This approach combines the global search efficiency of PSO with the local refinement capabilities of GWO, achieving superior sidelobe suppression while preserving resolution and detection accuracy. The suggested method has been evaluated in regards of suppression effectiveness and computational efficiency by simulating the various optimisation techniques in MATLAB, demonstrating its superiority over traditional windowing techniques and single optimisation algorithm as Genetic algorithm. This novel approach enhances radar performance by reducing interference, improving target discrimination, and increasing resilience against jamming, making it a robust solution for modern radar applications.
"> Radar systems rely on effective sidelobe suppression to concentrate energy on the primary lobe, ensuring accurate target detection and minimizing false identifications. In both military and civilian applications, sidelobe reduction plays a crucial role in enhancing precision by mitigating clutter and environmental interference. Uncontrolled sidelobes allow unwanted reflections from structures, water surfaces, and terrain, introducing noise that degrades radar sensitivity. Reducing sidelobe levels improves the radar system's capacity to detect weak or far-off objects by helping it differentiate between real targets and background noise. For applications where accurate target detection is required, such air traffic control, weather monitoring, and surveillance, this is especially important. This thesis focuses on improving radar signal processing by supressing side lobes in polyphase codes, which are commonly used in radar systems for pulse compression and waveform design. To address this, in this thesis a novel hybrid optimisation approach has been proposed combining Particle Swarm Optimization (PSO) and the Gray Wolf Optimizer (GWO) to design polyphase codes with significantly reduced sidelobes.This approach combines the global search efficiency of PSO with the local refinement capabilities of GWO, achieving superior sidelobe suppression while preserving resolution and detection accuracy. The suggested method has been evaluated in regards of suppression effectiveness and computational efficiency by simulating the various optimisation techniques in MATLAB, demonstrating its superiority over traditional windowing techniques and single optimisation algorithm as Genetic algorithm. This novel approach enhances radar performance by reducing interference, improving target discrimination, and increasing resilience against jamming, making it a robust solution for modern radar applications.
">Radar systems rely on effective sidelobe suppression to concentrate energy on the primary lobe, ensuring accurate target detection and minimizing false identifications. In both military and civilian applications, sidelobe reduction plays a crucial role in enhancing precision by mitigating clutter and environmental interference. Uncontrolled sidelobes allow unwanted reflections from structures, water surfaces, and terrain, introducing noise that degrades radar sensitivity. Reducing sidelobe levels improves the radar system's capacity to detect weak or far-off objects by helping it differentiate between real targets and background noise. For applications where accurate target detection is required, such air traffic control, weather monitoring, and surveillance, this is especially important. This thesis focuses on improving radar signal processing by supressing side lobes in polyphase codes, which are commonly used in radar systems for pulse compression and waveform design. To address this, in this thesis a novel hybrid optimisation approach has been proposed combining Particle Swarm Optimization (PSO) and the Gray Wolf Optimizer (GWO) to design polyphase codes with significantly reduced sidelobes.
This approach combines the global search efficiency of PSO with the local refinement capabilities of GWO, achieving superior sidelobe suppression while preserving resolution and detection accuracy. The suggested method has been evaluated in regards of suppression effectiveness and computational efficiency by simulating the various optimisation techniques in MATLAB, demonstrating its superiority over traditional windowing techniques and single optimisation algorithm as Genetic algorithm. This novel approach enhances radar performance by reducing interference, improving target discrimination, and increasing resilience against jamming, making it a robust solution for modern radar applications.
