Comparison of Kalman filter and particle filter used for localization of an underwater vehicle

Ko, Nak Yong; Kim, Tae Kyun

doi:10.1109/urai.2012.6463013

Cited by 33 publications

(15 citation statements)

References 6 publications

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“…PFs perform better than traditional Kalman filters in non-linear and non-Gaussian settings. Interesting insights into the advantages of PFs, performance comparison, and trade-offs of PFs over other non-PF solutions are provided by [37], [38]. However, PFs are computationally very demanding and take a significant amount of time to process a large number of particles; hence, PFs are seldom used for real-time applications.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

FPGA Implementation of Particle Filters for Robotic Source Localization

2021

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“…On the other hand, KF has restrictions on system model and uncertainty features in an application. Yong et al in [23] conducted a study comparing between both the PF and KF for robot localization. The study showed that PF and KF both provide more accurate localization compared to the methods of trilateration and triangulation.…”

Section: Kalman Filter Extensions Of Kalman Filter and Particle Filtermentioning

confidence: 99%

Using phase shift fingerprints and inertial measurements in support of precise localization in urban areas

Elbes

Alkhatib

Al-Fuqaha

et al. 2019

Pers Ubiquit Comput

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Localization is an important primitive that is utilized in a number of important applications such as location-based mobile services, augmented reality, and autonomous mobile robotics. While the GPS technology is considered the de facto standard for outdoor localization, it is known to suffer from significant accuracy limitation in urban areas. In this work, we present a particle filter-based data fusion technique for localization in urban areas. The proposed localization technique provides more accurate location estimation results due to its ability to efficiently fuse together information collected from diverse sensor technologies. The novelty of our proposed approach stems from its ability to fuse data from diverse sources, namely, phase shift fingerprints collected from Low Power AM Radio (LPAM) towers and inertial measurement sensors. Our simulation results indicate that the proposed approach can achieve an accuracy of 0.5 m using a limited number of LPAM towers as low as 5. Also, the proposed approach requires the collection of a low number of LPAM phase shift fingerprints. Our simulations indicate that 30 fingerprints are enough to provide 0.5 m accuracy in a 100 × 100 m 2 deployment. Keywords Smart cities • Localization in urban areas • Data fusion • Fingerprinting • Channel state information 1 Introduction In recent years, several critical national infrastructures (such as the electric grid, intelligent transportation, water systems) Mohammed Elbes

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“…As stated in [37] in cases of nonlinear systems, non-Gaussian noise distribution, and extreme irregular measurements, the use of other approaches of filtering other than the Kalman filter, like the particle filter, is recommended. The particle filter is the approximated Bayesian filtering algorithm based on the Monte Carlo method.…”

Section: Filteringmentioning

confidence: 99%

Altitude control of an underwater vehicle based on computer vision

Rodrigues

Cruz

Pinto

2018

OCEANS 2018 MTS/IEEE Charleston

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Ao professor Nuno Cruz, um especial agradecimento pela exímia orientação ao longo desta dissertação, pelas criticas, atenção, e mais importante, pelos ensinamentos e conhecimento que me transmitiu.Quero também agradecer ao professor Andry Pinto pela ajuda ao longo da dissertação e ao João Monteiro que se mostrou sempre disponível a esclarecer qualquer dúvida acerca do profiler ao longo deste caminho.Não poderia deixar de agradecer a todos os meus amigos mais próximos que de uma forma directa ou indirecta, contribuíram para a elaboração deste projeto, pelo apoio e sobretudo pelos momentos de descontração que proporcionaram.Quero também agradecer aos meus pais pelo seu apoio incondicional. Um especial agradecimento ao meu avô pela educação durante a minha infância que tanto contribuiu para a forma como encaro os obstáculos ao longo da minha vida. Pedro Rodrigues v vi "Sometimes you may be the smartest person in the room and you do not want anybody to know that. And there are other times that you are not the smartest person in the room but you want to make other people think you are"

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Comparison of Kalman filter and particle filter used for localization of an underwater vehicle

Cited by 33 publications

References 6 publications

FPGA Implementation of Particle Filters for Robotic Source Localization

FPGA Implementation of Particle Filters for Robotic Source Localization

Using phase shift fingerprints and inertial measurements in support of precise localization in urban areas

Altitude control of an underwater vehicle based on computer vision

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