No abstract

No abstract

The Yellow Sea is a strongly tidally-driven and highly stratified shallow sea due to the presence of the Yellow Sea Cold Water Masses. Observations show that the near-inertial event sustains for 10 days with a peak near-inertial velocity of 0.15m/s, which accounts for 30% of the total velocity during the passage of a cyclone. Near-inertial velocity is dominated by the first baroclinic mode with one zero-crossing at the depth of the maximum stratification and two velocity peaks in the mixed layer and below the thermocline, respectively. Combined with numerical simulation analysis, it was found that the two velocity peaks are controlled by stratification and tides. In the mixed layer, the near-inertial peak is induced by wind stress, but the strong stratification constrains the downward propagation of the near-inertial energy. With respect to the near-inertial peak below the thermocline, it is associated with a barotropic wave generated at the coast and propagating offshore. However, the near-inertial flow within the bottom layer is reduced by the eddy viscosity of the tidal currents. Within the thermocline, the pronounced vertical convection due to velocity shear weakens the intensity of the near-inertial flow.

No abstract

Realizing a reliable and robust localization based on mobile nodes plays a critical role in increasing pervasive sensing environments and location-based services (LBS). Although the Global Positioning System (GPS) has been widely used in outdoor environments, indoor robust positioning is still a challenging problem because of the unavailability of GPS and complex indoor environments where non-line-of-sight (NLOS) occurs due to reflection and diffraction. To solve the problem, an accurate and robust integration localization scheme based on Kalman filter is proposed in this paper. In the scheme, we merge the two heterogeneous but complementary positioning technologies on the mobile node equipped with both inertial sensors and the chirp-spread-spectrum ranging hardware. In order to NLOS identification and decrease NLOS error, a novel sight-state estimation method based on the Markov model is proposed. Besides, experiments have been carried out in real indoor NLOS environment to evaluate performance of proposed system. Experimental results indicate a remarkable performance improvement by using the proposed integrated system.