Advanced targets association based on GPU computation of PHD function

Abstract

The precise and quick association of targets is one of the main challenging tasks in the signal processing field of the Multistatic Radar System (MRS). The paper deals with target association techniques based on the computation of the Probability Hypothetic Density (PHD) Function. The Computation time makes solving the PHD a very demanding task. The speedup of a newly developed algorithm depends on vectorization and parallel processing techniques. This paper describes the comparison between the original and parallel version of the target association algorithm with the full set of input data (without any knowledge about the approximation of targets direction) and the comparison with the advanced target association algorithm using additional input information about the direction of the target. All algorithms are processed in the MATLAB environment and Microsoft Visual Studio - C. The comparison also includes Central Processor Unit (CPU) and Graphics Processor Unit (GPU) version of all algorithms.