Bayesian Inference of Hit Probability of Ammunition Based on Normal-Inverse Wishart Distribution

Abstract

In order to solve the problems of high experimental cost of ammunition, lack of field test data, and the difficulty in applying the ammunition hit probability estimation method in classical statistics, this paper assumes that the projectile dispersion of ammunition is a two-dimensional joint normal distribution, and proposes a new Bayesian inference method of ammunition hit probability based on normal-inverse Wishart distribution. Firstly, the conjugate joint prior distribution of the projectile dispersion characteristic parameters is determined to be a normal inverse Wishart distribution, and the hyperparameters in the prior distribution are estimated by simulation experimental data and historical measured data. Secondly, the field test data is integrated with the Bayesian formula to obtain the joint posterior distribution of the projectile dispersion characteristic parameters, and then the hit probability of the ammunition is estimated. Finally, compared with the binomial distribution method, the method in this paper can consider the dispersion information of ammunition projectiles, and the hit probability information is more fully utilized. The hit probability results are closer to the field shooting test samples. This method has strong applicability and is conducive to obtaining more accurate hit probability estimation results.

Keywords

ammunition / Bayesian inference / hit probability / normal-inverse Wishart distribution / projectile dispersion