A t-Distribution-Based Particle Filter for Bearings-Only Tracking

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E. B. Nkemnole
O. Abass


Bearings-only target tracking is a nonlinear estimation problem often addressed by linearised filters where the uncertainty in the sensor and motion models is typically modeled by Gaussian densities. In this paper, a particle filter or sequential Monte Carlo method is developed, based on student-t distribution, which is heavier tailed than Gaussian’s and hence more robust. The t-distribution-based particle filter provides an approximate solution to nonlinear non-Gaussian estimation problems. To estimate the target state based on samples, an expectation maximisation (EM)-type algorithm was developed and embedded in a student-t particle filter. The expectation step was implemented by the particle filter. In this step, the distribution of the states and the state vector were estimated. Consequently, in the maximisation step, the nonlinear observation equation was approximated as a mixture of the Gaussian and student-t models. A bearings-only tracking problem was simulated to present the implementation of the particle filter algorithm based on both the mixture of the Gaussian model and student-t. Simulations and real life data taken from the digital global system for mobile communications (GSM) real-time data-logging tracking system showed that the student-t-based particle filter significantly outperformed the Gaussian mixture filter and successfully accommodated a nonlinear model for a target-tracking scenario.

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Nkemnole, E., & Abass, O. (1). A t-Distribution-Based Particle Filter for Bearings-Only Tracking. Journal of Scientific Research and Development, 17(1), 57-64. Retrieved from http://jsrd.unilag.edu.ng/article/view/43


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