Multidimensional projection filters via automatic differentiation and sparse-grid integration
•We use automatic differentiation and sparse-grid integration to automate the construction of the projection filter.•We present methods for constructing projection filters for multidimensional filtering problems using a non-Gaussian parametric density.•We show that the practical performance of the f...
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| Veröffentlicht in: | Signal processing Jg. 204; S. 108832 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
01.03.2023
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| Schlagworte: | |
| ISSN: | 0165-1684, 1872-7557, 1872-7557 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | •We use automatic differentiation and sparse-grid integration to automate the construction of the projection filter.•We present methods for constructing projection filters for multidimensional filtering problems using a non-Gaussian parametric density.•We show that the practical performance of the filter is comparable to the particle filter and finite difference based solutions to the Kushner–Stratonovich equation.•An open-source implementation of the method is available.
The projection filter is a technique for approximating the solutions of optimal filtering problems. In projection filters, the Kushner–Stratonovich stochastic partial differential equation that governs the propagation of the optimal filtering density is projected to a manifold of parametric densities, resulting in a finite-dimensional stochastic differential equation. Despite the fact that projection filters are capable of representing complicated probability densities, their current implementations are limited to Gaussian family or unidimensional filtering applications. This work considers a combination of numerical integration and automatic differentiation to construct projection filter algorithms for more generic problems. Specifically, we provide a detailed exposition of this combination for the manifold of the exponential family, and show how to apply the projection filter to multidimensional cases. We demonstrate numerically that based on comparison to a finite-difference solution to the Kushner–Stratonovich equation and a bootstrap particle filter with systematic resampling, the proposed algorithm retains an accurate approximation of the filtering density while requiring a comparatively low number of quadrature points. Due to the sparse-grid integration and automatic differentiation used to calculate the expected values of the natural statistics and the Fisher metric, the proposed filtering algorithms are highly scalable. They therefore are suitable to many applications in which the number of dimensions exceeds the practical limit of particle filters, but where the Gaussian-approximations are deemed unsatisfactory. |
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| ISSN: | 0165-1684 1872-7557 1872-7557 |
| DOI: | 10.1016/j.sigpro.2022.108832 |