Implementation of Particle Filters for Single Target Tracking Using CUDA

Goyal, Bhavya; Budhraja, Tarun; Bhatnagar, Roheet; Shivakumar, Chandan

doi:10.1109/icacc.2015.111

Cited by 7 publications

(2 citation statements)

References 6 publications

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“…The tracking algorithm was accelerated by approximately 55 % compared to the CPU-based algorithm. In [16], they proposed a PF that parallelized the likelihood function calculation and reduced the calculation time of that. However, it required time to generate random values, such studies have conducted parallelization in environments with a considerable change in the signal or amount of information of particles, such as image tracking.…”

Section: Introductionmentioning

confidence: 99%

Accelerated Particle Filter With GPU for Real-Time Ballistic Target Tracking

et al. 2023

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This study addresses the problem of real-time tracking of high-speed ballistic targets. Particle filters can be used to overcome the nonlinearity of motion and measurement models in ballistic targets. However, applying particle filters (PFs) to real-time systems is challenging since they generally require a significant computation time. So, most of the existing methods of accelerating PF using a graphics processing unit (GPU) for target tracking applications have accelerated computation weight function and resampling part. However, the computational time per part varies from application to application, and in this work, we confirm that it takes a lot of computational time in the model propagation part and propose accelerated PF by parallelizing the corresponding logic. The real-time performance of the proposed method was tested and analyzed using an embedded system. And compared to conventional PF on the central processing unit (CPU), the proposed method shows that the proposed method significantly reduces computational time by at least 10 times, improving real-time performance.INDEX TERMS Ballistic target tracking, graphics processing unit, particle filter, real-time systems.

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Section: Introductionmentioning

confidence: 99%

Accelerated Particle Filter With GPU for Real-Time Ballistic Target Tracking

et al. 2023

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“…In general, solving the MTT problem involves three tasks: (i) Extraction -extract target related information from the raw data acquired from the sensors; (ii) Data associationidentify each target's corresponding measurements; and, (iii) Estimation -estimate the position of targets via single target tracking techniques (as shown [8]- [10]). Perhaps the most challenging task is to conduct data association because if data associated with each target is determined, it becomes much easier to conduct estimation for each individual target.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Layer K-means Approach for Multi-Sensor Data Pattern Recognition in Multi-Target Localization

Silva,

Suresh,

Tao

et al. 2017

Preprint

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Data-target association is an important step in multi-target localization for the intelligent operation of unmanned systems in numerous applications such as search and rescue, traffic management and surveillance. The objective of this paper is to present an innovative data association learning approach named multi-layer K-means (MLKM) based on leveraging the advantages of some existing machine learning approaches, including K-means, K-means++, and deep neural networks. To enable the accurate data association from different sensors for efficient target localization, MLKM relies on the clustering capabilities of K-means++ structured in a multi-layer framework with the error correction feature that is motivated by the backpropogation that is well-known in deep learning research. To show the effectiveness of the MLKM method, numerous simulation examples are conducted to compare its performance with K-means, K-means++, and deep neural networks.

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