Particle swarm optimization–based minimum residual algorithm for mobile robot localization in indoor environment

For indoor mobile robots, many localization systems based on wireless sensor network have been reported. Received signal strength indicator is often used for distance measurement. However, the value of received signal strength indicator always has large fluctuation because radio signal is easily influenced by environmental factors. This will bring adverse effect on the distance measurement and deteriorate the performance of robot localization. In this article, the measured data are dealt with weighted recursive filter, which can depress the measurement noise effectively. In the linearization procedure, the least square method often causes additional error because it seriously relies on anchor nodes. Therefore, a minimum residual localization algorithm based on particle swarm optimization is proposed for a mobile robot running in indoor environment. With continuous optimization and update of particle swarm, the position that gets the best solution of objective function can be adopted as the final estimated position. Experiment results show that the proposed algorithm, compared with traditional algorithms, can attain better localization accuracy and is closer to Cramer–Rao lower bound.

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A Novel Model on Curve Fitting and Particle Swarm Optimization for Vertical Handover in Heterogeneous Wireless Networks

Mathematical Problems in Engineering ◽

10.1155/2015/620658 ◽

2015 ◽

Vol 2015 ◽

pp. 1-16 ◽

Cited By ~ 8

Author(s):

Shidrokh Goudarzi ◽

Wan Haslina Hassan ◽

Mohammad Hossein Anisi ◽

Seyed Ahmad Soleymani ◽

Parvaneh Shabanzadeh

Keyword(s):

Particle Swarm Optimization ◽

Curve Fitting ◽

Particle Swarm ◽

Signal Strength ◽

Pso Algorithm ◽

Vertical Handover ◽

Received Signal Strength ◽

Network Selection ◽

Received Signal Strength Indicator ◽

Swarm Optimization

The vertical handover mechanism is an essential issue in the heterogeneous wireless environments where selection of an efficient network that provides seamless connectivity involves complex scenarios. This study uses two modules that utilize the particle swarm optimization (PSO) algorithm to predict and make an intelligent vertical handover decision. In this paper, we predict the received signal strength indicator parameter using the curve fitting based particle swarm optimization (CF-PSO) and the RBF neural networks. The results of the proposed methodology compare the predictive capabilities in terms of coefficient determination (R2) and mean square error (MSE) based on the validation dataset. The results show that the effect of the model based on the CF-PSO is better than that of the model based on the RBF neural network in predicting the received signal strength indicator situation. In addition, we present a novel network selection algorithm to select the best candidate access point among the various access technologies based on the PSO. Simulation results indicate that using CF-PSO algorithm can decrease the number of unnecessary handovers and prevent the “Ping-Pong” effect. Moreover, it is demonstrated that the multiobjective particle swarm optimization based method finds an optimal network selection in a heterogeneous wireless environment.

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A precise ultra high frequency partial discharge location method for switchgear based on received signal strength ranging

International Journal of Distributed Sensor Networks ◽

10.1177/1550147720903634 ◽

2020 ◽

Vol 16 (5) ◽

pp. 155014772090363

Author(s):

Jiajia Song ◽

Jinbo Zhang ◽

Xinnan Fan

Keyword(s):

Particle Swarm Optimization ◽

High Frequency ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Signal Strength ◽

Received Signal Strength ◽

Received Signal Strength Indicator ◽

Swarm Optimization ◽

Ultra High Frequency ◽

Discharge Location

Partial discharges are the main insulation defects encountered in gas-insulated switchgears. When it occurs inside the gas-insulated switchgear cavity, it degrades insulation, and, sooner or later, causes a breakdown. Therefore, it is important to discover insulation defects as early as possible, locate the discharge, and perform both defect identification and maintenance. Current ultra high frequency-based partial discharge location methods mainly use time delay. To obtain accurate delay times, however, a very high sampling rate is needed, which requires expensive hardware and greatly limits its application. Therefore, in this article, a localization method based on received signal strength indicator ranging is proposed, and location estimation is carried out. An easily implementable particle swarm optimization algorithm with high positioning accuracy is selected to compensate for the low positioning accuracy of current received signal strength indicator ranging methods. To further improve positioning accuracy, the convergence conditions of the particle swarm optimization are investigated, and, considering their constraints, an improved particle swarm optimization algorithm is proposed. By combining the characteristics of ultra high frequency wireless sensor array positioning, the particle size is optimized. The simulation results show that the location accuracy using the ultra high frequency switchgear partial discharge location method based on received signal strength indicator ranging with the improved particle swarm optimization algorithm performs significantly better.

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