tailieunhanh - Báo cáo nghiên cứu khoa học: " Ground Mobile Target Tracking By Hidden Markov Model"

Manuscript Revised November 13 , 2006) ABSTRACT: In recent years, there are many researches of finding the efficient method for tracking moving objects using some fundamental and traditional instruments such as GPS, video camera, | TẠP CHÍ PHÁT TRIỂN KH CN TẬP 9 SÓ 12 - 2006 Ground Mobile Target Tracking By Hidden Markov Model Huynh Quan Hieu Vu Dinh Thanh University of Technology VNU-HCM Manuscript Received on January 26th 2006 Manuscript Revised November 13th 2006 ABSTRACT In recent years there are many researches of finding the efficient method for tracking moving objects using some fundamental and traditional instruments such as GPS video camera . The most important task is to find out the accompanying algorithm to realize this tracking with a better precision. In this paper an algorithm based on Hidden Markov Model HMM for ground mobile target tracking is introduced. The HMM is a good choice for modeling moving process because of its character in modeling sequential processes. Suppose the moving object is on a known ground plane and the HMM parameters are the motion capture data position speed steering angle . . Once the HMM for the target motion has been constructed the Viterbi algorithm is applied to find the trajectory of the target. Some illustrating results in modeling a moving target for instance a mobile cell phone will be presented. Besides two kinds of tracking program non-learning and learning are compared and examined by evaluating the distant errors. 1. INTRODUCTION The mobile target tracking is one of new researches which appear in the next generation of mobile communication where all the mobile system requires higher performances in operation. In the real mobile system the positions of object are not known exactly and directly but we can detect them through a time sequence of measurements. This shows that there are two processes in parallel the first process involves the real movement of the target which has to recognize and the other is the accumulated observation sequences which are provided by the first one. Such problems are the same for speech recognition or video tracking. This paper uses the results of works based on image sequences obtained by fixed surveillance .