An Adaptive Unscented Kalman Filtering Algorithm for MEMS/GPS Integrated Navigation Systems

MEMS/GPS integrated navigation system has been widely used for land-vehicle navigation. This system exhibits large errors because of its nonlinear model and uncertain noise statistic characteristics. Based on the principles of the adaptive Kalman filtering (AKF) and unscented Kalman filtering (AUKF) algorithms, an adaptive unscented Kalman filtering (AUKF) algorithm is proposed. By using noise statistic estimator, the uncertain noise characteristics could be online estimated to adaptively compensate the time-varying noise characteristics. Employing the adaptive filtering principle into UKF, the nonlinearity of system can be restrained. Simulations are conducted for MEMS/GPS integrated navigation system. The results show that the performance of estimation is improved by the AUKF approach compared with both conventional AKF and UKF

Research on Wavelet Singularity Detection Based Fault-Tolerant Federated Filtering Algorithm for INS/GPS/DVL Integrated Navigation System

Soft faults in navigation sensors will lead to the degradation of the accuracy and reliability of integrated navigation system. To solve this problem, a wavelet analysis and signal singularities based soft fault detection method are given out. To find signal singularities and detect the faults, the modulus maxima values are calculated after the wavelet transform of original signal. By calculating the Lipschitz exponent using the modulus maxima value at the fault point, the fault types are distinguished. Then, a fault-tolerant federated filtering algorithm for the calibration of INS/GPS/DVL integrated navigation system is proposed. Simulations are conducted and results show that sensor soft faults can be detected accurately. By effectively isolating the fault and refactoring information, the accuracy and reliability of navigation system are improved

A Novel Method for Target Navigation and Mapping Based on Laser Ranging and MEMS/GPS Navigation

Making the sensor rigidly mounted in the target is the common characteristic of conventional navigation system. However, it is difficult or impossible to realize that for special applications such as the positioning of hostile aircraft. A novel new algorithm for target navigation and mapping is designed based on the position, attitude, and ranging information provided by laser distance detector (electronic distance measuring, LDS) and MEMS/GPS navigation, which can solve problem of the target navigation and mapping without any sensor in the target. The detailed error analysis shows that attitude error of MEMS/GPS is the main error source which dominated the accuracy of the algorithm. Based on the error analysis, a calibration algorithm is designed so as to improve the accuracy to a large extent. The result shows that, by using this new algorithm, the performance of target positioning can be efficiently improved, and the positioning error is less than 2 meters for the target within 1 kilometer range

Evaluation for Sortie Generation Capacity of the Carrier Aircraft Based on the Variable Structure RBF Neural Network with the Fast Learning Rate

The neural network has the advantages of self-learning, self-adaptation, and fault tolerance. It can establish a qualitative and quantitative evaluation model which is closer to human thought patterns. However, the structure and the convergence rate of the radial basis function (RBF) neural network need to be improved. This paper proposes a new variable structure radial basis function (VS-RBF) with a fast learning rate, in order to solve the problem of structural optimization design and parameter learning algorithm for the radial basis function neural network. The number of neurons in the hidden layer is adjusted by calculating the output information of neurons in the hidden layer and the multi-information between neurons in the hidden layer and output layer. This method effectively solves the problem that the RBF neural network structure is too large or too small. The convergence rate of the RBF neural network is improved by using the robust regression algorithm and the fast learning rate algorithm. At the same time, the convergence analysis of the VS-RBF neural network is given to ensure the stability of the RBF neural network. Compared with other self-organizing RBF neural networks (self-organizing RBF (SORBF) and rough RBF neural networks (RS-RBF)), VS-RBF has a more compact structure, faster dynamic response speed, and better generalization ability. The simulations of approximating a typical nonlinear function, identifying UCI datasets, and evaluating sortie generation capacity of an carrier aircraft show the effectiveness of VS-RBF

A Novel Optimal Configuration form Redundant MEMS Inertial Sensors Based on the Orthogonal Rotation Method

In order to improve the accuracy and reliability of micro-electro mechanical systems (MEMS) navigation systems, an orthogonal rotation method-based nine-gyro redundant MEMS configuration is presented. By analyzing the accuracy and reliability characteristics of an inertial navigation system (INS), criteria for redundant configuration design are introduced. Then the orthogonal rotation configuration is formed through a two-rotation of a set of orthogonal inertial sensors around a space vector. A feasible installation method is given for the real engineering realization of this proposed configuration. The performances of the novel configuration and another six configurations are comprehensively compared and analyzed. Simulation and experimentation are also conducted, and the results show that the orthogonal rotation configuration has the best reliability, accuracy and fault detection and isolation (FDI) performance when the number of gyros is nine

sedimentarygeochemicalproxiesformethaneseepageatsitec14intheqiongdongnanbasininthenorthernsouthchinasea

