The probability hypothesis density (PHD) filter is an efficient algorithm for multitarget tracking in the presence of nonlinearities and/or non-Gaussian noise. The sequential Monte Carlo (SMC) and Gaussian mixture (GM) techniques are commonly used to implement the PHD filter. Recently, a new implementation of the PHD filter using B-splines with the capability to model any arbitrary density functions using only a few knots was proposed. The spline PHD (SPHD) filter was found to be more robust than the SMC-PHD filter because it does not suffer from degeneracy, and it was better than the GM-PHD implementation in terms of estimation accuracy, albeit with a higher computational complexity. In this paper, we propose a multiple model extension to the SPHD filter to track multiple maneuvering targets. Simulation results are presented to demonstrate the effectiveness of the new filter.


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    Title :

    Multiple model spline probability hypothesis density filter




    Publication date :

    2016-06-01


    Size :

    1086195 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English