A Fast JPDA-IMM-PF based DFS Algorithm for Tracking Highly Maneuvering Targets

نویسندگان

  • Mohand Saïd Djouadi
  • Yacine Morsly
  • Daoud Berkani
چکیده

In this paper, we present an interesting filtering algorithm to perform accurate estimation in jump Markov nonlinear systems, in case of multi-target tracking. With this paper, we aim to contribute in solving the problem of modelbased body motion estimation by using data coming from visual sensors. The Interacting Multiple Model (IMM) algorithm is specially designed to track accurately targets whose state and/or measurement (assumed to be linear) models changes during motion transition. However, when these models are nonlinear, the IMM algorithm must be modified in order to guarantee an accurate track. In order to deal with this problem, the IMM algorithm was combined with the Unscented Kalman Filter (UKF) [6]. Even if the later algorithm proved its efficacy in nonlinear model case; it presents a serious drawback in case of non Gaussian noise. To deal with this problem we propose to substitute the UKF with the Particle Filter (PF). To overcome the problem of data association, we propose the use of an accelerated JPDA approach based on the depth first search (DFS) technique [12]. The derived algorithm from the combination of the IMMPF algorithm and the DFS-JPDA approach is noted DFS-JPDAIMM-PF.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

GA-JPDA-IMM-PF Algorithm for Tracking Highly Maneuvering Targets

In this paper, we present an interesting filtering algorithm to perform accurate estimation in jump Markov nonlinear systems, in case of multi-target tracking. With this paper, we aim to contribute in solving the problem of modelbased body motion estimation by using data coming from visual sensors. The Interacting Multiple Model (IMM) algorithm is specially designed to track accurately targets ...

متن کامل

Joint Particle Filtering of Multiple Maneuvering Targets From Unassociated Measurements

In the literature approximate Bayesian approaches towards maintaining tracks of multiple maneuvering targets from unassociated measurements have focussed on the development of combinations of Interacting Multiple Model (IMM) and Joint Probabilistic Data Association (JPDA) approaches. Initially, combinations of IMM and JPDA have been developed along two heuristic directions. Bar-Shalom et al. [4...

متن کامل

Multiple Maneuvering Target Tracking by Improved Particle Filter Based on Multiscan JPDA

The multiple maneuvering target tracking algorithm based on a particle filter is addressed. The equivalent-noise approach is adopted, which uses a simple dynamic model consisting of target state and equivalent noise which accounts for the combined effects of the process noise and maneuvers. The equivalent-noise approach converts the problem of maneuvering target tracking to that of state estima...

متن کامل

An Accelerated Imm-jpda Algorithm for Tracking Multiple Maneuvering Targets in Clutter

By far, the most complicated case in target tracking is to track multiple maneuvering targets in heavy clutter. Numerous methods and algorithms have been devoted to this problem and for any one of them pros and cons can be pointed out. Theoretically, for example, the MHT method is known to be the most powerful approach to tracking multiple maneuvering targets in clutter. This method, however, v...

متن کامل

Tracking Multiple Maneuvering Targets by Joint Combinations of IMM and PDA

For the problem of tracking multiple manoeuvering targets in false and missing measurements the paper develops a characterization of the exact Bayesian equations of the conditional density. Since in these exact equations both IMM and PDA are Jointly performed over all targets, we also develop two Joint IMMPDA type of filters and compare them with other combinations of IMM and JPDA through Monte...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2006