Recent studies have shown that specific geochemical characteristics of sediments can be used to reconstruct past methane seepage events. In this work, the correlation between the Sr/Ca and Mg/Ca ratios of sediment samples is analyzed and the sulfate concentration profile in Site C14 from cold-seep sediments in the Qiongdongnan Basin in northern South China Sea is obtained. The results confirmed that, sulfate at 0-247 cm below sea floor (Unit I) is mainly consumed by organic matter sulfate reduction (OSR), while sulfate at 247-655 cm (Unit II) is consumed by both the OSR and the anaerobic oxidation of methane (AOM). In addition, the bottom sediment layer is affected by weak methane seepage. The Mo and U enrichment factors also exhibit similar trends in their respective depth profiles. The responses of trace elements, including Co/Al, Ni/Al, Cr/Al and Zn/Al ratios to methane seepage allowed the study of depositional conditions and methane seepage events. Based on the results, it is speculated that the depositional conditions of Unit II changed with depth from moderate conditions of sulfidic and oxic conditions to locally anoxic conditions, and finally to suboxic conditions due to methane fluid leakage. The stable isotope values of chromium-reducible sulfide produced by AOM and those of sulfide formed by OSR in the early diagenetic environment suffered serious depletion of 34S. This was probably due to weak methane leakage, which caused the slower upward diffusion and the effect of early diagenesis on the samples. It is necessary to consider the effects of depositional environments and diagenesis on these geochemical parameters

Fatty-acids and their delta C-13 characteristics of seep carbonates from the northern continental slope of Gulf of Mexico

Here we reported the fatty-acids and their delta C-13 values in seep carbonates collected from Green Canyon lease block 185 (GC 185; Sample GC-F) at upper continental slope (water depth:similar to 540 m), and Alaminos Canyon lease block 645 (GC 645; Sample AC-E) at lower continental slope (water depth: similar to 2200 m) of the Gulf of Mexico. More than thirty kinds of fatty acids were detected in both samples. These fatty acids are maximized at C-16. There is a clear even-over-odd carbon number predominance in carbon number range. The fatty acids are mainly composed of n-fatty acids, iso-/anteiso-fatty acids and terminally branched odd-numbered fatty acids (iso/anteiso). The low delta C-13 values (-39.99aEuro degrees to.32.36aEuro degrees) of n-C-12:0, n-C-13:0, i-C-14:0 and n-C-14:0 suggest that they may relate to the chemosynthetic communities at seep sites. The unsaturated fatty acids n-C-18:2 and C-18:1 Delta(9) have the same delta C-13 values, they may originate from the Beggiatoa/Thioploca. Unlike other fatty acids, the terminally branched fatty acids (iso/anteiso) show lower delta C-13 values (as low as -63.95aEuro degrees) suggesting a possible relationship to sulfate reducing bacteria, which is common during anaerobic oxidation of methane at seep sites

An Adaptive Unscented Kalman Filtering Algorithm for MEMS/GPS Integrated Navigation Systems

MEMS/GPS integrated navigation system has been widely used for land-vehicle navigation. This system exhibits large errors because of its nonlinear model and uncertain noise statistic characteristics. Based on the principles of the adaptive Kalman filtering (AKF) and unscented Kalman filtering (AUKF) algorithms, an adaptive unscented Kalman filtering (AUKF) algorithm is proposed. By using noise statistic estimator, the uncertain noise characteristics could be online estimated to adaptively compensate the time-varying noise characteristics. Employing the adaptive filtering principle into UKF, the nonlinearity of system can be restrained. Simulations are conducted for MEMS/GPS integrated navigation system. The results show that the performance of estimation is improved by the AUKF approach compared with both conventional AKF and UKF

Sedimentary geochemical proxies for methane seepage at Site C14 in the Qiongdongnan Basin in the northern South China Sea

Recent studies have shown that specific geochemical characteristics of sediments can be used to reconstruct past methane seepage events. In this work, the correlation between the Sr/Ca and Mg/Ca ratios of sediment samples is analyzed and the sulfate concentration profile in Site C14 from cold-seep sediments in the Qiongdongnan Basin in northern South China Sea is obtained. The results confirmed that, sulfate at 0-247 cm below sea floor (Unit I) is mainly consumed by organic matter sulfate reduction (OSR), while sulfate at 247-655 cm (Unit II) is consumed by both the OSR and the anaerobic oxidation of methane (AOM). In addition, the bottom sediment layer is affected by weak methane seepage. The Mo and U enrichment factors also exhibit similar trends in their respective depth profiles. The responses of trace elements, including Co/Al, Ni/Al, Cr/Al and Zn/Al ratios to methane seepage allowed the study of depositional conditions and methane seepage events. Based on the results, it is speculated that the depositional conditions of Unit II changed with depth from moderate conditions of sulfidic and oxic conditions to locally anoxic conditions, and finally to suboxic conditions due to methane fluid leakage. The stable isotope values of chromium-reducible sulfide produced by AOM and those of sulfide formed by OSR in the early diagenetic environment suffered serious depletion of S-34. This was probably due to weak methane leakage, which caused the slower upward diffusion and the effect of early diagenesis on the samples. It is necessary to consider the effects of depositional environments and diagenesis on these geochemical parameters