It is very difficult to get exact accurate estimated value When the Markov process theory is used to calculate the number of remaining mines, because the prior knowledge has not been fully utilized. For solving the above problems, this paper establishes posterior distribution model of Estimation of the number of remaining mines, based on Bayesian estimation theory. Then several typical distributions are discussed, such as the binomial distribution, Poisson distribution, negative binomial distribution and uniform distribution, and the mean and variance of the number of remaining mines are given. Finally the simulation calculation is carried out for the given minefield prior data, the results show that the model accuracy is related to mine number, mine sweeping probability and other factors.
Abstract. In minesweeping operations, it is difficult to get optimum sweeping width due to complicated magnetic field distribution. Therefore the main work in this dissertation is to find a method to analyze the characteristics of magnetic field and key factors which can affect minesweeping width. First, the distribution model of magnetic field is given according to different magnet configurations. Second, the isomagnetic lines are drawn and disscussed for different components. Finally, the optimum sweeping width is obtained by the characteristics of isomagnetic lines. The study result shows that different magnet configurations will have a great impact on the effect of mine sweeping. In addition, the choice of different components will also determine the effect of mine sweeping.
The estimation of number of remaining mines is an important basis for the performance evaluation of naval minesweeping, and the accuracy of the estimation is related to the mastery of prior information and the selection of evaluation model. In this paper, we build the evaluation models by employing Markov process and Bayesian estimation theory to estimate the number of remaining mines.Then simulation experiments based on the above models were carried out, using the operational data which come from prior information of the minefield and information of swept mines. The results show that the Bayesian estimation is more reliable than Markov process in the case of sufficient prior information.
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