A theory of causal inference based on additive noise model and its application to radiological security projects

Security is particularly important in the process of radioactive material transportation. In order to solve the impact of the uncertain dynamic characteristics of the actual system and the complex uncertain noise containing in the target tracking and positioning of BDS(BeiDou Navigation Satellite System), an algorithm of causal inference is constructed to explain and deal with the state estimation method of the complex uncertain noise system. The measurement of noise variance is performed by using the covariance matching principle and real-time updated noise data with additive measurement noise of the system. an adaptive process detection model is proposed for the modeling problem of the Kalman filter. In comparing and testing the models, The adaptive process measurement model can give ideal estimation results regardless of the initial value of the measurement noise variance in the results. The paper demonstrates that the adaptive process measurement model has good robustness and is highly effective in dealing with state estimation problems for systems with noise which is complex and uncertain.