computational complexity

Gamma-ray detection has an important role in the enhancement the nuclear safety and provides a proper environment for applications of nuclear radiation. To reduce the risk of exposure, aerial gamma survey is commonly used as an advantage of the distance between the detection system and the radiation sources. One of the most important issues in aerial gamma survey is the detection noise. Various methods being proposed to reduce the noise of the gamma detectors, among which, in this paper, the utilization of Cyclo-stationary properties is proposed, because of its capability in detecting weakened gamma rays with low rate counts from far sources. To increase the accuracy of the results of gamma detection and reduce errors due to physical and flight constraints, we compared other time-series processing and spectral estimation methods. The most important problem with such methods is the high computational complexity, which makes them difficult to use. In this paper, we present the aerial gamma detection noise reduction methods based on the Cyclo-stationary properties in extended Kalman filters. The Kalman estimates real-time variations in the counts of radionuclides using data integration based on a dynamic model. The results show that the extended Kalman is superior to other filters due to its nonlinear distortion reduction feature. The focus of the paper is on the modeling, matching and computational aspects of applying the Kalman filter on real data obtained from aerial gamma survey. The covariance and the required computational time have been evaluated using the power spectral density estimation, Multi-taper spectral estimation, and the extended Kalman methods. The results indicate that the extended Kalman method increases the converging speed in addition to empowering the detector against the nonlinear noise and disturbances.