Study and Analysis of Advanced Countermeasures against Interference Threats for Global Navigation Satellite Systems

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GRAIL lunar gravity field recovery simulations based on short-arc analysis

The Moon is a fascinating planet with a great importance to planetary science. Due to the lack of geological activities on the Moon, it keeps the historical record of the early Solar System. The knowledge gained from the evolution of the Moon can be extended to other planets. The Gravity Recovery and Interior Laboratory (GRAIL) mission is the lunar analog of the successful terrestrial Gravity Recovery and Climate Experiment (GRACE) mission to unlock secrets of the Moon. It will provide data to derive the global lunar gravity field with a vast improvement on both the near side and the far side by the implementation of low-low satellite-to-satellite tracking (ll-SST) principle. Global gravity field recovery aims at deriving the spherical harmonic coefficients to represent the gravitational potential. In this thesis, the short-arc approach is applied and discussed for GRAIL simulation studies.Der Mond ist ein faszinierender Himmelskörper und hat eine große Bedeutung in der Planetologie. Aufgrund fehlender geologischer Aktivitäten kann der Mond die Geschichte des Frühstadiums unseres Sonnensystems wiedergeben. Das Wissen über die Entwicklung des Mondes kann auf andere Planeten übertragen werden. Die GRAIL-Mission (Gravity Recovery and Interior Laboratory) ist eine zu GRACE (Gravity Recovery and Climate Experiment) analoge Mission zur Erforschung des Mondes. Dabei werden Daten zur Bestimmung des lunaren Gravitationsfeldes basierend auf dem Prinzip des low-low Satellite-to-Satellite Tracking (ll-SST) erzeugt, die zu einer deutlichen Genauigkeitssteigerung des Gravitationsfeldes der Vorder- und Rückseite des Mondes führen sollen. Bei der globalen Gravitationsfeldbestimmung wird das Gravitationsfeld als eine Entwicklung in Kugelflächenfunktionen repräsentiert, wobei die Kugelfunktionskoeffizienten bestimmt werden müssen. In dieser Arbeit wird im Rahmen von Simulationsstudien das Randwertproblem für kurze Bahnbögen für die Anwendung bei GRAIL getestet

An Assessment of Impact of Adaptive Notch Filters for Interference Removal on the Signal Processing Stages of a GNSS Receiver

With the fast growing diffusion of the real-time high accuracy applications based on the Global Navigation Satellite System (GNSS), the robustness of the GNSS receiver performance has become a compelling requirement. Disruptive effects can be induced to the signal processing stages of GNSS receivers due to the disturbances from Radio-Frequency Interference (RFI), even leading to a complete outage of the positioning and timing service. A typical RFI threat to the GNSS signals is represented by portable jammers which transmit swept-frequency (chirp) signals in order to span the overall GNSS bandwidth. The implementation in the receivers of Adaptive Notch Filters (ANFs) for chirp cancellation has been extensively investigated and proved to be an efficient countermeasure. However, the performance of ANF is strongly dependent on its configuration setup. Inappropriate parameter settings of the ANF for interference removal may induce severe distortion to the correlation process. In addition, an effective mitigation will still introduce a vestigial signal distortion contributed by the residual unmitigated chirp and the ANF operation itself, being not negligible for high accuracy solutions. This paper addresses the detailed analysis for assessing the effects of interference mitigation by notch filtering. A bias compensation strategy is proposed, wherein for each Pseudo Random Noise (PRN) the biases due to the parameter settings of the notch filter are estimated and compensated. The impact of using the ANF operation on chirp signals at the acquisition and tracking stages of GNSS receivers is analyzed. On the basis of the three proposed metrics, the effects can be quantitatively estimated to depict a complete picture of the most influential parameters of the chirp and the ANF configurations, as well as the optimal achievable performance at the acquisition and tracking stages

A Matlab Toolbox for Feature Importance Ranking

More attention is being paid for feature importance ranking (FIR), in particular when thousands of features can be extracted for intelligent diagnosis and personalized medicine. A large number of FIR approaches have been proposed, while few are integrated for comparison and real-life applications. In this study, a matlab toolbox is presented and a total of 30 algorithms are collected. Moreover, the toolbox is evaluated on a database of 163 ultrasound images. To each breast mass lesion, 15 features are extracted. To figure out the optimal subset of features for classification, all combinations of features are tested and linear support vector machine is used for the malignancy prediction of lesions annotated in ultrasound images. At last, the effectiveness of FIR is analyzed according to performance comparison. The toolbox is online (https://github.com/NicoYuCN/matFIR). In our future work, more FIR methods, feature selection methods and machine learning classifiers will be integrated

Air quality data clustering using EPLS method

[EN] Nowadays air quality data can be easily accumulated by sensors around the world. Analysis on air quality data is very useful for society decision. Among five major air pollutants which are calculated for AQI (Air Quality Index), PM2.5 data is the most concerned by the people. PM2.5 data is also cross-impacted with the other factors in the air and which has properties of non-linear non-stationary including high noise level and outlier. Traditional methods cannot solve the problem of PM2.5 data clustering very well because of their inherent characteristics. In this paper, a novel model-based feature extraction method is proposed to address this issue. The EPLS model includes: (1) Mode Decomposition, in which EEMD algorithm is applied to the aggregation dataset; (2) Dimension Reduction, which is carried out for a more significant set of vectors; (3) Least Squares Projection, in which all testing data are projected to the obtained vectors. Synthetic dataset and air quality dataset are applied to different clustering methods and similarity measures. Experimental results demonstrate that EPLS is efficient in dealing with high noise level and outlier air quality clustering problems, and which can also be adapted to various clustering techniques and distance measures. (C) 2016 Elsevier B.V. All rights reserved.This work was supported in part by the National Natural Science Foundation of China (Nos. 61440018, 61501411), the Hubei Natural Science Foundation (No. 2014CFB904), China Scholarship Council Funding.Chen, Y.; Wang, L.; Li, F.; Du, B.; Choo, KR.; Hassan Mohamed, H.; Qin, W. (2017). Air quality data clustering using EPLS method. Information Fusion. 36:225-232. https://doi.org/10.1016/j.inffus.2016.11.015S2252323