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Reconstructing the history of elite communication in ancient China benefits from additional archaeological evidence. We combine textual analysis with new human stable carbon and nitrogen isotope data from two Chu burials in the Jingzhou area to reveal significant dietary differences among Chu nobles of the middle Warring States period (c. 350 BC). This research provides important new information on the close interaction between the aristocratic families of the Qin and Chu.
Considering that traditional visual navigation cannot be utilised in low illumination and sparse feature environments, a novel visual-inertial integrated navigation method using a Structured Light Visual (SLV) sensor for Micro Aerial Vehicles (MAVs) is proposed in this paper. First, the measurement model based on an SLV sensor is studied and built. Then, using the state model based on error equations of an Inertial Navigation System (INS), the measurement model based on the error of the relative motion measured by INS and SLV is built. Considering that the measurements in this paper are mainly related to the position and attitude information of the present moment, the state error accumulation in traditional visual-inertial navigation can be avoided. An Adaptive Sage-Husa Kalman Filter (ASHKF) based on multiple weighting factors is proposed and designed to make full use of the SLV measurements. The results of the simulation and the experiment based on real flight data indicate that high accuracy position and attitude estimations can be obtained with the help of the algorithm proposed in this paper.
BeiDou signals are modulated with a Neumann-Hofman (NH) code of 1 kbps. The frequent bit transitions lead to a sensitivity attenuation of classic acquisition algorithms. In order to increase acquisition sensitivity for weak BeiDou signals, a novel algorithm based on modified zero-padding and differential correlation is proposed. First, a zero-padding method is used to weaken the effect of NH code. Second, the differential coherent delay time is modified to 20 ms to remove the influence of data bit transitions. The integration time is extended to 10 ms to increase acquisition sensitivity. Finally, Monte Carlo simulations and real data tests are conducted to analyse the performance of the proposed algorithm. Simulated results show that the proposed acquisition algorithm outperforms traditional algorithms under a Carrier-to-Noise ratio (C/N0s) of 20~38 dB-Hz. The sensitivity of the proposed algorithm is about 10dB higher than traditional 6 ms repeated search algorithms. Real data test results show that the proposed algorithm outperforms the traditional method with weak signals. This algorithm can remove the effect of NH code and effectively increase the acquisition sensitivity. The proposed algorithm is suitable for acquisition of weak BeiDou signals.
The accuracy and fault tolerance of filters are directly affected by the filter architecture and algorithm, thus influencing navigation performance. The chi square detection used in the conventional reset federated filter is not sensitive to soft faults, and it is easy to cause the health subsystem to be polluted through information sharing. It is a challenge to design an adaptive reset federated filter to improve the performance of the navigation system. Therefore, taking the Strapdown Inertial Navigation System/Global Positioning System/Celestial Navigation System/Synthetic Aperture Radar (SINS/GPS/CNS/SAR) integrated navigation system as an example, an adaptive federated filter architecture for vector-formed information sharing without a fault isolation module is designed in this paper. The proposed method uses the two-state chi square detection algorithm to calculate the parameters corresponding to each state, making the state with higher accuracy obtain a greater information distribution coefficient. In addition, according to the value of vector-formed information sharing, an adaptive coefficient of measurement noise is designed. This improves the adaptability of the navigation system to soft faults. Simulation results show that the accuracy of the proposed algorithm has the same performance compared with the conventional method under normal circumstances. When the sensor has a soft fault, the adaptive federated filter algorithm proposed in this paper can adaptively adjust the distribution coefficients, eliminate the influence of the fault information and improve the precision of the navigation system. The approach described in this paper can be used in multi-sensor integrated navigation. It will have better performance in engineering applications.
In life-critical applications, the real-time detection of faults is very important in Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation systems. A new fault detection method for soft fault detection is developed in this paper with the purpose of improving real-time performance. In general, the innovation information obtained from a Kalman filter is used for test statistic calculations in Autonomous Integrity Monitored Extrapolation (AIME). However, the innovation of the Kalman filter is degraded by error tracking and closed-loop correction effects, leading to time delays in soft fault detection. Therefore, the key issue of improving real-time performance is providing accurate innovation to AIME. In this paper, the proposed algorithm incorporates Least Squares-Support Vector Machine (LS-SVM) regression theory into AIME. Because the LS-SVM has a good regression and prediction performance, the proposed method provides replaced innovation obtained from the LS-SVM driven by real-time observation data. Based on the replaced innovation, the test statistics can follow fault amplitudes more accurately; finally, the real-time performance of soft fault detection can be improved. Theoretical analysis and physical simulations demonstrate that the proposed method can effectively improve the detection instantaneity.
Zero velocity update (ZUPT) is widely discussed for error restriction in land vehicle Inertial Navigation Systems (INSs) and wearable pedestrian INSs to overcome the problems of Global Positioning System (GPS) unavailability in urban canyons or indoor scenarios. In this paper, an online smoothing method for ZUPT-aided INSs is presented. By introducing the Rauch–Tung–Striebel (RTS) smoothing method into the ZUPT-aided INS, position errors can be effectively restrained not only at stop points but during the whole trajectory. By integrating reverse navigation with a ZUPT smoother, the method realises forward and real-time processing. Compared with existing approaches, it can improve the position accuracy in real time without any other sensors, which is well suited for applications on high-accuracy navigation in GPS-challenging environments. Accuracy test results with different Inertial Measurement Units (IMUs) show that the developed method can significantly decrease position errors from hundreds or thousands of metres to below ten metres. During the whole trajectory, the online smoothing method ensures the maximum position errors at non-stop points can reach the same level of accuracy at stop points. A delay test result proves that the delay of the reverse online smoothing method proposed in this paper is much shorter than existing online smoothing methods.
In this paper, a fault-tolerant velocity estimation method is proposed for quadrotors in a GPS denied environment. A novel filter is developed in light of the quadrotor model and measurements from optical flow and inertial sensors. The proposed filter is capable of detecting and isolating the optical flow sensor faults, by which the velocity estimation accuracy and stability will be improved. It is also demonstrated that the wind velocity is observable in the proposed filter. Therefore, the new filter can also be implemented in a windy environment, which is a significant improvement to the previous model-aided inertial sensor estimator. At the end, some simulations are carried out to verify the advantages of our method.
The errors of an inertial navigation system (INS) in response to gyros' errors can be effectively reduced by the rotation technique, which is a commonly used method to improve an INS's accuracy. A gyro's error consists of a deterministic contribution and a stochastic contribution. The compensation effects of gyros' deterministic errors are clear now, but the compensation effects of gyros' stochastic errors are as yet unknown. However, the compensation effects are always needed in a rotational inertial navigation system's (RINS) error analysis and optimization study. In this paper, the compensation effects of gyros' stochastic errors, which are modelled as a Gaussian white (GW) noise plus a first-order Markov process, are analysed and the specific formulae are derived. During the research, the responses of an INS's and a RINS's position error equations to gyros' stochastic errors are first analysed. Then the compensation effects of gyros' stochastic errors brought by the rotation technique are discussed by comparing the error propagation characteristics in an INS and a RINS. In order to verify the theory, a large number of simulations are carried out. The simulation results show a good consistency with the derived formulae, which can indicate the correctness of the theory.
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