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Techniques of Multiple Passive Sensors Multiple Targets Tracking Based on Random Finite Sets Theory

Author: ZhaoXin
Tutor: JiHongBing
School: Xi'an University of Electronic Science and Technology
Course: Pattern Recognition and Intelligent Systems
Keywords: Multi-target tracking Passive Observing System Stochastic finite set Probability hypothesis density Particle filter
CLC: TP212.9
Type: Master's thesis
Year: 2009
Downloads: 425
Quote: 13
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Abstract


Passive multi- sensor multi - target tracking technology is one of the important research content of the target tracking system has demonstrated effective and broad theoretical and applied prospects in military and civilian fields . However, due to technological advances improve vehicle performance and electronic countermeasures , modern target tracking environment change , strong maneuvering target , background clutter , the observation of the high false alarm and the uncertainty of the target number of characteristics , making modern passive target tracking technology encountered great challenges . In response to these problems , the paper focuses on a random set theory - based passive multi- sensor multi-target tracking algorithm . First, passive multi-sensor system data processing methods . For a fixed number of multiple maneuvering targets , one for passive observation system associated with the particle filter tracking algorithm , multiple nonlinear moving target tracking . Secondly, the introduction of multi- target tracking random set theory , hypothesis density (PHD) based on the probability of a random set of variable dimension information fusion method . Studied for high clutter environment of high false alarm , the target number of changes in the non-linear multi- target tracking problem , the PHD algorithm the Gaussian and particle sampling (GSPPHD) and using the quasi Monte Carlo sampling GSPPHD algorithm has been improved . The experiments show that the proposed improved algorithm to ensure the accuracy of the track at the same time , reduce the amount of GSPPHD computation . In addition, for high clutter environment of high false alarm , the number of changes in motor multi- target tracking problems , interacting multiple model PHD (IMM-PHD) multi-sensor maneuvering target tracking algorithm . The simulation results show that the IMM -PHD algorithm for the existence of a large number of false alarm interference , multiple maneuvering targets only through the perspective of information about the target in real time tracking the number of changes . Finally, the the PHD tracking algorithm for random sets difficult to form individual target track , a new Rao-Blackwellized particle filter (RBPF) based on a random set of correlation algorithms , combined with the PHD algorithm , multi- target tracking its flight path detection and association .

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CLC: > Industrial Technology > Automation technology,computer technology > Automation technology and equipment > Automation components,parts > Transmitter ( converter),the sensor > Sensor applications
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