Magnetic particle imaging (MPI) is a new tomographic imaging technique that can quantitatively correlate MPI signal intensity to the spatial distribution of magnetic nanoparticle (MNP) tracers. Due to its non-ionizing nature, low background signal from biological matrices, high contrast, and relatively good spatial and temporal resolution, MPI has been actively studied and applied to biomedical imaging and is expected to reach the clinical stage soon. To further improve the spatial resolution limit in MPI, researchers have been working towards optimizing the image reconstruction algorithms, magnetic field profiles, tracer designs, circuitry, etc. Recent studies reported that lower excitation field amplitudes can improve spatial resolution, though this comes at the expense of lower MPI signal and tracer sensitivity. Different excitation field profiles directly affect the collective dynamic magnetizations of tracers recorded by the receiver coil in MPI. However, there is a gap between understanding the relaxation dynamics of MNP tracers, the signal-to-noise ratio (SNR) of MPI signals, and the MPI spatial resolution. In this work, we used a stochastic Langevin equation with coupled Brownian and Néel relaxations to model the magnetic dynamics of different MNP tracers subjected to varying excitation fields. We analyzed the collective time-domain dynamic magnetizations (- curves), magnetic-field hysteresis loops ( curves), point spread functions (PSFs), higher harmonics, and SNR of the third harmonic to understand how the excitation field affects MPI performance. We employed Full Width at Half Maximum and SNR as evaluation metrics for imaging resolution and signal quality, respectively. Our study supports previous findings on the impact of excitation field amplitude on MPI performance while offering more profound insights into the interplay of nonequilibrium Néel and Brownian relaxation, tracer core size, and SNR.