Distributed Hybrid Particle/FIR Filtering for Mitigating NLOS Effects in TOA-Based Localization Using Wireless Sensor Networks

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

doi:10.1109/tie.2016.2608897

Cited by 130 publications

(78 citation statements)

References 40 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

“…A recurrent neural network was proposed in Choi et al 3 ; it proposed a recurrent neural network (RNN) model, which takes a series of channel state information (CSI) to identify the corresponding channel condition. Based on distributed filter, a new NLOS recognition algorithm was proposed in Pak et al 4 ; it proposes a new NLOS identification algorithm based on distributed filtering to mitigate NLOS effects, including localization failures. Momtaz et al 5 proposed a new algorithm based on subspace method to identify and eliminate NLOS errors.…”

Section: Introductionmentioning

confidence: 99%

Wireless sensor indoor positioning based on an improved particle filter algorithm

Pan

Qin

et al. 2020

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Positioning by wireless sensor network is one of its main functions and has been widely used in many fields. However, when signal propagation is hindered, serious errors, non-line-of-sight errors, occur. In order to solve this problem, this article proposes an improved particle filter algorithm, which introduces the idea of residual analysis to improve reliability. The algorithm assigns weights to the particles based on the residuals and selects the appropriate particles. In addition, the non-line-of-sight error parameter a is introduced, and the second selection is made according to a, which considers the influence of non-line-of-sight error. The non-line-of-sight error is greatly reduced after two selections. The simulation is performed under several different non-line-of-sight errors, and results show that the algorithm is superior to Kalman filter and particle filter.

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

Distributed Hybrid Particle/FIR Filtering for Mitigating NLOS Effects in TOA-Based Localization Using Wireless Sensor Networks

Cited by 130 publications

References 40 publications

Multi-UAVs Cooperative Localization Algorithms with Communication Constraints

Multi-UAVs Cooperative Localization Algorithms with Communication Constraints

Wireless sensor indoor positioning based on an improved particle filter algorithm

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

Contact Info

Product

Resources

About