Dmitry Mikushin | Searcharxiv

arXiv2019

Phase tracking based on GPGPU and applications in Planetary radio Science

This paper introduces a phase tracking method for planetary radio science research with computational algorithm implemented fo r NVIDIA GPUs. In contrast to the phase-locked loop (PPL) phase counting method used in traditional Doppler data processing, this method fits the tracking data signal into the shape expressed by the Taylor polynomial with optimal phase and amplitude coefficients. The Differential Evolution (DE) algorithm is employed for polynomial fitting. In order to cope with high computational intensity of the proposed phase tracking method, the graphics processing units (GPUs) are employed. As a result, the method estimates the instantaneous phase, frequency, derivative of frequency (line-of-sight acceleration) and the total count phase of different integration scales. This data can be further used in planetary radio science research to analyze the planetary occultation and gravitational fields. The method has been tested on MEX (Mars Express, ESA) and Chang'E 4 relay satellite (China) tracking data. In a real experiment with 400K data block size and $\sim$80,000 DE solver objective function evaluations we were able to acheive the target convergence threshold in 6.5 seconds and do real-time processing on NVIDIA GTX580 and 2$\times$ NVIDIA K80 GPUs, respectively. The precision of integral Doppler (60s) is 2 mrad/s and 4 mrad/s for MEX(3-way) and Chang'E 4 relay satellite(3-way) respectively.

astro-ph.IM↗

arXiv2016

End-to-end Learning of Cost-Volume Aggregation for Real-time Dense Stereo

We present a new deep learning-based approach for dense stereo matching. Compared to previous works, our approach does not use deep learning of pixel appearance descriptors, employing very fast classical matching scores instead. At the same time, our approach uses a deep convolutional network to predict the local parameters of cost volume aggregation process, which in this paper we implement using differentiable domain transform. By treating such transform as a recurrent neural network, we are able to train our whole system that includes cost volume computation, cost-volume aggregation (smoothing), and winner-takes-all disparity selection end-to-end. The resulting method is highly efficient at test time, while achieving good matching accuracy. On the KITTI 2015 benchmark, it achieves a result of 6.34\% error rate while running at 29 frames per second rate on a modern GPU.

cs.CV↗