• DocumentCode
    1734270
  • Title

    Interacting Multiple Model Algorithm Used In Multi-Sensor Fusion System

  • Author

    Guang-yuan, Zhang ; Fu-jun, Wang ; Zhen-sheng, Wei

  • Author_Institution
    Department of Optics and Electronics, Ordnance Engineering College, Shijiazhuang 050003, China
  • fYear
    2007
  • Abstract
    In recent years, along with the development of information fusion technique, surveillance and tracking systems have relied more and more on the multi-sensor systems (MSS), which is comprised of multiple sensors working in a coordinated way to provide more accurate and reliable state estimates of targets than isolated sensors. In these systems, fusion usually plays a critical role in combining information. There are many fusion techniques, and most of them fall into two categories - measurement fusion and track fusion, depending on what kind of information is to be shared among sensors. Some researchers have shown that the use of multiple sensor data can sometimes degrade performance when a single model filter (e.g., the Kalman filter) is used. In this paper we consider a distributed track fusion system, in which use interacting multiple model (IMM) filter , educe a distributed multiple sensors IMM fusion algorithm and investigate in more detail how the performance of the IMM is affected when it is used with multiple sensors.
  • Keywords
    Kalman filters; sensor fusion; surveillance; tracking filters; Kalman filter; distributed track fusion system; information fusion; interacting multiple model filter; measurement fusion; multiple model algorithm; multisensor fusion system; single model filter; surveillance systems; tracking systems; Filters; Instruments; Intelligent sensors; Optical sensors; Position measurement; Sensor fusion; Sensor systems; State estimation; Surveillance; Target tracking; IMM; Kalman; information fusion; state estimate;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electronic Measurement and Instruments, 2007. ICEMI '07. 8th International Conference on
  • Conference_Location
    Xi´an
  • Print_ISBN
    978-1-4244-1136-8
  • Electronic_ISBN
    978-1-4244-1136-8
  • Type

    conf

  • DOI
    10.1109/ICEMI.2007.4351101
  • Filename
    4351101