Considering Uncertain System Parameters in Multitarget Space Surveillance Tracking


Consider analysis is an estimation technique that emerged in the 1960s to account for errors in system parameters while simultaneously reducing system dimensionality, and accordingly real-time computational cost, and/or guarding against issues of observability surrounding the parameters. The multitarget joint estimation problem is one whose dynamical and observational systems contain such parameter errors, and these errors can drastically impact the performance of a suboptimal recursion, such as the probability hypothesis density (PHD) filter. A consider formulation of the Gaussian mixture PHD filter is proposed to treat such problems while accounting for errors in system parameters without neglecting or directly estimating them. The proposed algorithm is applied to an example that illustrates its value in space object tracking and orbit determination.

Meeting Name

19th International Conference on Information Fusion (2016: Jul. 5-8, Heidelberg, GE)


Mechanical and Aerospace Engineering

Keywords and Phrases

Errors; Information fusion; Probability density function; Space surveillance; Uncertainty analysis; Computational costs; Estimation techniques; Gaussian mixture PHD; Joint estimation problems; Orbit determination; Probability hypothesis density filter; Reducing systems; Uncertain system parameters; Parameter estimation

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


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© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Aug 2016

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