Abstract. Improving an estimate of an incoherent scatter radar signal is vital to provide reliable and unbiased information about the Earth's ionosphere. Thus optimizing the measurement spatial and temporal resolutions has attracted considerable attention. The optimization usually relies on employing different kinds of pulse compression filters in the analysis and a matched filter is perhaps the most widely used one. A mismatched filter has also been used in order to suppress the undesirable sidelobes that appear in the case of matched filtering. Moreover, recently an adaptive pulse compression method, which can be derived based on the minimum mean-square error estimate, has been proposed. In this paper we have investigated the performance of matched, mismatched and adaptive pulse compression methods in terms of the output signal-to-noise ratio (SNR) and the variance and bias of the estimator. This is done by using different types of optimal radar waveforms. It is shown that for the case of low SNR the signal degradation associated to an adaptive filtering is less than that of the mismatched filtering. The SNR loss of both matched and adaptive pulse compression techniques was found to be nearly the same for most of the investigated codes for the case of high SNR. We have shown that the adaptive filtering technique is a compromise between matched and mismatched filtering method when one evaluates its performance in terms of the variance and the bias of the estimator. All the three analysis methods were found to have the same performance when a sidelobe-free matched filter code is employed.