## 1 Introduction  
In this paper, we propose an accelerated target position tracking system using a GPU-based acoustic sensor and a particle filter (PF) for effective tracking of moving targets. We focus on using parallel processing of GPU to track sudden change of target movement by using multiple system state equations in the existing PF. The proposed parallel processing is scalable for number of sensors and for tracking multiple target. So, proposed architecture can be used in systems such as Internet of Things (IoT) applications. We analyzed the execution time of the algorithm for actual operation on the GPU.  
Through this, we searched for the proper design elements of the memory buffer and the algorithm that computes the signal processing.  
Position estimation of moving targets using an acoustic signal is a method that can be used not only in air but also in water due to its special environment [1]. The sensor array receives the acoustic signal from the target and estimates the position by measuring three or more time difference of arrival (TDOA) values in the three-dimensional space [2]. Currently, there is a generalized cross correlation phase transform (GCC-PHAT) method for measuring TDOA in the frequency domain not only in the time domain. In particular, GCC-PHAT is a method of obtaining a correlation coefficient by converting a signal into a frequency domain, and has strength in a real-time system because a relatively small amount of calculation is required compared to a method of obtaining a general correlation coefficient. In this paper, we propose an algorithm using parallel Fast Fourier Transform (FFT) in GPU to increase the processing speed of TDOA measurement for each acoustic sensor in three-dimensional space.  
In general, various filters such as the kalman filter (KF), extended KF, unscented KF and PF are used to estimate the state of the target [3]. Additionally, there is a problem that it is difficult to estimate the target state due to the non-linearity between the system state and the measured value in estimating the state of the target using TDOA measurement. Therefore, in this paper, we propose an accelerated system that estimates the target position using the PF, which has an advantage in non-linear systems and non-Gaussian systems.  
The PF is a sequential monte carlo (SMC) method that estimates the state of a system by observing an error. If the number of particles is sufficient, an optimal estimate can be obtained. However, if the number of particles is not sufficient, the estimation may be problematic. That is, increasing the number of particles to obtain an optimal estimate means that the amount of computation in the system increases, which inevitably affects the operation speed of the system.  
Therefore, in this paper, we propose accelerating the processing speed by simultaneously processing the state update process and weight calculation process of each particle using GPU. Additionally, we propose the PF system for multiple state equation to accurately track even when the sudden movement of a target deviates from the system state equation used in the process in the Markov chain based PF.  
We described the target system with multiple state-space models, which are processed by the parallel processing algorithm on GPU. In the experiment, we show an implemented result using the proposed algorithm on the GPU and analyzed the execution time of the algorithm required for the target position estimation, including the partial execution time of the proposed algorithm.