Wideband GNSS signals suffer signal distortions such as waveform deformations and correlation peak reduction when traverse the ionosphere. Basing on the standard model of the ionosphere, we first demonstrate a modified ionosphere model to capture the ionosphere dispersion effects on wideband signals. We decompose the first-order ionosphere model into Taylor series. By using the first three terms of Taylor series, it is possible to account for all frequency components of wideband signals rather than treating them as single tone. We then make an analysis of the ionosphere dispersion effects on wideband GNSS signal tracking. It is revealed that the ionosphere dispersion degrades correlation peak results and shifts carrier-phase in the phase locked loop (PLL) output but dose not cause an additional delay for code measurements. Furthermore, we carry out a simulation for evaluating the ionosphere dispersion effects on tracking of various new generation wideband GNSS signals such as Galileo E5 AltBOC(15, 10) signals and BDS B3 BOC(15, 2.5) signals during ionosphere quietness and activities. The results show that the wider the bandwidth and the greater the total electron content (TEC) values, the more dramatic the ionosphere effects are. The Galileo E5 AltBOC(15, 10) signals are most affected among various wideband GNSS signals. For AltBOC(15, 10) signal tracking the correlation power loss is around 0.1 dB and the carrier-phase change is about 20° caused by the dispersion in quiet ionosphere case, and increases up to 0.35 dB and 33° during ionosphere activities, respectively.

The combination of underwater acoustic processing and the Global Navigation Satellite System (GNSS) has achieved remarkable economic benefits in offshore operations. As the key technology of GNNS positioning, feature extraction of underwater acoustic signals is affected by the complex marine environment. To extract more effective information from underwater acoustic signals, we use four types of multi-scale entropies, including multi-scale sample entropy (MSE), multi-scale fuzzy entropy (MFE), multi-scale permutation entropy (MPE), and multi-scale dispersion entropy (MDE), to analyze and distinguish underwater acoustic signals. In this study, two groups of real-word underwater acoustic signal experiments were performed for feature extraction of ship-radiated noises (SRNs) and ambient noises (ANs). The results indicated that the performance of the MFE-based feature extraction method is superior to that of feature extraction methods based on the other three entropies under the same number of features, and the highest average recognition rate (ARR) of the MFE-based feature extraction method for SRNs reaches 100% when the number of features is 3.