Moving-Target Position Estimation Using GPU-Based Particle Filter for IoT Sensing Applications

Kim, Seongseop; Cho, Jeonghun; Park, Daejin

doi:10.3390/app7111152

Cited by 13 publications

(11 citation statements)

References 22 publications

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“…During the last few years, the capability of GPU is growing much faster than that of CPU's because of greatly increasing hardware requirement for modern computer games. The GPU is also rapidly and widely used for various scientific computations in addition to graphic display [30], such as fluid dynamics [31], biophysics [32], molecular dynamics [33], and IoT sensing [34]. GPUs can provide huge performance improvement than a single CPU core for many applications.…”

Section: Cuda Gpumentioning

confidence: 99%

On GPU Implementation of the Island Model Genetic Algorithm for Solving the Unequal Area Facility Layout Problem

et al. 2018

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Facility layout problem (FLP) is one of the hottest research areas in industrial engineering. A good facility layout can achieve efficient production management, improve production efficiency, and create high economic values. Because FLP is an NP-hard problem, meaning it is impossible to find the optimal solution when problem becomes sufficiently large, various evolutionary algorithms (EAs) have been proposed to find a sub-optimal solution within a reasonable time interval. Recently, a genetic algorithm (GA) was proposed for unequal area FLP (UA-FLP), where the areas of facilities are not identical. More precisely, the GA is an island model based, which is called IMGA. Since EAs are still very time consuming, many efforts have been devoted to how to parallelize various EAs including IMGA. In recent work, Steffen and Dietmar proposed how to parallelize island models of EAs. However, their parallelization approaches are preliminary because they focused mainly on comparing the performances between different parallel architectures. In addition, they used one mathematical function to model the problem. To further investigate on how to parallelize the IMGA by GPU, in this paper we propose multiple parallel algorithms, for each individual step in the IMGA when solving the industrial engineering problem, UA-FLP, and conduct experiments to compare their performances. After integrating better algorithms for all steps into the IMGA, our GPU implementation outperforms the CPU counterpart and the best speedup can be as high as 84.

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Section: Cuda Gpumentioning

confidence: 99%

On GPU Implementation of the Island Model Genetic Algorithm for Solving the Unequal Area Facility Layout Problem

et al. 2018

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“…Their performance analysis shows that up to 75x speedup can be achieved on a 512core GPU over sequential implementation. Kim et al [26] implemented PF on a GPU for target position estimation and parallelized the calculation process utilizing multiple GPU cores. The proposed algorithm was simulated on a CPU in MATLAB and then verified on GPU, resulting in a 55% reduction in execution time.…”

Section: State-of-the-artmentioning

confidence: 99%

FPGA Implementation of Particle Filters for Robotic Source Localization

2021

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Particle filtering is very reliable in modelling non-Gaussian and non-linear elements of physical systems, which makes it ideal for tracking and localization applications. However, a major drawback of particle filters is their computational complexity, which inhibits their use in real-time applications with conventional CPU or DSP based implementation schemes. The re-sampling step in the particle filters creates a computational bottleneck since it is inherently sequential and cannot be parallelized. This paper proposes a modification to the existing particle filter algorithm, which enables parallel re-sampling and reduces the effect of the re-sampling bottleneck. We then present a high-speed and dedicated hardware architecture incorporating pipe-lining and parallelization design strategies to supplement the modified algorithm and lower the execution time considerably. From an application standpoint, we propose a novel source localization model to estimate the position of a source in a noisy environment using the particle filter algorithm implemented on hardware. The design has been prototyped using Artix-7 field-programmable gate array (FPGA), and resource utilization for the proposed system is presented. Further, we show the execution time and estimation accuracy of the high-speed architecture and observe a significant reduction in computational time. Our implementation of particle filters on FPGA is scalable and modular, with a low execution time of about 5.62 µs for processing 1024 particles (compared to 64 ms on Intel Core i7-7700 CPU with eight cores clocking at 3.60 GHz) and can be deployed for real-time applications.

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“…In the industry, there also exists a wide range of applications that require to perform acoustic source localization [22]. Currently, smart factories making use of distributed sensors are gaining momentum.…”

Section: Acoustic Source Localization In Iot 21 Sound Source Localizmentioning

confidence: 99%

Practical Considerations for Acoustic Source Localization in the IoT Era: Platforms, Energy Efficiency, and Performance

Belloch

Badía

Igual

et al. 2019

IEEE Internet Things J.

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The rapid development of the Internet of Things (IoT) has posed important changes in the way emerging acoustic signal processing applications are conceived. While traditional acoustic processing applications have been developed taking into account high-throughput computing platforms equipped with expensive multichannel audio interfaces, the IoT paradigm is demanding the use of more flexible and energy-efficient systems. In this context, algorithms for source localization and ranging in wireless acoustic sensor networks can be considered an enabling technology for many IoT-based environments including security, industrial and health-care applications. This paper is aimed at evaluating important aspects dealing with the practical deployment of IoT systems for acoustic source localization. Recent Systems-On-Chip (SoC) composed of low-power multicore processors, combined with a small graphics accelerator (or GPU), yield a notable increment of the computational capacity needed in intensive signal processing algorithms while partially retaining the appealing low power consumption of embedded systems. Different algorithms and implementations over several state-of-the-art platforms are discussed, analyzing important aspects such as the trade-offs between performance, energy efficiency and exploitation of parallelism by taking into account real-time constraints.

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Moving-Target Position Estimation Using GPU-Based Particle Filter for IoT Sensing Applications

Cited by 13 publications

References 22 publications

On GPU Implementation of the Island Model Genetic Algorithm for Solving the Unequal Area Facility Layout Problem

On GPU Implementation of the Island Model Genetic Algorithm for Solving the Unequal Area Facility Layout Problem

FPGA Implementation of Particle Filters for Robotic Source Localization

Practical Considerations for Acoustic Source Localization in the IoT Era: Platforms, Energy Efficiency, and Performance

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