A significant problem in multi-sensor multi-target tracking system is measurement to track association. Based on fuzzy clustering means algorithm, an efficient algorithm has been proposed to solve this problem. The fuzzy clustering means data association (FCMDA) algorithm has better performance than the other already known fuzzy logic data association algorithms. However, it is still worthy to investigate the characteristics of the FCMDA algorithm, which has high accuracy in measurement to track association when targets are far from each other, while it has low accuracy when targets are close to each other. The FCMDA algorithm usually loses its performance in this situation, especially when the noise of measurement is high. In this paper, to overcome the disadvantage of the FCMDA algorithm, an adaptive neuro-fuzzy inference system (ANFIS) is used. The ANFIS adjusts the predicted state of targets which are used as cluster centers in the FCMDA algorithm. The ANFIS has the advantage of expert knowledge of fuzzy inference system and the learning capability of neural networks. This is so, since a trained ANFIS is able to compensate the effect of wrong data association in the FCMDA algorithm. Monte Carlo simulation results show considerable improvement in terms of accuracy and performance achieved by using the ANFIS in the FCMDA algorithm.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Fuzzy clustering means data association algorithm using an adaptive neuro-fuzzy network


    Contributors:


    Publication date :

    2009-03-01


    Size :

    206253 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Intelligent Parallel Parking Using Adaptive Neuro-Fuzzy Inference System Based on Fuzzy C-Means Clustering Algorithm

    Rezaei Nedamani, Hamidreza / Masnadi Khiabani, Parisa / Azadi, Shahram | SAE Technical Papers | 2018


    Adaptive Neuro-Fuzzy Model with Fuzzy Clustering for Nonlinear Prediction and Control

    Al-Himyari, Bayadir Abbas / Yasin, Azman / Gitano, Horizon | BASE | 2014

    Free access

    Prediction of Automotive Ride Performance Using Adaptive Neuro-Fuzzy Inference System and Fuzzy Clustering

    Shi, Tianze / Chen, Shuming / Wang, Dengfeng | British Library Conference Proceedings | 2015


    Prediction of Automotive Ride Performance Using Adaptive Neuro-Fuzzy Inference System and Fuzzy Clustering

    Wang, Dengfeng / Chen, Shuming / Shi, Tianze | SAE Technical Papers | 2015


    Adaptive Neuro-Fuzzy Controller

    Kishan Kumar Kumbla / Jamshidi, M. / Rodrieguiz, S. | British Library Conference Proceedings | 1997