## 3 Proposed Architecture
### Target Tracking Using Multiple State-Space Models
Table 2 shows the execution time of the algorithm measured by changing the sampling rate. As the sampling rate increases, the time required to execute the entire algorithm increases. That is, the amount of data input during the same time increases, so that more time is taken to calculate the TDOA measurement value. When using the sampled signal at a sampling rate of 44 kHz, the CPU-based algorithm took about 100 times faster than the CPU-based algorithm. \begin{table}
\begin{tabular}{c c} \hline
**Target Device** & **Model** \\ \hline GPU 1 & Jetson TX1 256 cores \\ CPU 1 & 64-bit ARM A57 CPUs \\ GPU 2 & Tesla K40c 2880 cores \\ \hline \end{tabular}
\end{table} 
Table 1: Target devices. Figure 8: Verification of PF using MATLAB. Figure 7: Experimental flow and target embedded board(NVIDIA Jetson TX1). 62.60 ms and the GPU-based algorithm took about 18.24 ms. The reducing rate of GPU-based algorithm for CPU-based algorithm has increased as sampling rate increased. As a result, we found that the GPU-based algorithm is less affected by the execution time relative to the sampling rate. To estimate sudden state changes of the system, we implemented the proposed GPU-based PF using a multiple state-space model that can be predicted. The multiple state-space model based PF using GPU with added parallel processing has similar performance to the PF that just uses the GPU. The results are as follows. Figure 10 shows the execution time according to the number of state-space models with the proposed GPU-based multiple system model using a PF. In this experiment, we use NVIDIA Tesla K40c to see GPU parallelism by multiple state-space model. In the GPU kernel, we implemented block-thread kernel architecture, especially divided the particles in each block according to the state-space model and each block has 1024 threads in two dimensions. Therefore, the implemented algorithm uses the same number of GPU blocks as the number of state-space models. Because of GPU's parallelism, for each number of particles, 1000 to 5000, the execution time is only different by less than 0.16 ms according to the number of state-space models. We found that parallel execution in the GPU does not significantly affect execution time even if the number of particles increases by the number of system models. For example, if the number of state-space models is X, the number of particles is X\({}^{*}\)N. Table 3 shows the number of blocks and threads, execution time for PF processing, and execution time for copying particles to GPU according to the number of state-space models when the number of particles is 5000. When the number of state-space models is 1 and 100, it shows just a 0.16 ms time difference for processing the PF, while there is a 10.99 ms time difference for copying data to GPU. It shows that a lot of execution time is used in the process of copying data to the GPU. It also shows that the PF processing time does not change significantly when the number of state-space models increases. Using this proposed GPU-based algorithm, we found that the state-space model suitable for sudden movement of the target was found through parallel computing as verified

\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sampling Rate** & **GPU Based** & **Time Increment** & **CPU Based** & **Time Increment** & **Time Reduction Rate** \\
**(kz)** & **(ms)** & **(ms)** & **(ms)** & **(ms)** & **(\%)** \\ \hline
11 & 6.19 & - & 15.06 & - & 58.87 \\
22 & 10.96 & 4.77 & 27.16 & 12.10 & 59.64 \\
33 & 14.23 & 3.26 & 46.27 & 19.10 & 69.25 \\
44 & 18.24 & 4.01 & 62.60 & 16.32 & 70.86 \\ \hline \hlineusing MATLAB. With this parallel architecture, we find out that this architecture can also be applied to multiple target tracking using different state-space models, as implemented in this study in IoT sensing applications. ## 5 Conclusions

In this paper, we implemented an accelerated GPU-based algorithm to estimate moving target position by using multiple state-space models which can be applied to IoT sensing applications.