Estimation of potential field environments from heterogeneous behaviour of sensing agents
Abstract This paper proposes a novel modelling framework for estimating the global potential field from trajectories of multiple sensing agents whose perception of the unknown field is subject to abrupt changes. We derive a parametrised formulation of the estimation problem by combining the jump Mar...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | IET Signal Processing |
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| Online Access: | https://doi.org/10.1049/sil2.12181 |
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| author | Anastasia Kadochnikova Visakan Kadirkamanathan |
| author_facet | Anastasia Kadochnikova Visakan Kadirkamanathan |
| author_sort | Anastasia Kadochnikova |
| collection | DOAJ |
| description | Abstract This paper proposes a novel modelling framework for estimating the global potential field from trajectories of multiple sensing agents whose perception of the unknown field is subject to abrupt changes. We derive a parametrised formulation of the estimation problem by combining the jump Markov non‐linear system (JMNLS) model of agent dynamics with a basis function decomposition of the environmental field. An approximate expectation‐maximisation algorithm is employed for joint estimation of the global field and of the agent behavioural modes from observed agent trajectories. To avoid prohibitive computational costs associated with the state estimation of JMNLS, we utilise two approximation steps. First, an interacting multiple model smoother is used to account for the hybrid structure that emerges in this problem. Second, we propose two approaches to approximating the non‐linear sufficient statistics during the expectation step. This results in the maximization step being exact. The performance of the developed framework is tested on simulation examples and demonstrated on an application study in which the observed movement patterns of immune cells are utilised in quantifying the underlying chemical concentration field that governs their migration. The results showcase that the proposed framework can be readily applied to problems where agents assume several behavioural modes. |
| format | Article |
| id | doaj-art-b2fb4c5813924f348e67fd41fb4b3ddc |
| institution | Kabale University |
| issn | 1751-9675 1751-9683 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj-art-b2fb4c5813924f348e67fd41fb4b3ddc2025-02-03T01:29:43ZengWileyIET Signal Processing1751-96751751-96832023-01-01171n/an/a10.1049/sil2.12181Estimation of potential field environments from heterogeneous behaviour of sensing agentsAnastasia Kadochnikova0Visakan Kadirkamanathan1Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UKDepartment of Automatic Control and Systems Engineering University of Sheffield Sheffield UKAbstract This paper proposes a novel modelling framework for estimating the global potential field from trajectories of multiple sensing agents whose perception of the unknown field is subject to abrupt changes. We derive a parametrised formulation of the estimation problem by combining the jump Markov non‐linear system (JMNLS) model of agent dynamics with a basis function decomposition of the environmental field. An approximate expectation‐maximisation algorithm is employed for joint estimation of the global field and of the agent behavioural modes from observed agent trajectories. To avoid prohibitive computational costs associated with the state estimation of JMNLS, we utilise two approximation steps. First, an interacting multiple model smoother is used to account for the hybrid structure that emerges in this problem. Second, we propose two approaches to approximating the non‐linear sufficient statistics during the expectation step. This results in the maximization step being exact. The performance of the developed framework is tested on simulation examples and demonstrated on an application study in which the observed movement patterns of immune cells are utilised in quantifying the underlying chemical concentration field that governs their migration. The results showcase that the proposed framework can be readily applied to problems where agents assume several behavioural modes.https://doi.org/10.1049/sil2.12181hidden Markov modelsmaximum likelihood estimationnonlinear dynamical systemsparameter estimationstate estimation |
| spellingShingle | Anastasia Kadochnikova Visakan Kadirkamanathan Estimation of potential field environments from heterogeneous behaviour of sensing agents IET Signal Processing hidden Markov models maximum likelihood estimation nonlinear dynamical systems parameter estimation state estimation |
| title | Estimation of potential field environments from heterogeneous behaviour of sensing agents |
| title_full | Estimation of potential field environments from heterogeneous behaviour of sensing agents |
| title_fullStr | Estimation of potential field environments from heterogeneous behaviour of sensing agents |
| title_full_unstemmed | Estimation of potential field environments from heterogeneous behaviour of sensing agents |
| title_short | Estimation of potential field environments from heterogeneous behaviour of sensing agents |
| title_sort | estimation of potential field environments from heterogeneous behaviour of sensing agents |
| topic | hidden Markov models maximum likelihood estimation nonlinear dynamical systems parameter estimation state estimation |
| url | https://doi.org/10.1049/sil2.12181 |
| work_keys_str_mv | AT anastasiakadochnikova estimationofpotentialfieldenvironmentsfromheterogeneousbehaviourofsensingagents AT visakankadirkamanathan estimationofpotentialfieldenvironmentsfromheterogeneousbehaviourofsensingagents |