Autonomous heterogeneous sensor networks (HSNs) consist of platforms equipped with various sensors. Platforms and sensors have limited range, so they must make decisions on where and when to point these sensors, what information to share among the cooperating platforms, and how to exploit multi-modal dependencies for accurate detection, tracking, and classification (DTC) of time-critical objects. We posit that cooperating platforms within HSN s that share and fuse multimodal data among themselves (using constrained inter-platform communication) will be more effective in accomplishing their required tasks and have more accurate target detection and recognition. We study a scenario where a group of cooperating platforms, each equipped with multi-modal sensors are collecting measurements of an object and the measurements are transmitted over a bandwidth-limited communication channel to a centralized node for processing. The communication channel presents an information transfer bottleneck as the sensors collect measurements at a much higher rate than what is feasible to transmit over the communication channel. In order to minimize the estimation error at the centralized node, only a carefully selected subset of measurements should be transmitted. We propose a unique synthesis of novel submodular optimization based reinforcement learning (RL) for active sensor control/management. Our approach solves the measurement selection problem by employing a model-free RL with sub modular objective functions, where instead of maximizing the cumulative reward, the goal is to maximize the objective value induced by a sub modular function. We make evident the effectiveness of this approach with simulated and real data (the latter including ESCAPE Government-furnished information which we have been extensively using on our previous efforts).


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Submodular Optimization via Reinforcement Learning for Active Control of Sensor Networks


    Beteiligte:
    Garagic, Denis (Autor:in) / Ravier, Robert (Autor:in) / Peskoe, Jacob (Autor:in) / Galoppo, Travis (Autor:in) / Zulch, Peter (Autor:in)


    Erscheinungsdatum :

    05.03.2022


    Format / Umfang :

    3156348 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Exploiting submodular value functions for scaling up active perception

    Satsangi, Y. | British Library Online Contents | 2018


    Heterogeneous Measurement Selection for Vehicle Tracking using Submodular Optimization

    Kirchner, Matthew R. / Hespanha, Joao P. / Garagic, Denis | IEEE | 2020


    Multiagent Reinforcement Learning and Game-Theoretic Optimization for Autonomous Sensor Control

    Ravier, Robert / Garagic, Denis / Galoppo, Travis et al. | IEEE | 2024


    SYSTEM AND METHOD FOR GROUP ELEVATOR SCHEDULING BASED ON SUBMODULAR OPTIMIZATION

    NIKOVSKI DANIEL / RAGHUNATHAN ARVIND / RAMALINGAM SRIKUMAR | Europäisches Patentamt | 2018

    Freier Zugriff

    System and Method for Group Elevator Scheduling Based on Submodular Optimization

    NIKOVSKI DANIEL NIKOLAEV / RAGHUNATHAN ARVIND U / RAMALINGAM SRIKUMAR | Europäisches Patentamt | 2018

    Freier Zugriff