WLAN Indoor Tracking Method via Improved Particle Filter Algorithm

2010 ◽  
Author(s):  
Yubin Xu ◽  
Jingyu Liu ◽  
Lin Ma ◽  
Lang Peng
Keyword(s):  
Particle Filter ◽  
Tracking Method ◽  
Indoor Tracking ◽  
Particle Filter Algorithm

scholarly journals Modeling the indoor tracking method based on particle water wave optimization

2020 ◽  
Author(s):  
Yangyong Guo
Keyword(s):  
Particle Filter ◽  
Signal Intensity ◽  
Water Wave ◽  
Indoor Environment ◽  
Estimation Algorithm ◽  
Tracking Accuracy ◽  
Simulation Experiments ◽  
Tracking Method ◽  
Node Location ◽  
Indoor Tracking

Abstract A new tracking algorithm is proposed based on particle filter and water wave optimization for the problem that indoor tracking accuracy is not high enough. In this algorithm, the observation equation of the node location is first given using the least squares estimation algorithm based on the node signal intensity. Meanwhile, the algorithm steps of indoor tracking are designed using the water wave optimization, and the particle filter is introduced to accelerate the convergence. Finally, the performance of the algorithm is simulated through simulation experiments. The results show that the algorithm can adapt well to indoor environment.

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.

A Trust Region-Based Particle Filter Algorithm for Indoor Tracking

2014 ◽  
Vol 80 (2) ◽  
pp. 739-750 ◽  
Author(s):  
Liang Zhou ◽  
Guangjun Li ◽  
Zhi Zheng ◽  
Tingting Xiao
Keyword(s):  
Particle Filter ◽  
Trust Region ◽  
Indoor Tracking ◽  
Particle Filter Algorithm

Tracking method of space IR small target based on improved particle filter

2010 ◽  
Vol 24 (11) ◽  
pp. 1007-1011
Author(s):  
Xuezhi Xiang ◽  
Yu Peng ◽  
Zhiying Han ◽  
Zhihong Xi
Keyword(s):  
Particle Filter ◽  
Small Target ◽  
Tracking Method

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.

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