This article considers the problem of multitarget detection for massive multiple input multiple output cognitive radar (CR). The concept of CR is based on the perception-action cycle that senses and intelligently adapts to the dynamic environment in order to optimally satisfy a specific mission. However, this usually requires a priori knowledge of the environmental model, which is not available in most cases. We propose a reinforcement learning (RL) based algorithm for cognitive multitarget detection in the presence of unknown disturbance statistics. The radar acts as an agent that continuously senses the unknown environment (i.e., targets and disturbance) and consequently optimizes transmitted waveforms in order to maximize the probability of detection ($P_{\mathsf {D}}$) by focusing the energy in specific range-angle cells (i.e., beamforming). Furthermore, we propose a solution to the beamformin optimization problem with less complexity than the existing methods. Numerical simulations are performed to assess the performance of the proposed RL-based algorithm in both stationary and dynamic environments. The RL-based beamforming is compared to the conventional omnidirectional approach with equal power allocation and to adaptive beamforming with no RL. As highlighted by the proposed numerical results, our RL-based beamformer outperforms both approaches in terms of target detection performance. The performance improvement is even particularly remarkable under environmentally harsh conditions such as low SNR, heavy-tailed disturbance, and rapidly changing scenarios.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Reinforcement Learning Based Approach for Multitarget Detection in Massive MIMO Radar




    Publication date :

    2021-10-01


    Size :

    2282079 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Deep Learning-based Estimation for Multitarget Radar Detection

    Delamou, Mamady / Bazzi, Ahmad / Chafii, Marwa et al. | IEEE | 2023





    Reinforcement Learning Based Antenna Selection in User-Centric Massive MIMO

    Chai, Xinxin / Gao, Hui / Sun, Ji et al. | IEEE | 2020