Abstract
In this chapter we first recall the notion of contracted GPTs. Then we show that the CGPTs have some nice properties, such as simple rotation and translation formulas, simple relation with shape symmetry, etc. More importantly, we derive new invariants for the CGPTs. Based on those invariants, we develop a dictionary matching algorithm. We suppose that the unknown shape of the target is an exact copy of some element from the dictionary, up to a rigid transform and dilatation. Using the invariants, we identify the target in the dictionary with a low computational cost. We also apply the Extended Kalman Filter to track both the location and the orientation of a mobile target from MSR data.
Keywords
- Extend Kalman Filter
- Shape Descriptor
- Rigid Motion
- Observation Equation
- Invariant Shape
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2013 Springer International Publishing Switzerland
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Ammari, H. et al. (2013). Target Identification and Tracking. In: Mathematical and Statistical Methods for Multistatic Imaging. Lecture Notes in Mathematics, vol 2098. Springer, Cham. https://doi.org/10.1007/978-3-319-02585-8_11
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DOI: https://doi.org/10.1007/978-3-319-02585-8_11
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02584-1
Online ISBN: 978-3-319-02585-8
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