A Diversity Enhanced Particle Filter for Carrier Frequency Offset Estimation in Nonlinear OFDM System

Malarvezhi, P.; Kumar, Raghvendra

doi:10.1007/s11277-016-3248-2

Cited by 3 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the sequential importance re-sampling particle filter (SIR-PF), a duplicating particle is the main reason for loss of the particle diversity [ 37 ]. Compared with the algorithm program 5.6 proposed by Thrun [ 11 ], HPFL adds Gaussian noise to the predicted robot pose to enhance the local diversity of particles, making the robot relocate, even if it is not moving, when the error between maximum likelihood estimate (MLE) and posterior estimate is large.…”

Section: System Overview and Methodologymentioning

confidence: 99%

Design of a Hybrid Indoor Location System Based on Multi-Sensor Fusion for Robot Navigation

Shi

Zhang

Yao

et al. 2018

Sensors

View full text Add to dashboard Cite

In the case of a single scene feature, the positioning of an indoor service robot takes a long time, and localization errors are likely to occur. A new method for a hybrid indoor localization system according to multi-sensor fusion is proposed to solve these problems. The localization process is divided in two stages: rough positioning and precise positioning. By virtue of the K nearest neighbors based on possibility (KNNBP) algorithm first created in the present study, the rough position of a robot is determined according to the received signal strength indicator (RSSI) of Wi-Fi. Then, the hybrid particle filter localization (HPFL) algorithm improved on the basis of adaptive Monte Carlo localization (AMCL) is adopted to get the precise localization, which integrates various information, including the rough position and information from Lidar, a compass, an occupancy grid map, and encoders. The experiments indicated that the positioning error was 0.05 m; the success rate of localization was 96% with even 3000 particles, and the global positioning time was 1.9 s. However, under the same conditions, the success rate of AMCL was approximately 40%, the required time was approximately 25.6 s, and the positioning accuracy was the same. This indicates that the hybrid indoor location system is efficient and accurate.

show abstract

Section: System Overview and Methodologymentioning

confidence: 99%