arXiv subjects

Robin Forsling

Publications and source records attributed to Robin Forsling.

4 recordsLinked to original sources

arXiv2024

Matrix-Valued Measures and Wishart Statistics for Target Tracking Applications

Ensuring sufficiently accurate models is crucial in target tracking systems. If the assumed models deviate too much from the truth, the tracking performance might be severely degraded. While the models are usually defined using multivariate conditions, the measures used to validate them are most often scalar-valued. In this paper, we propose matrix-valued measures for both offline and online assessment of target tracking systems. Recent results from Wishart statistics, and approximations thereof, are adapted and it is shown how these can be incorporated to infer statistical properties for the eigenvalues of the proposed measures. In addition, we relate these results to the statistics of the baseline measures. Finally, the applicability of the proposed measures are demonstrated using two important problems in target tracking: (i) distributed track fusion design; and (ii) filter model mismatch detection.

eess.SY↗

arXiv2023

Track-To-Track Association for Fusion of Dimension-Reduced Estimates

Network-centric multitarget tracking under communication constraints is considered, where dimension-reduced track estimates are exchanged. Previous work on target tracking in this subfield has focused on fusion aspects only and derived optimal ways of reducing dimensionality based on fusion performance. In this work we propose a novel problem formalization where estimates are reduced based on association performance. The problem is analyzed theoretically and problem properties are derived. The theoretical analysis leads to an optimization strategy that can be used to partly preserve association quality when reducing the dimensionality of communicated estimates. The applicability of the suggested optimization strategy is demonstrated numerically in a multitarget scenario.

eess.SP↗

arXiv2022

Decentralized State Estimation In A Dimension-Reduced Linear Regression

Decentralized state estimation in a communication-constrained sensor network is considered. The exchanged estimates are dimension-reduced to reduce the communication load using a linear mapping to a lower-dimensional space. The mean squared error optimal linear mapping depends on the particular estimation method used. Several dimension-reducing algorithms are proposed, where each algorithm corresponds to a commonly applied decentralized estimation method. All except one of the algorithms are shown to be optimal. For the remaining algorithm, we provide a convergence analysis where it is theoretically shown that this algorithm converges to a stationary point and numerically shown that the convergence rate is fast. A message-encoding solution is proposed that allows for efficient communication when using the proposed dimension reduction techniques. We also derive different properties from the proposed framework and show its superiority in relation to baseline methods. The applicability of the different algorithms is demonstrated using a simple fusion example and a more realistic target tracking scenario.

eess.SY↗

arXiv2014

First-passage dynamics of obstructed tracer particle diffusion in one-dimensional systems

The standard setup for single-file diffusion is diffusing particles in one dimension which cannot overtake each other, where the dynamics of a tracer (tagged) particle is of main interest. In this article we generalise this system and investigate first-passage properties of a tracer particle when flanked by crowder particles which may, besides diffuse, unbind (rebind) from (to) the one-dimensional lattice with rates $k_{\rm off}$ ($k_{\rm on}$). The tracer particle is restricted to diffuse with rate $k_D$ on the lattice. Such a model is relevant for the understanding of gene regulation where regulatory proteins are searching for specific binding sites ona crowded DNA. We quantify the first-passage time distribution, $f(t)$ ($t$ is time), numerically using the Gillespie algorithm, and estimate it analytically. In terms of our key parameter, the unbinding rate $k_{\rm off}$, we study the bridging of two known regimes: (i) when unbinding is frequent the particles may effectively pass each other and we recover the standard single particle result $f(t)\sim t^{-3/2}$ with a renormalized diffusion constant, (ii) when unbinding is rare we recover well-known single-file diffusion result $f(t)\sim t^{-7/4}$. The intermediate cases display rich dynamics, with the characteristic $f(t)$-peak and the long-time power-law slope both being sensitive to $k_{\rm off}$.

cond-mat.stat-mech↗