispersive finite-difference time-domain (FDTD) algorithm using a four-pole complex rational function (CRF). For the sake of a better curve fitting of the four-pole CRF dispersion model, we use a particle swarm optimization technique. We also discuss an efficient memory storage strategy using a state-space approach. The numerical aspects of four-pole CRF-FDTD, the numerical accuracy and the numerical stability, are investigated in detail. Numerical examples are used to validate four-pole CRF-FDTD and numerical stability issues are discussed in detail. We also discuss the computational accuracy and the computational efficiency of an arbitrary $N$-pole CRF-FDTD. [ABSTRACT FROM PUBLISHER]

We propose a dispersive finite-difference time domain (FDTD) suitable for the electromagnetic analysis of human tissues. The dispersion relation of biological tissues is characterized by a quadratic complex rational function (QCRF) that leads to an accurate FDTD algorithm in 400 MHz–3 GHz. QCRF coefficients are extracted by applying the complex-curve fitting technique, without initial guess. Numerical examples are used to illustrate the computational accuracy and stability of QCRF-based FDTD. [ABSTRACT FROM PUBLISHER]