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Multi- sensor data fusion target tracking algorithm

Author: ZhengLiYi
Tutor: ChenXingWu；WangLei
School: Chinese Academy of Engineering Physics
Course: Optical Engineering
Keywords: Data Fusion Target tracking Interacting multiple model Probabilistic Data Association
CLC: TP202
Type: Master's thesis
Year: 2005
Downloads: 1154
Quote: 9
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Abstract


Data fusion technology is just emerging in the nineties of the last century, an information processing disciplines. It's a very wide range of applications, including military and civilian use in many areas such as: target tracking and recognition, medical diagnostics, traffic control, industrial robots and so on. For now, the target tracking and recognition is still the most important issue of data fusion technology research. This paper systematically in-depth study of a single multi-sensor data fusion target tracking problem. First, the theoretical basis of the emerging discipline of data fusion, implementation techniques, research status and significance are reviewed, and an overview of the multi-sensor fusion target tracking technology; Secondly discussed the choice of the coordinate system in which the target tracking and target motion model The key issue of the formation of the door of the establishment of tracking and Kalman filtering methods; Based on this study and improvement of the two infrared / radar multi-sensor fusion tracking algorithm. Based on the weighted average of the fusion algorithm is simple and practical. Analysis we found that if you use the unconstrained extremum obtained by the Lagrangian multiplier method as weight coefficient, the the fusion accuracy can achieve the best. At the same time, in addition, efforts to solve the problem of image information is converted to infrared sensors to measure the angle information in the inertial frame. And using the least squares method to achieve heterogeneous sensor to measure the temporal and spatial alignment. Simulation results show that: the two sensor fusion tracking performance is superior to any single sensor. A research focus in the field of target tracking is how to solve the problem of clutter and adapt to a highly mobile targets. Probabilistic data association algorithm and interacting multiple model algorithm to solve the above problems. The article Bar-shalom interactive multi-model - probabilistic data association algorithm, a promotion to the method in the case of multi-sensor. The simulation results show that the use of multi-sensor fusion tracking not only improved accuracy, and the ability to adapt to better target maneuver and clutter single sensor. Finally, the article this multi-sensor, multi-model / smooth probabilistic data association algorithm applied to the fixed delay state which extended through the state. The simulation results show that: even if only delayed step, tracking accuracy improvement is obvious. For each algorithm, we use the Monte Carlo method Simulation results show the effectiveness. How the hardware system design algorithm to be realized will be the focus of further work.

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CLC: > Industrial Technology > Automation technology,computer technology > Automation technology and equipment > General issues > Design, performance analysis and synthesis
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