Zhi Xiong

Chi-square and SPRT combined fault detection for multisensor navigation

A fault-detection algorithm for a redundant multisensor navigation system for hypersonic cruise vehicles (HCVs) is proposed. The algorithm comprehensively diagnoses failures according to the failure level monitored by the sequential probability ratio test (SPRT) and chi-square test as well as the failure trend monitored by the SPRT. A test statistics feedback-reset loop is also added to shorten the recovery time after failure ceases. Simulations indicate improvements in both failure detection and recovery speed, contributing to improved accuracy and stability in HCV fault-tolerant navigation.

Dynamic calibration method for SINS lever-arm effect for HCVs

A dynamic calibration method for the lever-arm effect of the Strap-Down Inertial Navigation System (SINS) on hypersonic cruise vehicles is proposed. The lever-arm length is extended as the state variables of the Kalman filter; the model of the lever-arm effect is built, and observability is analyzed. Simulation results show that the proposed method can effectively calibrate the lever-arm length. The calibration results are utilized to compensate the lever-arm effect, and the accuracy of SINS is improved.

SINS/GPS/CNS information fusion system based on improved Huber filter with classified adaptive factors for high-speed UAVs

For High-speed UAV, the measurement noise of GPS and star sensor show non-Gaussian characteristics in high-dynamic and high speed flight. In order to improve the system performance in the above situation, this paper presents an INS/GPS/CNS integrated navigation system and builds the asynchronous measurement model. The system measurement noise feature has also been analyzed according to a perturbed Gaussian mode. Furthermore, this paper designs an integrated navigation algorithm based on improved Huber filter with classified adaptive factors (CAHF), which could improve the precision of position, velocity and attitude in the condition of perturbed measurement noise. Simulation cases involving both CAHF and Kalman Filter are provided to validate the advantage of CAHF.

Research on multi-sensor information fusion system application for UAVS with long range and high altitude

Multi-sensors combination is an effective means to improve the accuracy and fault tolerance of the navigation system for UAVs with long range and high altitude. Based on analyses of attitude determination using STAR sensor, this paper presents a SINS/STAR/GPS information fusion navigation system. To solve the problem of the incoordinate interval characteristics of multi-sensors, an asynchronous centralized Kalman Filter (AKF) is designed., and the filter period is divided to time update period and measurement update period. An extrapolation method is designed to deal with GPS information. Moreover, a new model is designed to solve the problem of attitude combination in process of vertical mobility. Simulation results indicate that filtering accuracy is improved by 50% with the Kalman Filter, and the method is of important value in engineering application

Stepwise fusion algorithm with dual correction for multi-sensor navigation

In high-altitude, long-endurance unmanned aerial vehicles, a celestial attitude determination system is used to enhance the inertial navigation system (INS)/global positioning system (GPS) to achieve the required attitude performance. The traditional federal filter is not applicable for INS/GPS/celestial attitude determination system information fusion because it does not consider the mutually coupled relationship between the horizontal reference error in the celestial attitude determination system and the navigation error; this limitation results in reduced navigation accuracy. This article proposes a novel stepwise fusion algorithm with dual correction for multi-sensor navigation. Considering the horizontal reference error, the celestial attitude determination system measurement model is constructed and the issues involved in applying the federal filter are discussed. Then, preliminary error estimation and horizontal reference compensation are added to the navigation architecture. In addition, a sequential update strategy is derived to estimate the attitude error with the compensated celestial attitude determination system based on the preliminary estimation. A stepwise correction filtering algorithm with interactive preliminary and sequential updates that can effectively fuse celestial attitude determination system measurements with the INS/GPS is constructed. High-altitude, long-endurance unmanned aerial vehicle navigation in a remote sensing task is simulated to verify the performance of the proposed method. The simulation results demonstrate that the horizontal reference error is effectively compensated, and the attitude accuracy is significantly improved after stepwise error estimation and correction. The proposed method also provides a novel multi-sensor integrated navigation architecture with mutually coupled errors; this architecture is beneficial in unmanned aerial vehicle navigation applications.

A Minor Fault Detection Algorithm Based on Sliding-Window Accumulated Parity Vector

The receiver autonomous integrity monitoring (RAIM) technology, which with the advantage of quick response and completed independent, plays an important role in the integrity monitoring system of GNSS. With the development of BDS and other navigation satellite systems, the dual-mode and the multi-mode satellite navigation receivers are paid more and more attention, which provide not only superior positioning accuracy, but also sufficient redundancy for RAIM. Among the fault detection algorithms, the “snapshot” detection methods show satisfied rapidity for detecting large fault in pseudorange measurement, while their abilities to detect a minor fault is poor. Although minor faults usually not serious enough to cause unavailable positioning of GNSS receiver immediately, but their influence on positioning error accuracy will accumulate with time to a significant level. In order to overcome the shortcoming of the traditional RAIM algorithm in detecting minor faults, an improved minor fault detection algorithm based on sliding-window accumulated parity vector is proposed in this paper. In the proposed algorithm, a new statistics for fault detection is obtained by accumulating the parity vector in a sliding window, so as to make an optimized fault diagnosis results online. A simulation system is established for validating the proposed algorithm. The simulation results show that the proposed algorithm could shorten the time delay in detecting minor faults.

Robust Inertial-Astronomic Attitude Determination Algorithm with Adaptive Star Geometrical Error Model for HCVs

AbstractInertial/astronomic integration is an effective way to improve the accuracy of attitude determination of hypersonic cruise vehicles (HCVs). Compared with common low-dynamic aircraft, the environmental affection during hypersonic flight leads to the non-Gaussian noise character of astronomic observation. Meanwhile, rapid star geometry changing during HCVs’ rapid movement causes redistribution of errors in astronomic measurements and significant variation of its main Gaussian characteristic. A kind of robust inertial/astronomic attitude determination algorithm with adaptive star geometrical error model is proposed. The adaptive star geometrical error distribution model is established for obtaining the main Gaussian model of astronomic measurement misalignment errors in flight. After that, inertial/astronomic integration model–based on misalignment errors is proposed, which avoids Euler angle transformation. On these bases, the improved robust filter algorithm is designed, which utilizes real-time as...

Adaptive Huber-Based Filter for Hypersonic Cruise Vehicle Navigation

The navigation for hypersonic cruise vehicle (HCV) is a challenging task because of the complex vehicle dynamic and sensor measurement noise it suffered. This paper proposes a kind of adaptive robust Kalman filter using Mahalanobis distance for HCV navigation. The innovation-based adaptive estimation is discussed first. Based on Mahalanobis distance theory, a kind of robust covariance matrix estimation method is used to modify the innovation-based adaptive Kalman filter. Considering that the vehicle maneuver characteristics and noise statistics parameters varies during different periods, dual-frequency tuning for unknown noise statistics is designed based on this. The algorithm proposed by this paper is applied to hypersonic cruise vehicle navigation. Simulation has been made to verify the performance of the new algorithm according to HCV flight profile and characteristics; both Gaussian and non-Gaussian simulation are included.