Program for Calculating Detection Probability Using 5 Swerling Target Fluctuation Models

The following program implementations calculate detection probability using five Swerling target fluctuation models: 1. Swerling Model 0: In this model, target intensity remains constant. The program employs Rician distribution to simulate signals received by the detector, then computes detection probability. Implementation typically involves calculating the Rician K-factor and using Marcum Q-function for probability derivation. 2. Swerling Model 1: This model features randomly fluctuating target intensity. The program utilizes Rayleigh distribution to model received signal strength before calculating detection probability. Code implementation often includes generating Rayleigh-distributed random variables and applying threshold detection algorithms. 3. Swerling Model 2: Here, target intensity varies over time. The program implements time-varying Rician distribution to simulate received signals prior to detection probability computation. The algorithm typically handles time-dependent parameters and sliding window analysis for dynamic signal processing. 4. Swerling Model 3: This model incorporates random fluctuations in both target intensity and direction. The program uses complex Gaussian distribution during rotational processes to simulate received signals, then calculates detection probability. Implementation commonly involves covariance matrix operations and eigenvalue decomposition for multidimensional signal analysis. 5. Swerling Model 4: In this model, both target intensity and direction vary temporally. The program employs time-varying Gaussian distribution to simulate received signals before computing detection probability. The code typically features adaptive filtering techniques and Kalman filter implementations for tracking time-evolving statistical parameters.