Enhanced Box-Muller method for high quality Gaussian random number generation

Addaim, Adnane; Gretete, Driss; Madi, Abdessalam Aït

doi:10.1504/ijcsm.2018.093153

Cited by 11 publications

(4 citation statements)

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“…If equation ( 4) is compared with equation ( 2), it can be seen that Adnan et al in [8] developed the Box-Muller formula in equation ( 2) by adding the n term to the Trigonometric term and using only one Uniform distributed random number (U).…”

Section: Enhanced Box Muller Methods (E Box-muller)mentioning

confidence: 99%

“…The Box-Muller method is a random number generation method that has a simple, portable and easy algorithm to implement at the hardware level (FPGA and Microcontroller). The Box-Muller method is a Gaussian distributed random number generation method that converts 2 (two) Uniform distributed random numbers into 2 Gaussian distributed random numbers that are perpendicular to each other (Quadrature) [6][7] [8].…”

Section: Noise Generating Systemmentioning

confidence: 99%

“…The modulator will place the resulting noise signal in the desired band. The Box-Muller method was later improved by Adnane et al in [8] in the "tail" section of his Gaussian response.…”

Section: Introductionmentioning

confidence: 99%

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Gaussian Distributed Noise Generator Design Using MCU-STM32

Zakaria

Setiawan

et al. 2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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The random noise signal is widely used as a test signal to identify a physical or biological system. In particular, the Gaussian distributed white noise signal (Gaussian White Noise) is popularly used to simulate environmental noise in telecommunications system testing, input noise in testing ADC (Analog to Digital Converter) devices as well as testing other digital systems. Random noise signal generation can be done using resistors or diodes. The weakness of the noise generator system using physical components is the statistical distribution. An alternative solution is to use a Pseudo-Random System that can be adjusted for distribution and other statistical parameters. In this study, the implementation of the Gaussian distributed pseudo noise generation algorithm based on the Enhanced Box-Muller method is described. Prototype of noise generation system using a minimum system board based on Cortex Microcontroller or MCU-STM32F4. From the test results, it was found that the Enhanced Box-Muller (E Box-Muller) method can be applied to the MCU-STM32F4 efficiently, producing signal noise with Gaussian distribution. The resulting noise signal has an amplitude of ±1Volt, is Gaussian distributed and has a relatively wide frequency spectrum. The noise signal can be used as a jamming device in a certain frequency band using an Analog modulator.

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Section: Enhanced Box Muller Methods (E Box-muller)mentioning

confidence: 99%

Section: Noise Generating Systemmentioning

confidence: 99%

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Gaussian Distributed Noise Generator Design Using MCU-STM32

Zakaria

Setiawan

et al. 2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…To generate these coefficients, the Box-Muller method was used. Box-Muller is an algorithm for generating normally distributed random numbers using uniformly distributed random numbers [23].…”

Section: 5 Normal Distribution Generationmentioning

confidence: 99%

Development of a hardware emulator of a nanosatellite gyroscope

2023

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The gyroscope sensor has multiple applications in consumer electronics, aircraft navigation, and control systems. Significant errors that match the corresponding data are a typical disadvantage of this sensor. This needs to be done by making error models that can be used to get the right level of measurement accuracy. For high-precision space applications, the navigation design system should take into account the angle random walk (N), bias instability error (B), and rate random walk (K) of the BMG160 gyroscope. For this reason, this paper shows how to use Allan Variance (AVAR) and Power Spectral Density (PSD) for the experimental identification and modeling of the stochastic parameters of the Bosch BMG160 gyroscope embedded in a nanosatellite in order to get an accurate gyroscope model. This work also demonstrates the principle of operation of the equivalent electronic model intended to carry out advanced simulations without recourse to the real material in order to avoid the problem of bad manipulation and availability of the material in order to reduce the time and cost of development. The interpretation of the Allan curves and the PSD obtained from the measurements collected over a long period is presented, as well as a comparison between the real raw data of the BMG160 gyroscope and the designed hardware emulator in both the time and frequency domains. This is done to evaluate the accuracy of the gyroscope model emulating the real sensor in laboratory simulations. The experimental results show that the signals from the emulator and the BMG160 gyroscope are quite close. Therefore, the proposed prototype could be an optimal solution for laboratory calculations and simulations

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