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GNSS (Global Navigation Satellite Systems) are today the core of all new ITS applications and require increasing accuracy in particular in dense urban areas where complex localization scenarios become more and more frequent as the number of users evolves. In order to reduce inaccuracy caused by the obstacles around the receiver's antenna, a solution lies in developing new filtering algorithms. These...
GNSS localization is accurate in clear environment where the pseudorange noise distributions are assumed white- Gaussian. But in constricted environment, e.g. dense urban environment, because of the signal reflections on the surrounding obstacles, this assumption cannot be used and accuracy and continuity of service of GNSS receivers are strongly degraded. To enhance the localization performances,...
In satellite navigation system, classical localization algorithms assume that the observation noise is white-Gaussian. This assumption is not correct when the signal is reflected on the surrounding obstacles. That leads to a decrease of accuracy and of continuity of service. To enhance the localization performances, a better observation noise density can be use in an adapted filtering process. This...
In Global Navigation Satellite Systems (GNSS) positioning, the receiver measures the pseudoranges with respect to each observable navigation satellite and determines the user position. The use of many constellations should lead to highly available, highly accurate navigation anywhere. However, it is important to notice that even if modern receivers achieve high position accuracy in line-of-sight (LOS)...
Satellite-based positioning systems do not offer accurate solutions in urban environments because of propagation disturbances noising measurements. Furthermore, usual positioning computation, like extended Kalman filter, relies on considering the noise as a Gaussian centered distribution, which is unrealistic in constrained area. Moreover, today only the US system (GPS) is fully operational. Available...
This paper proposes to apply optimized one-class support vector machines (1-SVMs) to tackle some audio recognition tasks. We show that 1-SVMs provide a significant improvement in performance on event detection and classification. We propose an efficient and accurate approach for detecting events in a continuous audio stream. The proposed method which does not require any pre-trained models is based...
In a sounds recognition system, the most encountered problem is the background noise that can be captured with the sounds to be identified. This paper describes work that has been performed to address this problem. First, the robustness to the environmental noise is investigated for specific kinds of acoustic representation. The representations considered are RASTA-PLP, J-RASTA and wavelets-based...
This paper proposes to apply optimized one-class support vector machines (1-SVMs) as a discriminative framework in order to address a specific audio classification problem. First, since SVM-based classifier with gaussian RBF kernel is sensitive to the kernel width, the width will be scaled in a distribution-dependent way permitting to avoid under-fitting and over-fitting problems. Moreover, an advanced...
Summary form only given. This work forms part of a larger investigation into the integration of sound surveillance in a monitoring application. However, mismatches between training and testing environment severely degrade performance. Thus, in order to enhance the system robustness, we explored two issues: the training mode and the model adaptation. First, the originality of our system resides in...
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