Abstract
In this paper, a policy iterative algorithm based on reinforcement learning is proposed to improve the balanced detection quality of passive remote sensing equipment. Firstly, the basic structure of the policy iteration algorithm is given for nonlinear affine systems, and stability proof is given. The whole process of detection information of passive remote sensing equipment is equivalent to the absorption of electric energy by the explored object. A nonlinear affine model for the PI solution is established by using this equivalent model. The working quality based on various information sensors is equivalent to the discrete-time cost function. Finally, a simulation experiment is carried out for a class of passive remote sensing equipment with 8 kinds of characteristic information and a virtual self-powered device is considered. The effectiveness of the algorithm is proven.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
