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.

Tracking Error Modeling of the Theodolite Based on GRNN Method

2011 ◽  
Vol 121-126 ◽  
pp. 4870-4874
Author(s):  
Miao Li ◽  
Hui Bin Gao
Keyword(s):  
Evaluation Method ◽  
Tracking Error ◽  
High Accuracy ◽  
Error Model ◽  
Error Modeling ◽  
General Regression Neural Network ◽  
Engineering Practice ◽  
Tracking Accuracy ◽  
Model Based ◽  
General Regression

To meet the requirement of high tracking accuracy as well as develop more reasonable evaluation method, in this paper, the General Regression Neural Network (GRNN) has been applied to build the tracking error model of the theodolite. First, we analyze the nonlinear factors in the theodolite. Second, we discuss the principle of GRNN, including its structure, the function as well as its priors. Third, we build the tracking error model based on GRNN and verify the model through the different parameters. The result indicated that the network model based on GRNN has high accuracy and good generalization ability. It could instead the real system to a certain extent. The research in this paper has important value to the engineering practice.

scholarly journals The study of particle filter for satellite angular rate estimation without rate sensor measurement

2018 ◽  
Vol 150 ◽  
pp. 06010
Author(s):  
Nor Hazadura Hamzah ◽  
Sazali Yaacob ◽  
Ahmad Kadri Junoh ◽  
Mohd Zamri Hasan
Keyword(s):  
Particle Filter ◽  
Angular Rate ◽  
Euler Angles ◽  
Algorithm Performance ◽  
Flight Data ◽  
Computational Aspects ◽  
Sensor Measurement ◽  
Particle Filter Algorithm ◽  
Rate Sensor ◽  
Number Of Particles

This paper studies particle filter algorithm to estimate the angular rate of a satellite without the rate sensor measurements. In this work, the performance of the algorithm is studied in terms of capability to estimate the angular rate by using the Euler angles attitude information only. The effects of the number of particles on the algorithm performance are also investigated in terms of accuracy and computational aspects. The performance of the particle filter algorithm is verified using real flight data of Malaysian satellite.

scholarly journals Improved Interacting Multiple Model Particle Filter Algorithm

2018 ◽  
Vol 36 (1) ◽  
pp. 169-175 ◽  
Author(s):  
Qiaoran Liu ◽  
Xun Yang
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
Multiple Model ◽  
Tracking Accuracy ◽  
Proposal Distribution ◽  
Model Particle ◽  
Interactive Multiple Model ◽  
Particle Filter Algorithm

For the issue of limited filtering accuracy of interactive multiple model particle filter algorithm caused by the resampling particles don't contain the latest observation information, we made improvements on interactive multiple model particle filter algorithm in this paper based on mixed kalman particle filter algorithm. Interactive multiple model particle filter algorithm is proposed. In addition, the composed methods influence to tracking accuracy are discussed. In the new algorithm the system state estimation is generated with unscented kalman filter (UKF) first and then use the extended kalman filter (EKF) to get the proposal distribution of the particles, taking advantage of the measure information to update the particles' state. We compare and analyze the target tracking performance of the proposed algorithm of IMM-MKPF in this paper, IMM-UPF and IMM-EPF through the simulation experiment. The results show that the tracking accuracy of the proposed algorithm is superior to other two algorithms. Thus, the new method in this paper is effective. The method is of important to improve tracking accuracy further for maneuvering target tracking under the non-linear and non-Gaussian circumstances.

scholarly journals Research on the Particle Filter Single-Station Target Tracking Algorithm Based on Particle Number Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lieping Zhang ◽  
Jinghua Nie ◽  
Shenglan Zhang ◽  
Yanlin Yu ◽  
Yong Liang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Real Time ◽  
Particle Number ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
Time Performance ◽  
Single Station ◽  
Tracking Model ◽  
Number Of Particles

Given that the tracking accuracy and real-time performance of the particle filter (PF) target tracking algorithm are greatly affected by the number of sampled particles, a PF target tracking algorithm based on particle number optimization under the single-station environment was proposed in this study. First, a single-station target tracking model was established, and the corresponding PF algorithm was designed. Next, a tracking simulation experiment was carried out on the PF target tracking algorithm under different numbers of particles with the root mean square error (RMSE) and filtering time as the evaluation indexes. On this basis, the optimal number of particles, which could meet the accuracy and real-time performance requirements, was determined and taken as the number of particles of the proposed algorithm. The MATLAB simulation results revealed that compared with the unscented Kalman filter (UKF), the single-station PF target tracking algorithm based on particle number optimization not only was of high tracking accuracy but also could meet the real-time performance requirement.

Wave Filtering of Ship Dynamic Positioning System Using Particle Filter

2013 ◽  
Vol 397-400 ◽  
pp. 551-555
Author(s):  
Wen Juan Li ◽  
Hai Xiang Xu ◽  
Hui Feng
Keyword(s):  
Particle Filter ◽  
Low Frequency ◽  
Nonlinear Filter ◽  
Dynamic Positioning ◽  
Positioning System ◽  
Dynamic Positioning System ◽  
Frequency Position ◽  
Particle Filter Algorithm

This paper presents a nonlinear filter which is particle filter. The filter produces accurate estimates of low-frequency position and velocity only from measured values of ship position and heading in Dynamic Positioning System. The results of simulation confirm the validity and adaptability of the particle filter algorithm.

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