Particle filter techniques are common methods used to estimate the evolving state of nonlinear, non-Gaussian time-variant systems by utilizing a periodic sequence of noisy measurements. The accuracy of particle filter methods has often been shown to be superior to other state estimation techniques, such as the extended Kalman filter (EKF), for many applications. Unfortunately, the high computational cost and highly nondeterministic runtime behavior of particle filters often preclude their use in hard, real-time environments, where filter response must meet the strict timing requirements of the application. Particle filter algorithms are composed of three main stages: prediction, update, and resampling. General purpose graphics processing units (GPGPUs) have been successfully employed in previous research to accelerate the computation of both the prediction and update stages by exploiting their natural fine-grain parallelism. This research focuses on accelerating the resampling stage for GPGPU execution, which has been much more difficult to parallelize due to it's apparent inherent sequentially. This paper introduces a novel GPGPU implementation of the systematic and stratified resampling algorithms that exploit the monotonically increasing nature of the prefix-sum and the evolutionary nature of the particle weighting process to allow the re-indexing portion of the algorithms to occur in a two-phase, multi-threaded manner. This resulting measured factor of performance improvement for the systematic and stratified algorithms was 15x and 32x, respectively, over the serial implementations. INDEX TERMS Graphics processing units, parallel algorithms, parallel architectures, parallel programming, particle filters, state estimation, resampling.