Abstract
The treatment so far has assumed that the appearance function \(g^m(\mathbf {y}|\mathbf {x}_t^m)\) is known. In general, the target appearance is affected by two factors. The first is the physical shape of the object itself and the second is the way that the sensor hardware and signal processing spreads energy across pixels. The second factor is often referred to as the point spread function of the sensor and depends on physical features of the sensor, such as the size of the aperture and the properties of the optics, and also software features, especially the tapers used in Fourier transforms to form range cells and beams.
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Notes
- 1.
MakeHPMHTParams
The H-PMHT toolbox contains two main functions for running H-PMHT. MakeHPMHTParams defines tracking parameters, including which version of H-PMHT to execute, and HPMHTTracker actually does the tracking. The toolbox contains two versions of H-PMHT-RM. The original H-PMHT-RM was built on core H-PMHT and uses the multinomial model. This can be setup using MakeHPMHTParams(‘core H-PMHT-RM’). A version incorporating the Poisson assignment prior is initialised with MakeHPMHTParams(‘Poisson H-PMHT-RM’). For details on how to use this function, refer to the H-PMHT toolbox documentation.
- 2.
MakeHPMHTParams
The H-PMHT toolbox contains two main functions for running H-PMHT. MakeHPMHTParams defines tracking parameters, including which version of H-PMHT to execute, and HPMHTTracker actually does the tracking. The default tracking parameters for the Dirichlet H-PMHT are created by MakeHPMHTParams(‘Dirichlet’). For details on how to use this function, refer to the H-PMHT toolbox documentation.
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Davey, S.J., Gaetjens, H.X. (2018). Adaptive Appearance Models. In: Track-Before-Detect Using Expectation Maximisation. Signals and Communication Technology. Springer, Singapore. https://doi.org/10.1007/978-981-10-7593-3_8
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DOI: https://doi.org/10.1007/978-981-10-7593-3_8
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