PFPSO: An Optimised Filtering Approach Based on Sampling

Hernane, Y.; Hernane, Soumeya Leila; Benyettou, M.

doi:10.3923/jas.2010.494.499

Cited by 5 publications

(1 citation statement)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PSO is a population based stochastic optimization technique, developed by Eberhart and Kennedy in 1995 [ 14 ]. They claimed that searching for a food source is similar to finding a solution for a common research goal [ 15 ]. In comparison with other AI optimization techniques, the power of PSO lies in its simplicity of implementation.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Swarm Optimization-Based Magnetometer Calibration for Personal Handheld Devices

Ali

Siddharth

Syed

et al. 2012

Sensors

View full text Add to dashboard Cite

Inertial Navigation Systems (INS) consist of accelerometers, gyroscopes and a processor that generates position and orientation solutions by integrating the specific forces and rotation rates. In addition to the accelerometers and gyroscopes, magnetometers can be used to derive the user heading based on Earth's magnetic field. Unfortunately, the measurements of the magnetic field obtained with low cost sensors are usually corrupted by several errors, including manufacturing defects and external electro-magnetic fields. Consequently, proper calibration of the magnetometer is required to achieve high accuracy heading measurements. In this paper, a Particle Swarm Optimization (PSO)-based calibration algorithm is presented to estimate the values of the bias and scale factor of low cost magnetometers. The main advantage of this technique is the use of the artificial intelligence which does not need any error modeling or awareness of the nonlinearity. Furthermore, the proposed algorithm can help in the development of Pedestrian Navigation Devices (PNDs) when combined with inertial sensors and GPS/Wi-Fi for indoor navigation and Location Based Services (LBS) applications.

show abstract

Section: Particle Swarm Optimizationmentioning

confidence: 99%