Advanced Target Detection Strategy Using a Hybrid Multiplexing Photonic Radar System for Resilient and Intelligent Autonomous Vehicle Operation in 5G Communication Environments

Photonic radar systems improve environment sensing and multi-target detections in self-driven vehicles but are hampered in their adoption due to high system cost, complexity of integration, and environment dependencies. To overcome these limitations, an Advanced Hybrid Mode-Division Multiplexing-Polarization-Division Multiplexing Photonic Radar is proposed in this research for target detection in 5G-enabled self-driven vehicles. The primary objective is to develop a hybrid multiplexing photonic radar system that integrates mode-division multiplexing (MDM) and polarization-division multiplexing (PDM) to enable precise multi-target detection under adverse operating conditions, thereby improving the reliability, safety, and sustainability of autonomous vehicle navigation systems. The MDM-PDM configuration supports four-channel transmissions in a compact form factor using X- and Y-polarized streams with phase-shifted donut modes. Linear Frequency Modulation (LFM) chirp signals improve Doppler tolerance, while beat signal extraction and signal-to-noise ratio (SNR) estimation allow precise range and velocity measurements. Radar data are processed using a Logarithmic Differential Convolutional Neural Network (LDCNN) implemented in MATLAB, enhancing target discrimination and detection efficiency. The proposed system demonstrates achieves an accuracy of 96.4% and probability of detection (Pd) of 0.984, while maintaining a low false alarm rate (Pfa) of 0.016. Additionally, a low SNR error of 0.015% is observed, serving as an auxiliary indicator of signal fidelity under simulated conditions. Simulation results demonstrate superior multi-target localization, resilience to noise, and reliable operation, supporting safe and intelligent autonomous navigation. This work introduces a novel hybrid MDM-PDM photonic radar architecture integrated with deep learning-based signal processing, providing a compact, high-precision solution for real-time target detection in complex driving environments.