Development of a Digital Optimal Filter Platform

Detta är en Uppsats för yrkesexamina på avancerad nivå från Uppsala universitet/Signaler och System

Författare: Joakim Brykt; [2018]

Nyckelord: ;

Sammanfattning: This report is the result of a Master Thesis project which is a part of the Master programme in Electrical Engineering at Uppsala universitet. The purpose of this Master Thesis project is to develop an embedded platform for the design and implementation of optimal digital filters, in particular, the Kalman and the Wiener filter. In this project these filters are used for noise reduction on noisy signals. The project is a further development of a previous Master Thesis project where a Universal Filter Bank was developed. The Filter Bankis used for designing and implementing various linear digital filters such as lowpass, highpass, bandpass and bandstop. The Filter Bank is a hand held box with two input and two output connections and a human-device interface (HDI) including a Liquid Crystal Display (LCD) and a keypad. It contains anti-aliasing and reconstruction (analog) filters and an ARM 32-bit Microcontroller Unit (MCU) which is programmed in the C programming language. The HDI lets the user specify a desired digital filter. In this project the Kalman and Wiener filtering algorithms were first developed in MATLAB and tested with simulated autoregressive–movingaverage (ARMA) processes (signals) in additive white noise. Aftershown to work, they were implemented on the ARM 32-bit MCU development kit, and finally ported to the Filter Bank. A user interface specially for the specifications of the filters has been created. The Kalman and Wiener filtering algorithms have been tested using the same noisy ARMA processes and assessed in terms of the Normalized Mean Square Error (NMSE). The results have shown that both the Wiener and Kalman filters running on the development kit and the Filter Bank are successful in reducing noise. The Kalman filter is shown to perform better than the Wiener filter, which can be due to the extra information about the signal used in the Kalman filter. The performance of both algorithms are heavily dependent on the pre knowledge about the desired signal.

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