The volume of telemetry data is gradually increasing, both because of the increasingly larger number of parameters involved, and the use of higher sampling frequencies. Efficient data compression schemes are therefore needed in space telemetry systems to improve transmission efficiency and reduce the burden of required spacecraft resources, in particular regarding their transmitter power. In our primary study, a D-CLU algorithm was proposed to perform lossless compression for telemetry data, and achieve better performance. However, a limitation of this algorithm is that the compression time may become longer when the clustering head (CH) and outlier (which are compressed by LZW algorithm) numbers increase. To reduce compression delay, this paper proposed a modified character string (MCS) parallel search strategy for LZW algorithm (denoted by MCS-based LZW). The proposed MCS-based LZW algorithm designs coding principle, dictionary update rule and search strategy according to the character string matching results. Example verification and simulation results show that the proposed algorithm can effectively decrease the dictionary search times, and thus reduce the compression time.
The study on the frequency estimation of a real sinusoid has been addressed in this article. Discrete‐Fourier‐transform (DFT)‐based frequency estimation always uses the maximum bin of DFT as a coarse estimation followed by a fine estimation algorithm, which computes a correction term to compensate for the initial frequency offset caused by the coarse estimation. To enhance the frequency estimation accuracy, a fine estimation algorithm based on DFT samples and fuzzy logic (FL) is proposed in this study. Firstly, a modified phase‐based algorithm is presented to estimate the sign of the correction term. Then, a main‐lobe coefficient and a side‐lobe coefficient are constructed using the amplitude of three spectral lines to calculate the correction term. A FL controller is utilised to generate a weighted factor to adjust the weight of the main‐lobe coefficient and the side‐lobe coefficient in the formula of the correction term. Compared with other existing fine algorithms, the proposed algorithm improves the correct probability of sign estimation by 10%–20%, and improves the estimation accuracy by 5%–8% at lower carrier‐to‐noise ratios (CNRs). In addition, the performance of the proposed algorithm is less affected by the initial frequency offset than other algorithms.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.