Accurate Location Estimation for a Dynamically Moving Object Using Modified Particle Filter Algorithm

2009 ◽  
Author(s):  
N. Karthick ◽  
Keshav Prashanth ◽  
K. Venkatraman ◽  
Amutha Nanmaran ◽  
J. Naren
Keyword(s):  
Particle Filter ◽  
Location Estimation ◽  
Moving Object ◽  
Particle Filter Algorithm

scholarly journals A Moving Object Indoor Tracking Model Based on Semiactive RFID

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Hongshan Kong ◽  
Bin Yu
Keyword(s):  
Particle Filter ◽  
Tracking Error ◽  
Low Frequency ◽  
Moving Object ◽  
Tracking Accuracy ◽  
Algorithm Performance ◽  
Model Based ◽  
Tracking Model ◽  
Indoor Tracking ◽  
Particle Filter Algorithm

Aimed at the weak anti-interference and low accuracy problem of moving object indoor tracking based RFID, a moving object indoor tracking model based on semiactive RFID is presented. This model acquires scene location information through RFID low frequency triggers preinstalled, which can enhance the anti-interference ability. This model adopts an improved particle filter algorithm, which can increase the diversity of the particles, overcome the particle impoverishment, and reduce the tracking error. Simulation results indicate that the model can achieve better tracking performances. Compared with standard particle filter, the improved algorithm performance is better in the capability of tracking accuracy and robust and is more suitable for indoor tracking application in the complicated environments.

scholarly journals Unscented Particle Filter Algorithm Based on Divide-and-Conquer Sampling for Target Tracking

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2236
Author(s):  
Sichun Du ◽  
Qing Deng
Keyword(s):  
Particle Filter ◽  
State Space ◽  
Adaptive Filtering ◽  
Divide And Conquer ◽  
Particle Swarm Algorithm ◽  
Tracking Accuracy ◽  
Filtering Algorithm ◽  
Maneuvering Target ◽  
Unscented Particle Filter ◽  
Particle Filter Algorithm

Unscented particle filter (UPF) struggles to completely cover the target state space when handling the maneuvering target tracing problem, and the tracking performance can be affected by the low sample diversity and algorithm redundancy. In order to solve this problem, the method of divide-and-conquer sampling is applied to the UPF tracking algorithm. By decomposing the state space, the descending dimension processing of the target maneuver is realized. When dealing with the maneuvering target, particles are sampled separately in each subspace, which directly prevents particles from degeneracy. Experiments and a comparative analysis were carried out to comprehensively analyze the performance of the divide-and-conquer sampling unscented particle filter (DCS-UPF). The simulation result demonstrates that the proposed algorithm can improve the diversity of particles and obtain higher tracking accuracy in less time than the particle swarm algorithm and intelligent adaptive filtering algorithm. This algorithm can be used in complex maneuvering conditions.

A quick evaluation method for the lifetime of the fuel cell MEA with the particle filter algorithm

2021 ◽  
pp. 1-14
Author(s):  
Luyan He ◽  
Zhigang Zhan ◽  
Hong Chen ◽  
Panxing Jiang ◽  
Yuan Yu ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Particle Filter ◽  
Evaluation Method ◽  
Particle Filter Algorithm
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