Classification of Polytime Codes of LPI Radar Signals Using XGBOOST Algorithm

Low probability of intercept (LPI) radars are one of the most significant radars in radar technology because of their functionality. It has certain features such as low power, wide bandwidth, etc. To accumulate this, the LPI radars use a particular form of changed wave bureaucracy, which might be tough to intercept. The precept obligations of intercept receivers are to discover, estimate, and classify LPI signs even in the presence of excessive noise. Additionally, the estimation of the modulation parameters additionally offers statistics approximately the danger to radar so that essential counter measures may be initiated towards the enemy radar. In this mission, we initially describe the simulation/contour plots of poly time code indicators (T1, T2, T3, T4) and then the main intention of the task, i.e., the class of poly time codes are effectively labelled. All the four types of Poly time codes are classified using python in anaconda navigator and jupyter notebook. The algorithm used here involves image pre-processing, segmentation, and feature extraction, whose output is fed to an XGBOOST classifier used to classify polytime code images into T1, T2, T3, and T4. In this proposed method, the outcome of testing is very high, which is 89% (accuracy) and the AVC score is 92%, which is an appreciable value.