A Hybrid Multi-sensor Multi-target Tracking Scheme with MLE and ANFIS | Zendy

Liyun Su | Zendy

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A Hybrid Multi-sensor Multi-target Tracking Scheme with MLE and ANFIS

Author(s) -

Liyun Su

Publication year - 2014

Publication title -

international journal of information technology and computer science

Language(s) - English

Resource type - Journals

eISSN - 2074-9015

pISSN - 2074-9007

DOI - 10.5815/ijitcs.2014.02.02

Subject(s) - computer science , combing , adaptive neuro fuzzy inference system , scheme (mathematics) , realization (probability) , clutter , probabilistic logic , tracking (education) , set (abstract data type) , data association , artificial intelligence , algorithm , fuzzy logic , data mining , fuzzy control system , mathematics , radar , statistics , psychology , mathematical analysis , pedagogy , telecommunications , cartography , programming language , geography

The Joint Probabilistic Data Association (JPDA) solves single sensor multi-target tracking in clutter, but it cannot be used directly in multi-sensor multi-target tracking (MMT) and has high computational complexity with the number of targets and the number of returns. This paper presents a hybrid method to implement MMT by combing Maximum Likelihood Estimation (MLE) with Adaptive Neuro-Fuzzy Inference System (ANFIS). The MLE is applied to classify the same source observations at one time into the same set, then the cheap JPDA(CJPDA) approach is used to calculate the data association probability, and ANFIS is used to realize the MMT. The computer simulations indicate that this scheme achieves MMT perfectly with higher precision and easy realization.

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