scholarly journals An Adaptive Density-Based Fuzzy Clustering Track Association for Distributed Tracking System

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 135972-135981
Author(s):  
Mousa Nazari ◽  
Saeid Pashazadeh ◽  
Leyli Mohammad-Khanli
Keyword(s):  
Fuzzy Clustering ◽  
Tracking System ◽  
Track Association ◽  
Distributed Tracking

scholarly journals Distributed Target Tracking in Challenging Environments Using Multiple Asynchronous Bearing-Only Sensors

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2671
Author(s):  
Yifang Shi ◽  
Jee Woong Choi ◽  
Lei Xu ◽  
Hyung June Kim ◽  
Ihsan Ullah ◽  
...  
Keyword(s):  
Measurement Accuracy ◽  
Tracking System ◽  
Sampling Interval ◽  
Fusion Center ◽  
Forward Prediction ◽  
Distributed Target ◽  
Calculated Probability ◽  
Track Management ◽  
Internal Processing ◽  
Distributed Tracking

In the multiple asynchronous bearing-only (BO) sensors tracking system, there usually exist two main challenges: (1) the presence of clutter measurements and the target misdetection due to imperfect sensing; (2) the out-of-sequence (OOS) arrival of locally transmitted information due to diverse sensor sampling interval or internal processing time or uncertain communication delay. This paper simultaneously addresses the two problems by proposing a novel distributed tracking architecture consisting of the local tracking and central fusion. To get rid of the kinematic state unobservability problem in local tracking for a single BO sensor scenario, we propose a novel local integrated probabilistic data association (LIPDA) method for target measurement state tracking. The proposed approach enables eliminating most of the clutter measurement disturbance with increased target measurement accuracy. In the central tracking, the fusion center uses the proposed distributed IPDA-forward prediction fusion and decorrelation (DIPDA-FPFD) approach to sequentially fuse the OOS information transmitted by each BO sensor. The track management is carried out at local sensor level and also at the fusion center by using the recursively calculated probability of target existence as a track quality measure. The efficiency of the proposed methodology was validated by intensive numerical experiments.

A Track Association Algorithm Based on Transitive Closure Fuzzy Clustering

2021 ◽  
pp. 1929-1940
Author(s):  
Yinghao Huang ◽  
Kaihua Zhang ◽  
Jing Wang ◽  
Yunze Cai
Keyword(s):  
Fuzzy Clustering ◽  
Transitive Closure ◽  
Track Association ◽  
Association Algorithm

scholarly journals Distributed Tracking System for Patients with Cognitive Impairments

2011 ◽  
pp. 49-56 ◽  
Author(s):  
Xabier Laiseca ◽  
Eduardo Castillejo ◽  
Pablo Orduña ◽  
Aitor Gómez-Goiri ◽  
Diego López-de-Ipiña ◽  
...  
Keyword(s):  
Tracking System ◽  
Cognitive Impairments ◽  
Distributed Tracking

Adaptive Tracking Algorithm of Multi-Target Based on Fuzzy Clustering

2014 ◽  
Vol 513-517 ◽  
pp. 448-452
Author(s):  
Xiu Hua Hu ◽  
Lei Guo ◽  
Hui Hui Li
Keyword(s):  
State Estimation ◽  
Target Tracking ◽  
Fuzzy Clustering ◽  
Tracking System ◽  
Joint Probability ◽  
Tracking Algorithm ◽  
Adaptive Tracking ◽  
Correlation Criterion ◽  
Multi Target Tracking ◽  
Fuzzy Clustering Theory

For multi-target tracking system, aiming at solving the problem of low precision of state estimation caused by the data correlation ambiguity, the paper presents a novel multi-sensor multi-target adaptive tracking algorithm based on fuzzy clustering theory. Based on the joint probability data association algorithm, the new approach takes account of the case that whether the measure is validated and its possibility of belong to false alarm, and improves the correlation criterion of effective measurement with existing track on the basis of fuzzy clustering theory, which all perfect the update equation of target state estimation and the covariance. Meanwhile, with the adaptive distributed fusion processing structure, it enhance the robustness of the system and without prejudice to the real-time tracking. With the simulation case studies of radar/infrared sensor fusion multi-target tracking system, it verifies the effectiveness of the proposed approach.

MODEL IDENTIFICATION OF A SERVO-TRACKING SYSTEM USING FUZZY CLUSTERING

1999 ◽  
Vol 07 (04) ◽  
pp. 337-346
Author(s):  
ERIC M. NGUYEN ◽  
NADIPURAM R. PRASAD
Keyword(s):  
Least Squares ◽  
Observational Data ◽  
Fuzzy Clustering ◽  
Least Squares Method ◽  
Tracking System ◽  
Model Identification ◽  
Automatic Generation ◽  
Non Linear ◽  
Servo Tracking ◽  
Takagi Sugeno

This paper investigates the use of Fuzzy Clustering as a means for model identification of a complex and highly non-linear servo-tracking system when only observational data is available. The use of Fuzzy Clustering facilities automatic generation of rules and its antecedent parameters. The consequent of the model is then formulated in the form of Takagi, Sugeno and Kang (TSK), and its parameters determined by the Least Squares Method (LSM).

Sign in / Sign up

Export Citation Format

Share Document