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Parallel PHD Function on Target Association with Multistatic Radar System (MRS) using CUDA
Authors: Hudi Robertus | Suhartanto Heru | Pidanič Jan
Year: 2020
Type of publication: článek ve sborníku
Name of source: 16th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2020 : proceedings
Publisher name: IEEE (Institute of Electrical and Electronics Engineers)
Place: New York
Page from-to: 69-74
Titles:
Language Name Abstract Keywords
cze Paralelizace PHD funkce pro asociaci cílů v multi-statickém radarovém systém s využitím CUDA Asociace cílů je jedním z hlavních úkolů zpracování signálu v pasivním multi-statickém radarovém systému (MRS). Mezi pokročilými technikami asociace cílů pasivního radarového systému bylo provedeno několik experimentů založených na výpočtu funkce pravděpodobnosti hypotetické hustoty (PHD). Výpočet PHD funkce je velice náročným numerickým úkolem z hlediska časové náročnosti. Pro jeho optimalizaci a paralelizaci byla využita technologie CUDA (NVIDIA) na GPU akcelerátorech. Urychlení výpočtu algoritmu bylo ověřeno pomocí profilovacích nástrojů NVIDIA
eng Parallel PHD Function on Target Association with Multistatic Radar System (MRS) using CUDA Target association is one of the main process of the signal processing of the passive Multi-static Radar System (MRS) which requires a complex geometry calculation. Among advanced techniques for passive radar system's target association, several experiments have been done based on Probability Hypothetic Density (PHD) function. The complex calculation makes the computation process a very demanding task to be done, thus, this paper is focused on PHD function performance comparison between preceding attempts to the implementation using pure C programming language with CUDA library. Naive parallelization is used on mapping each matrices data to CUDA memories, for each major operation is done in parallel behavior via self-made CUDA kernels to suits the data dimensions. Results for kernels are captured with NVIDIA profiling tools for increasing number of random targets on 4 transmitter-receiver (PV) combination (without any knowledge about approximation of targets direction). All results are taken according to the average running time of kernel calls and speed up for each size of input, compared with serial and CPU parallel version data of the previous work. PHD; MRS; CUDA; GPU; target; association

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