Redundant Inertial Navigation System Research Articles

An Improved Fault Detection and Isolation Method for Airborne Inertial Navigation System/Attitude and Heading Reference System Redundant System

The integrity of airborne inertial navigation systems (INSs) is the key to ensuring the safe flight of civil aircraft. The airborne attitude and heading reference system (AHRS) is introduced into the construction of a redundant inertial navigation system. As a backup system for an airborne INS, the AHRS exhibits a different device performance. A sequential weighted generalized likelihood ratio test (SWGLT) method, based on a principal component parity vector (PPV), is proposed. The PPV method improves the adaptability of the detection threshold to the inertial sensors’ noise and improves the probability of correct detection. At the same time, the multiscale problem of a heterogeneous redundant system error is solved by sequential weighting, and the false alarm rate is reduced. Simulation experiments show that the proposed method can improve fault detection sensitivity, reduce false alarm rates, and ensure the integrity of civil aircraft navigation systems.

A Systematic Calibration Modeling Method for Redundant INS with Multi-Sensors Non-Orthogonal Configuration.

Because of the non-orthogonal configuration of multi-sensors, the redundant inertial navigation system (INS) has a more complex error model compared with the traditional orthogonal INS, and the complexity of sensors configuration also increases the difficulty of error separation. Based on sufficient analysis of the error principle of redundant IMUs, a generalized high-accuracy calibration modeling method which is suitable for filtering method systematic calibration is summarized in this paper, and it has been applied to an RIMU prototype consisting of four ring laser gyros (RLGs) and four quartz accelerometers. Through the rotational excitation of the three-axis turntable in the laboratory, the high-precision filtering method systematic calibration of the RIMU is achieved, and static navigation and dynamic vehicle test experiments are also carried out. The experimental results reflect that the positioning accuracy can be obviously improved by using this new systematic calibration error model and the validity of this modeling method is also verified.

The calibration method for accelerometers in the redundant MEMS inertial navigation system

Compared with the non-redundant inertial navigation system (INS), the redundant INS (RINS) has more error parameters and the system is more complicated. The calibration methods for non-redundant INS are unable to be adopted by RINS directly. Meanwhile, the inner lever arm effect of accelerometers is more severe in RINS, which is not supposed to be ignored in the error compensation. To solve the problems above, this paper proposes a novel calibration method for accelerometers in RINS. First, the paper analyses and models the bias, scale factor error, installation angle error and lever arm error. Based on the error model, two Kalman filters are designed to estimate the error parameters. The calibration is divided into two steps: the bias, scale factor error and installation angle error are calibrated by the static multi-position experiment first, and then the lever arm error is calibrated by the rotation experiment. Experiments prove that the proposed method can effectively calibrate the deterministic error of the accelerometers, that the estimation errors are controlled at 1 × 10−4 level. Further, the paper studies and optimizes the turntable rotation scheme in the lever arm error calibration experiment, and proposes the design principles for the rotation scheme. Comparing with the casually designed scheme, the optimized procedure can improve the accuracy by almost an order of magnitude as well as the time-consumption being shortened by 40%. The design principles are applicable to the other inertial navigation systems to improve the calibration accuracy and reduces the time cost.

A configuration design method for a redundant inertial navigation system based on diagnosability analysis

Redundant design can improve the performance of an inertial navigation system, and the configuration is an important factor in determining the accuracy and reliability of the redundant inertial navigation system. As well as the sensor quantity and the reliability of single sensors, the reliability of the redundant inertial navigation system also depends on the capability of the fault diagnosis, which has been neglected in much of the previous research. This paper verifies that configurations designed using the previous method cannot maintain optimal performance in fault diagnosis. To optimize the performance in fault detection and isolation through the configuration design, an evaluation function for fault detectability and isolability is proposed based on Kullback–Leibler divergence. On this basis, the inherent diagnosability analysis method is proposed, and the configuration design method is developed with the analysis results. Simulation experiments prove that the proposed diagnosability analysis method can find the defects in the fault detection and isolation, and the diagnosability can be assessed correctly. Configurations designed using the proposed methodensure the optimal accuracy and reliability of the system, and effectively improve the fault diagnosis ability, too.

A Fault-Tolerant Data Fusion Method of MEMS Redundant Gyro System Based on Weighted Distributed Kalman Filtering.

The application of the Micro Electro-mechanical System (MEMS) inertial measurement unit has become a new research hotspot in the field of inertial navigation. In order to solve the problems of the poor accuracy and stability of MEMS sensors, the redundant design is an effective method under the restriction of current technology. The redundant data processing is the most important part in the MEMS redundant inertial navigation system, which includes the processing of abnormal data and the fusion estimation of redundant data. A developed quality index of the MEMS gyro measurement data is designed by the parity vector and the covariance matrix of the distributed Kalman filtering. The weight coefficients of gyros are calculated according to this index. The fault-tolerant fusion estimation of the redundant data is realized through the framework of the distributed Kalman filtering. Simulation experiments are conducted to test the performance of the new method with different types of anomalies.

