In this paper we introduce a self-tuning Kalman filter for fast time-domain amplitude estimation of noisy harmonic signals with non-stationary amplitude and harmonic distortion, which is the problem of a contactvoltage measurement to which we apply the proposed method. The research method is based on the self-tuning of the Kalman filter's dropping-off behavior. The optimal performance (in terms of accuracy and fast response) is achieved by detecting the jump of the amplitude based on statistical tests of the innovation vector of the Kalman filter and reacting to this jump by adjusting the values of the covariance matrix of the state vector. The method's optimal configuration of the parameters was chosen using a statistical power analysis. Experimental results show that the proposed method outperforms competing methods in terms of speed and accuracy of the jump detection and amplitude estimation.

This paper deals with the amplitude estimation in the frequency domain of low-level sine waves, i.e. sine waves spanning a small number of quantization steps of an analog-to-digital converter. This is a quite common condition for high-speed low-resolution converters. A digitized sine wave is transformed into the frequency domain through the discrete Fourier transform. The error in the amplitude estimate is treated as a random variable since the offset and the phase of the sine wave are usually unknown. Therefore, the estimate is characterized by its standard deviation. The proposed model evaluates properly such a standard deviation by treating the quantization with a Fourier series approach. On the other hand, it is shown that the conventional noise model of quantization would lead to a large underestimation of the error standard deviation. The effects of measurement parameters, such as the number of samples and a kind of the time window, are also investigated. Finally, a threshold for the additive noise is provided as the boundary for validity of the two quantization models