Radar Signal Detection Using Swerling Target Models with Performance Simulation

To simulate detection performance under different signal-to-noise ratio (SNR) conditions, we generate various target model datasets and process them according to false alarm probability requirements. The radar system configuration implements square law detection followed by non-coherent integration of 10 pulses. The simulation framework generates Swerling type 0-IV target signals with additive white Gaussian noise modeling. For statistical reliability, Monte Carlo simulations with minimum 10^5 iterations are conducted across SNR values ranging from -10dB to 10dB with 1dB increments. The false alarm probability is constrained to 10^-6 throughout the simulation. Key implementation aspects include: 1. Signal generation using Swerling model parameters for amplitude fluctuation characteristics 2. Threshold calculation based on Neyman-Pearson criterion for constant false alarm rate 3. Statistical counting methods for detection probability estimation The simulation results produce detection performance curves where the x-axis represents SNR (1dB step size) and the y-axis shows the corresponding detection probability (Pd).