Bayesian Processors of Radar Signals for the Skyguard Radar System of the Ecuadorian Army

Cluttered Environment and Target Simulator to Evaluate Primary Surveillance Radar Processors

“…The generated test environments correspond to a Skyguard Oerlikon radar [20] (surveillance radar with a range of 16 km) and an AN/TPS 70 radar [23] (surveillance radar with a range of 440 km). These environments are used to evaluate the MTD and CFAR processors.…”

“…In order to verify the environment generation, two traditional radar processors are employed, the MTD processor, for monopulse targets without pulse compression [6,20,24] (as shown in Figure 3a), and the CFAR processor, for pulse-compressed targets [19,21], using Barker code [22,25] (as depicted in Figure 3b). These processors generate a detection vector with information on azimuth, distance, and whether the target is stationary or mobile.…”

“…The graph provides insights into the radar's ability to distinguish between targets and clutter. The original Oerlikon Skyguard system has an MTI processor, with a detection probability of 80%, with an SCR of 9 dB, and a false alarm probability of 10 −5 [20,24]. By using simulated environments to train processors with machine learning and deep learning, the theoretical capabilities are increased, with a detection probability of 90%, an SCR from −3 dB to 0 dB, and a false alarm probability of 10 −5 [10].…”

“…The proposed system simulates Weibull clutter, stationary targets, and moving targets with or without pulse compression (Swerling 0) [19]. The generated simulator is tested with two radar processors, a variant of moving target detection (MTD) [20] and a constant false alarm rate (CFAR) processor [21], in order to develop a tool that permits the assessment and testing of radar design before its implementation.…”