Active imaging processing technique for sensor data reconstruction and identification

Active imaging (AI) is necessary for measuring parameters of the objects that do not give out or reflect a specific type of radiation. AI systems offer a number of advantages over passive imaging systems that operate at visible through nearinfrared wavelengths and usually rely on solar illumination. The reliability and precision of the target identification depends on how the signal received from a sensor is processed. Often, obstacles or the imperfection of the sensors and processing electronics cause loss of some of the information. The technique of processes with missing data is suggested as part of time series prediction and analysis. Thus, the image may be reconstructed even if the necessary data is partially absent in the input signal. The suggested method reduces the false alarm rate of the target identification. Results are provided.