A key component of cognitive radar is the ability to generalize or achieve consistent performance across a range of sensing environments, since aspects of the physical scene may vary over time. This presents a challenge for learning-based waveform selection approaches, since transmission policies that are effective in one scene may be highly suboptimal in another. We address this problem by strategically biasing a learning algorithm by exploiting high-level structure across tracking instances, referred to as Meta-Learning. In this work, we develop an online Meta-Learning approach for waveform-agile tracking. This approach uses information gained from previous target tracks to speed up and enhance learning in new tracking instances. This results in sample-efficient learning across a class of finite-state target channels by exploiting inherent similarity across tracking scenes, attributed to common physical elements such as target type or clutter statistics. We formulate the online waveform selection problem within the framework of Bayesian learning and provide prior-dependent performance bounds for the Meta-Learning problem using probability approximately correct Bayes theory. We present a computationally feasible metaposterior sampling algorithm and study the performance in a simulation study consisting of diverse scenes. Finally, we examine the potential performance benefits and practical challenges associated with online Meta-Learning for waveform-agile tracking.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Online Bayesian Meta-Learning for Cognitive Tracking Radar


    Contributors:


    Publication date :

    2023-10-01


    Size :

    1737457 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Distributed Online Learning for Coexistence in Cognitive Radar Networks

    Howard, William W. / Martone, Anthony F. / Buehrer, R. Michael | IEEE | 2023



    Cognitive chaotic UWB-MIMO radar based on nonparametric Bayesian technique

    Nijsure, Yogesh / Kaddoum, Georges / Leung, Henry | IEEE | 2015