Noise power estimation is central to efficient radio resource allocation in modern wireless communication systems. In the literature, there exist many noise power estimation methods that can be classified based on underlying theoretical principle; the most common are spectral averaging, eigenvalues of sample covariance matrix, information theory, and statistical signal analysis. However, how these estimation methods compare against each other in terms of accuracy, stability, and complexity is not well studied, and the focus instead remains on the enhancement of individual methods. In this paper, we adopt a common simulation methodology to perform a detailed performance evaluation of the prominent estimation techniques. The basis of our comparison is the signal-to-noise ratio estimation in the simulated industrial, scientific and medical band transmission, while the reference noise signal is acquired from an industrial production plant using a software-defined radio platform, USRP-2932. In addition, we analyze the impact of different techniques for noise-samples' separation on the estimation process. As a response to defects in the existing techniques, we propose a novel noise-samples' separation algorithm based on the adaptation of rank-order filtering. Our analysis shows that the proposed solution, apart from its low complexity, has a very good root-mean-squared error of 0.5 dB and smaller than 0.1-dB resolution, thus achieving a performance comparable with the methods exploiting information theory concepts.