Data association for PHD filter based on MHT | IEEE Conference Publication | IEEE Xplore

Data association for PHD filter based on MHT


Abstract:

The main drawback of probability hypothesis density (PHD) filter is that it canpsilat identify the trajectories of the different targets. Data association for PHD filter ...Show More

Abstract:

The main drawback of probability hypothesis density (PHD) filter is that it canpsilat identify the trajectories of the different targets. Data association for PHD filter based on multiple hypotheses tracking (MHT) is presented to solve the problem. The track-oriented MHT is used to perform data association on the output of PHD filter. An adaptive Kalman filter based on ldquocurrentrdquo statistic model, combined with MHT, is implemented to track maneuvering targets. Two examples are given to test the performance of the new method. Monte Carlo simulation results show that this approach is computationally feasible and effective for associating multi-targets in dense clutter environments.
Date of Conference: 30 June 2008 - 03 July 2008
Date Added to IEEE Xplore: 26 September 2008
ISBN Information:
Conference Location: Cologne

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