Paper
16 September 2005 An optimization-based parallel particle filter for multitarget tracking
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Abstract
Particle filter based estimation is becoming more popular because it has the capability to effectively solve nonlinear and non-Gaussian estimation problems. However, the particle filter has high computational requirements and the problem becomes even more challenging in the case of multitarget tracking. In order to perform data association and estimation jointly, typically an augmented state vector of target dynamics is used. As the number of targets increases, the computation required for each particle increases exponentially. Thus, parallelization is a possibility in order to achieve the real time feasibility in large-scale multitarget tracking applications. In this paper, we present a real-time feasible scheduling algorithm that minimizes the total computation time for the bus connected heterogeneous primary-secondary architecture. This scheduler is capable of selecting the optimal number of processors from a large pool of secondary processors and mapping the particles among the selected processors. Furthermore, we propose a less communication intensive parallel implementation of the particle filter without sacrificing tracking accuracy using an efficient load balancing technique, in which optimal particle migration is ensured. In this paper, we present the mathematical formulations for scheduling the particles as well as for particle migration via load balancing. Simulation results show the tracking performance of our parallel particle filter and the speedup achieved using parallelization.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
S. Sutharsan, A. Sinha, T. Kirubarajan, and M. Farooq "An optimization-based parallel particle filter for multitarget tracking", Proc. SPIE 5913, Signal and Data Processing of Small Targets 2005, 591309 (16 September 2005); https://doi.org/10.1117/12.618456
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Cited by 12 scholarly publications.
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KEYWORDS
Particles

Particle filters

Detection and tracking algorithms

Technetium

Algorithm development

Computer simulations

Computing systems

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