An innovative information fusion method with adaptive Kalman filter for integrated INS/GPS navigation of autonomous vehicles

The paradigm of multisensor data fusion has been evolved from a centralized architecture to a decentralized or distributed architecture along with the advancement in sensor and communication technologies. These days, distributed state estimation and data fusion has been widely explored in diverse fields of engineering and control due to its superior performance over the centralized one in terms of flexibility, robustness to failure and cost effectiveness in infrastructure and communication. However, distributed multisensor data fusion is not without technical challenges to overcome: namely, dealing with cross-correlation and inconsistency among state estimates and sensor data. In this paper, we review the key theories and methodologies of distributed multisensor data fusion available to date with a specific focus on handling unknown correlation and data inconsistency. We aim at providing readers with a unifying view out of individual theories and methodologies by presenting a formal analysis of their implications. Finally, several directions of future research are highlighted.

Distributed Multisensor Data Fusion under Unknown Correlation and Data Inconsistency.

The INS/GNSS integration is the commonly used technique for hypersonic vehicle navigation. However, owing to the complicated flight dynamics with high maneuverability and large flight envelope, the dynamic model of INS/GNSS integration inevitably exists errors which degrades the navigation performance of a hypersonic vehicle seriously. In this paper, a new model predictive based unscented Kalman filter (MP-UKF) is proposed to address this problem. The MP-UKF employs the concept of model predictive filter for the establishment of a dynamic model error estimator, and it subsequently compensate the model error estimation to UKF for nonlinear state estimation. Since the MP-UKF could predict the dynamic model error persistently and correct the filtering procedure of UKF online, it improves the UKF adaptiveness and is promising for the performance enhancement of INS/GNSS integration for hypersonic vehicle navigation. Simulation results and comparison analysis have been conducted to demonstrate the effectiveness of the proposed method.

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Model Predictive Based Unscented Kalman Filter for Hypersonic Vehicle Navigation With INS/GNSS Integration

The wind turbine power curve is an important indicator of the performance of a wind turbine. Modeling and monitoring the power curve can detect wind turbine operation abnormalities and degradation in a timely manner. First, this paper points out the drawbacks of the standard binned power curve modeling method of IEC-61400-12-1. Multiple factors that influence wind energy capture and power output of a wind turbine are analyzed in detail and used as the power curve model inputs. A multivariable power curve model is constructed with a modified Cholesky decomposition Gaussian process (GP) and validated using wind turbine Supervisory Control and Data Acquisition (SCADA) data. A sequential probability ratio test with two groups of hypotheses is introduced to analyze and detect abnormal changes in GP power curve prediction residuals and thus detect abnormal operation. In order to locate failed components when an alarm is identified, longitudinal and transverse data comparisons are proposed to check the operation of specific components. The modeling and monitoring methods proposed in this paper successfully identify faults and locate the faulty component for two wind turbines with anemometer failure and pitch system failure, respectively.

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Wind Turbine Power Curve Modeling and Monitoring With Gaussian Process and SPRT

GPS/IRS AIME: Calculation of Thresholds and Protection Radius Using Chi-Square Methods. | 文献情報 | J-GLOBAL 科学技術総合リンクセンター

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.

Chi-square and SPRT combined fault detection for multisensor navigation

Integrating celestial navigation system (CNS) and inertial navigation system (INS) is an effective way to realize autonomous navigation for hypersonic cruise vehicles. In multi-stars synchronous ob...

A new tightly-coupled INS/CNS integrated navigation algorithm with weighted multi-stars observations:

A robust filtering algorithm for integrated navigation system of aerospace vehicle in launch inertial coordinate

The invention discloses an on-line dynamic inertia sensor error identification method of an inertia navigation system, and belongs to the technical field of the inertia navigation. The method comprises the following steps: firstly establishing an error model of an inertia sensor, and establishing a Kalmar filter model containing an inertia sensor error state quantity according to the error model of the inertia sensor; giving out a dynamic exciting method of the inertia sensor error; finally designing an error identification dynamic track according to the error dynamic exciting method, and performing the on-line dynamic identification on an inertia sensor error by utilizing Kalmar filtering. By adopting the method, the inertia sensor error can be effectively excited in the dynamic flight process of an aircraft, the on-line identification of the inertia sensor error is realized, and the important reference significance on improving the navigation precision of an inertia navigation system can be realized.

On-line dynamic inertia sensor error identification method of inertia navigation system

The invention discloses an on-line calibration method for a lever arm effect error of an inertial navigation system in a high dynamic environment and belongs to the technical field of error calibration for inertial sensors for inertial navigation. The method comprises the following steps: firstly, building a lever arm effect error model of an accelerometer in the dynamic flight process of a hypersonic flight vehicle; on the basis of a traditional stochastic error model of an accelerometer, expanding the length of a lever arm to a state variable of a kalman filter system, and establishing a filtering state equation and a position and speed linear measurement equation, which contain the lever arm effect error; and finally, in the dynamic flight process of the hypersonic flight vehicle, dynamically calibrating and correcting the lever arm effect error in real time. The method can perform on-line calibration and correction on the lever arm effect error in the inertial navigation system in the high dynamic flight process of the flight vehicle, effectively improves the performance of the inertial navigation system of the hypersonic flight vehicle, and is suitable for engineering application.

Shi Lijuan

Papers

Chi-square and SPRT combined fault detection for multisensor navigation

A new tightly-coupled INS/CNS integrated navigation algorithm with weighted multi-stars observations:

A robust filtering algorithm for integrated navigation system of aerospace vehicle in launch inertial coordinate

On-line dynamic inertia sensor error identification method of inertia navigation system

On-line calibration method for lever arm effect error of inertial navigation system in high dynamic environment