  High-performance GPU-accelerated particle filter methods are critical for
object detection applications, ranging from autonomous driving, robot
localization, to time-series prediction. In this work, we investigate the
design, development and optimization of particle-filter using half-precision on
CUDA cores and compare their performance and accuracy with single- and
double-precision baselines on Nvidia V100, A100, A40 and T4 GPUs. To mitigate
numerical instability and precision losses, we introduce algorithmic changes in
the particle filters. Using half-precision leads to a performance improvement
of 1.5-2x and 2.5-4.6x with respect to single- and double-precision baselines
respectively, at the cost of a relatively small loss of accuracy.
