Implementation of radar cross-sections model for targets with different scattering centers

Abstract

Target scattering or reflection is used by radar systems to identify and detect targets. The larger the echo that was returned to the radar receiver, the superior the signal-to-noise ratio and the greater the likelihood of detection. The radar cross-section (RCS) determines the quantity of energy reflected from a target in radar systems. This work shows new modeling for radar targets with growing stages of fidelity. We introduce the RCS concept for straightforward point targets and extend this into additional complex states of targets with several scattering midpoints. In addition, we discuss the modeling of fluctuations in RCS with time and briefly consider the case of the polarized signal. Because the receiver and transmitter are co-located, the effort focuses on narrowband mono-static radar techniques. The RCS value changes between scans. We simulate the replicated power of a sent signal over 10,000 scanning over unit incident signals, assuming that the signal illuminates the target just once each dwell. The target is modeled by four scatterers that are placed at four square vertices. All scatterers are cylindrical-point targets on a 0.5-meter square XY plane without losing generality. The acquired results demonstrated how to generate target echoes while accounting for statistical fluctuations. From the relation between RCS and elevation angle variations for cylindrical targets, the obtained result demonstrated that the first two outputs are the same and confirmed that there is no reliance on azimuth angle. The comparison between wideband and narrowband RCS patterns demonstrated that the RCS profiles of the target-matched shallower nulls for azimuth direction are in the range of (40–50) degrees at zero elevation for 4 scatterers’ extended targets.

Enhancing Hybrid System Based on Reinforcement Learning

“…However, the focus of this study is on RLbased load balancing for hybrid LiFi Wi-Fi networks, hence the simplest TRPO form was investigated. The algorithm's performance and rate of convergence will be improved in future work by incorporating knowledge transfer [25].…”

“…Following a similar procedure as described [25], the process of AP assignment and resource allocation has been carried out by calculating the Lagrangian multipliers λµ and ωa and using them to determine the optimal value ofg µ,a .…”