Accurate and Reliable Human Localization Using Composite Particle/FIR Filtering

Pak, Jung Min; Ahn, Choon Ki; Shmaliy, Yuriy S.; Shi, Peng; Lim, Myo Taeg

doi:10.1109/thms.2016.2611826

Cited by 48 publications

(30 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both centralized and distributed cooperative localization algorithms are IIR filtering approach. Compared to FIR filter approach, such as hybrid particle/FIR filtering [11], composite particle/FIR filtering [12], and deadbeat dissipative FIR filtering [13], the presented algorithms have greater mean square error when there are model uncertainty and/or numerical error in the system, but these proposed algorithms can still meet the needs of practical positioning. And to design filters with the same parameters, FIR approaches need more parameters than these proposed algorithms; this will increase the amount of computation of the DSP.…”

Section: Simulation Resultsmentioning

confidence: 99%

Multi‐UAVs Cooperative Localization Algorithms with Communication Constraints

Gao

2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Communication is the basis of multi-UAVs cooperative localization. There will be some communication delay and packet loss when one UAV communicates with others, and these communication restrictions may have negative influences on multi-UAV cooperative localization. In this paper, the communication among UAVs is described as Bernoulli random variables, and two kinds of cooperative localization algorithms are proposed. One is centralized algorithm, where a kind of one-step prediction strategy is designed. The other is distributed algorithm, where a kind of prediction-compensation strategy is proposed and weighted expectation state estimation method is designed. Simulation results show the effectiveness of the proposed algorithms.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Multi‐UAVs Cooperative Localization Algorithms with Communication Constraints

Gao

2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Next following section will discuss about the objective of this paper. Small [37] Big [37] Noise Measurement Normal [34] [10] Normal [25] Small or Normal [25] …”

Section: Problem Formulationmentioning

confidence: 99%

“…This phenomenon will cause computation burden to the overall systems [24]. Two (2) reason behind this issue, whether it caused by the of small noise measurement in accurate sensor or small number of particle [25]. In order to counter the phenomenon, the special strategies resampling algorithm can be used.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of the RSIL to LLVM IR Translator for Verification of the Intermediate Code on IoT Virtual Machine

Son¹,

Oh²,

Lee³

2017

IJCA

View full text Add to dashboard Cite

The phenomenon of sample impoverishment during particle filtering always contribute computation burden to the inertial-based mobile IPS systems. This is due to the factor of noise measurement and number of particle. Usually, the special strategies resampling algorithms was used. However, these algorithms just can fit in certain environment. This needs an adaptation of noise measurement and number of particle in a algorithm in order to make resampling with more intelligence, reliability and robust. In tbis paper, we will propose an adaptive special strategies resampling by adapting noise and particle measurement. These adaptation is used to determine the most suitable algorithm of special strategies resampling, that can be switched for resampling purpose. Finally, we illustrate our proposed solution our for indoor environment setup.

show abstract

“…Practically, these three parameters are manually tuned by matching the parameters with the operating system and are fixed for all state of operation (Jerome Mendes & Araujo, ). Until now, many methods have been proposed to overcome the dynamic system such as Kalman filter (Yang et al, ) and particle filter (Pak, Ahn, Shi, Shmaliy, & Lim, ; Pak, Ahn, Shmaliy, Shi, & Lim, ; Zhang, Zang, Zhao, Fan, & Hao, ). However, in the system with the PID controller, the PID parameters still need to be tuned for each different operation.…”

Section: Introductionmentioning

confidence: 99%

A novel optimal PID controller autotuning design based on the SLP algorithm

Pongfai

Zhang

et al. 2019

Expert Systems

View full text Add to dashboard Cite

A novel optimal proportional integral derivative (PID) autotuning controller design based on a new algorithm approach, the “swarm learning process” (SLP) algorithm, is proposed. It improves the convergence and performance of the autotuning PID parameter by applying the swarm and learning algorithm concepts. Its convergence is verified by two methods, global convergence and characteristic convergence. In the case of global convergence, the convergence rule of a random search algorithm is employed to judge, and Markov chain modelling is used to analyse. The superiority of the proposed method, in terms of characteristic convergence and performance, is verified through the simulation based on the automatic voltage regulator and direct current motor control system. Verification is performed by comparing the results of the proposed model with those of other algorithms, that is, the ant colony optimization with a new constrained Nelder–Mead algorithm, the genetic algorithm (GA), the particle swarm optimization (PSO) algorithm, and a neural network (NN). According to the global convergence analysis, the proposed method satisfies the convergence rule of the random search algorithm. With respect to the characteristic convergence and performance, the proposed method provides a better response than the GA, the PSO, and the NN for both control systems.

show abstract

Accurate and Reliable Human Localization Using Composite Particle/FIR Filtering

Cited by 48 publications

References 33 publications

Multi‐UAVs Cooperative Localization Algorithms with Communication Constraints

Multi‐UAVs Cooperative Localization Algorithms with Communication Constraints

Design and Implementation of the RSIL to LLVM IR Translator for Verification of the Intermediate Code on IoT Virtual Machine

A novel optimal PID controller autotuning design based on the SLP algorithm

Contact Info

Product

Resources

About