The anti-noise jamming evaluation toward surveillance radar often needs to estimate quantized Rayleigh noise samples. In addition, due to the conversion digit number restriction of hardwares, real radar systems sometimes discard the low bit data, which increases the quantization error. In that case, calculating the average of these truncated data would cause large biases. In order to efficiently estimate the truncated noise samples, we derive the maximum likelihood estimation for their distribution parameters is derived, and the convex function property for the logarithm likelihood function as well as the logarithm concave distribution property for the posterior distribution are proved. Based on that, we propose Bayesian estimation methods under noninformative/conjugate priors. Simulation experiments demonstrate the effectiveness of the proposed maximum likelihood estimation, and noninformative/conjugate priorsbased Bayesian estimation. By comparison with the maximum likelihood estimation, the advantage of the Bayesian estimation is analyzed.