An Anomaly Diagnosis Method for Redundant Inertial Measurement Unit and Its Application with Micro-Electro-Mechanical System Sensors

The application of the micro-electro-mechanical system inertial measurement unit has become a new research hotspot in the field of inertial navigation. In order to solve the problems of the poor accuracy and stability of micro-electro-mechanical system sensors, redundant design is an effective method under the restriction of current technology. Redundant data processing is the most important part in the micro-electro-mechanical system redundant inertial navigation system, which includes the processing of anomaly data and the fusion estimation of redundant data. To further improve the reliability of the micro-electro-mechanical system redundant inertial measurement unit, an anomaly detection, isolation, and recognition method for data anomalies is proposed. The relationship between the parity space method detection function and the deterioration degree of anomaly data is mathematically deduced. The parity space method detection functions of different anomalies are analyzed, and five indicators are designed to quantitatively analyze the detection function values. According to these indicators, the detection and recognition method are proposed. The new method is tested by a series of simulation experiments.

Homologous fault monitoring technology of redundant INS in airborne avionics systems

Redundant technology plays an important role in improving the reliability and fault-tolerance of the airborne avionics systems.A Markov state transition model is introduced to the reliability analysis of the redundant inertial navigation system (RINS) in airborne navigation systems.An information processing mechanism based on difference filtering is put forward to strengthen the consistency between the outputs of the equal-precision inertia navigation system (INS).On this basis,the homologous fault monitoring algorithm is designed to realize the homologous fault monitoring of RINS.The simulation is carried out based on the above algorithms,and the results verify the effectiveness of the proposed fault monitoring algorithm based on difference filtering.Research results have good reference value for the configuration and design of RINS in airborne integrated avionics systems.

Optimal Sensor Fusion in Redundant Inertial Measurement Unit

In order to improve the reliability and accuracy of inertial navigation system in some navigation applications, the redundant inertial measurement unit (RIMU) is used. In this study, an optimal sensor fusion is introduced, and drift compensation based on this sensor fusion is presented subsequently. And the sensor fusion and drift compensation are combined into one algorithm. In this algorithm, faulty drift sensors are not isolated, but are compensated. So the redundant inertial navigation system can maintain systems redundancy. An errors model of RIMU was derived firstly, the optimal sensor fusion and the drift compensation algorithm were introduced secondly, and several simulations were carried out and proved effective of the proposed algorithm lastly.

Fault Tolerant Control in Redundant Inertial Navigation System

Conventional fault detection and isolation technology cannot fully ensure system redundancy features when sensors experience drift in a redundant inertial navigation system. A new fault tolerant control method employs state estimation and state feedback techniques to compensate the sensor drift. However, the method is sensitive to measurement noise characteristics, and the performance of the method nearly depends on the feedback gain. This paper proposes an improved fault tolerant control algorithm, which employs an adaptive extended Kalman particle filter (AEKPF) to deal with unknown noise characteristics and model inaccuracies. In addition, a drift factor is introduced in the improved fault tolerant controlin order to reduce the dependence of compensation system on the feedback gain. Simulation results show that the improved fault tolerant control algorithm can effectively correct the faulty sensor even when the multiple erroneous sensors are producing faulty outputs simultaneously. Meanwhile, the AEKPF is able to solve the problem of unknown non-Gaussian noise characteristics. Moreover, the feedback gain is significantly improved by the drift factor.

EGNOS-BASED MULTI-SENSOR ACCURATE AND RELIABLE NAVIGATION IN SEARCH-AND-RESCUE MISSIONS WITH UAVS

Abstract. This paper will introduce and describe the goals, concept and overall approach of the European 7th Framework Programme's project named CLOSE-SEARCH, which stands for 'Accurate and safe EGNOS-SoL Navigation for UAV-based low-cost SAR operations'. The goal of CLOSE-SEARCH is to integrate in a helicopter-type unmanned aerial vehicle, a thermal imaging sensor and a multi-sensor navigation system (based on the use of a Barometric Altimeter (BA), a Magnetometer (MAGN), a Redundant Inertial Navigation System (RINS) and an EGNOS-enabled GNSS receiver) with an Autonomous Integrity Monitoring (AIM) capability, to support the search component of Search-And-Rescue operations in remote, difficult-to-access areas and/or in time critical situations. The proposed integration will result in a hardware and software prototype that will demonstrate an end-to-end functionality, that is to fly in patterns over a region of interest (possibly inaccessible) during day or night and also under adverse weather conditions and locate there disaster survivors or lost people through the detection of the body heat. This paper will identify the technical challenges of the proposed approach, from navigating with a BA/MAGN/RINS/GNSS-EGNOSbased integrated system to the interpretation of thermal images for person identification. Moreover, the AIM approach will be described together with the proposed integrity requirements. Finally, this paper will show some results obtained in the project during the first test campaign performed on November 2010. On that day, a prototype was flown in three different missions to assess its high-level performance and to observe some fundamental mission parameters as the optimal flying height and flying speed to enable body recognition. The second test campaign is scheduled for the end of 2011.

Prelaunch Checkout of the Inertial Upper Stage Redundant IMU in the Magellan and Galileo Missions

The Redundant Inertial Measurement Unit (RIMU) is used in the Redundant Inertial Navigation System (RINS) of the Inertial Upper Stage (IUS). Recent IUS missions of critical importance to the space community culminated in successful trajectory insertions of the Magellan and Galileo interplanetary spacecraft. The redundancy features of the strapdown RIMU, combined with a technique for on-pad calibration and sensor consistency checks, provide a comprehensive, self-contained performance-monitoring capability during on-pad, prelaunch checkout. This capability was exploited during launch holds, and recycles of both the Magellan and Galileo missions. The RIMU was maintained in its preliftoff gyrocompass mode of operation with no quiet-time restrictions for extended time periods, thereby allowing a rapid return to launch readiness.