PARITY VECTOR COMPENSATION USING NON-LINEAR FILTERING

Based on the singular value decomposition (SVD) algorithm, this paper proposes the singular value decomposition optimization algorithm for redundant strapdown inertial measurement unit (SIMU) fault detection. Based on the existing singular value decomposition algorithm, the columns of decoupling matrix are unitized. In addition, the decision function for isolation is corrected. Finally, the fault detection of the redundant SIMU regular dodecahedron configuration is simulated. By comparing the simulation results of the SVD algorithm and the SVD optimization algorithm, it proves that the SVD optimization algorithm is the feasible and effective.

This paper addresses application of a fault tolerant control design technique for parameter dependent systems in two phases, namely (1) control law design and (2) sensor and actuator failure detection and isolation and reconfiguring. Traditional solutions (i.e. gain scheduling) to the parameter dependent control problem typically assume slow variation of the system dynamics, which is not necessarily valid for the case of fast parameter variations. The basis of the technique is the casting of the problem in a framework, which is an extension of feedback linearization, to account for the time varying nature of the system. A coordinates transformation, when combined with a linearizing control law including terms accounting for time variation of the parameters, defines a structure for designing the control system for parameter dependent systems with fast parameter variation. The framework is extended to the case of control system failure detection and isolation (FDI) and reconfiguring, thus facilitating design of sensor and actuator failure detection algorithms for parameter dependent systems. The combined control and FDI systems then offer a structured design approach for fault tolerant control of parameter dependent systems. The techniques are demonstrated on (1) a unicycle robot, (2) a Perseus UAV, and (3) a Cessna O2 aircraft.

In order to improve the precision and reliability of the autonomous underwater vehicle (AUV) inertial navigation system, a redundant inertial measurement unit (RIMU) based on micro electromechanical system (MEMS) inertial sensors has been designed, then use support vector machine theory (SVM),construct multi-fault classifier training and combine three-step search parameter optimization method,to achieve rapid, automatic fault detection and isolation (FDI). With Monte Carlo simulation and experimental analysis, SVM method has more obvious advantages than conventional Generalized Likelihood ratio Test (GLT) on false alarm rate, undetected rate and correct isolation rate for common fault sources of RIMU, and can detect and identify the type and number of failure more effectively on redundant systems, and provide a guarantee for fault sensors isolation.

The microelectromechanical system (MEMS) redundant inertial measurement unit (RIMU) is a low-cost navigation system, which can improve reliability effectively. In existing MEMS-RIMU calibration schemes, extra equipment with higher accuracy is required. However, on some occasions, a rotation platform or other reference equipment cannot be provided, and calibration of the MEMS-RIMU is limited. To solve the problem, a novel self-calibration method for the MEMS-RIMU based on gravity vector observation is proposed, which can estimate the bias, scale errors and alignment errors of accelerometers and gyroscopes without extra equipment or rotation platforms. In this paper, a novel calibration model for accelerometers and gyroscopes is built by analyzing the relationship between gravity vector observation and the measurement value of the MEMS-RIMU. There is no attitude information in the calibration model, so the RIMU can be calibrated without attitude references via extra equipment. Then, a derivative unscented Kalman filter is proposed to estimate the bias, scale errors and alignment errors of accelerometers and gyroscopes, which can reduce the computation load. Finally, the simulation and experimental results demonstrate the efficiency and accuracy of the proposed calibration scheme.

In this paper we introduce a method allowing theoretical calculation of linearity and scale factor errors in linear position sensors based on bi-cell photodiodes. Such calculations are applied using three methods to compensate for scale factor temperature drift. An example based on data of a specific sensor was computed to illustrate how the sensor's dimensions and drift compensation method influence the scale factor and linearity errors of its volt-displacement characteristic.

Micro Aerial Vehicles (MAVs) rely on onboard attitude and position sensors for autonomous flight. Due to their size, weight, and power (SWaP) constraints, most modern MAVs use miniaturized inertial measurement units (IMUs) to provide attitude feedback, which is critical for flight stabilization and control. However, recent adversarial attack studies have demonstrated that many commonly used IMUs are vulnerable to attacks exploiting their physical characteristics. Conventional redundancy-based approaches are not effective against such attacks because redundant IMUs have the same or similar physical vulnerabilities. In this paper, we present a novel fault-tolerant solution for IMU compromised scenarios, using separate position and heading information to restore the failed attitude states. Rather than adding more IMU alternatives for recovery, the proposed method is intended to minimize any modifications to the existing system and control program. Thus, it is particularly useful for vehicles that have tight SWaP constraints while requiring simultaneous high performance and safety demands. To execute the recovery logic properly, a robust estimator was designed for fine-grained detection and isolation of the faulty sensors. The effectiveness of the proposed approach was validated on a quadcopter MAV through both simulation and experimental flight tests.

According to the urgent need of high-precision navigation system to future fighters, this paper researches on a dynamic error excitation and calibration method for high-precision IMU. The Kalman filter model including installation errors, scale factor errors and random constant errors of the inertial measurement unit (IMU) as state variables for online identification is established. The relationship between IMU errors and aircraft maneuver manner is derived, and the dynamic flight track to incite IMU errors is designed. Simulation results verify the feasibility of the proposed dynamic error parameter excitation and calibration method, which can incite and identify the IMU error effectively. With the error identification results, the IMU errors are compensated and the precision of the INS is improved significantly.

This paper presents an approach to estimate the attitude of skis for an entire ski jump using wearable, MEMS-based, low-cost Inertial Measurement Units (IMUs). First of all, a kinematic attitude model based on rigid-body dynamics and a sensor error model considering bias and scale factor error are established. Then, an extended Rauch-Tung-Striebel (RTS) smoother is used to combine measurement data provided by both gyroscope and magnetometer to achieve an attitude estimation. Moreover, parameters for the bias and scale factor error in the sensor error model and the initial attitude are determined via a maximum-likelihood principle based parameter estimation algorithm. By implementing this approach, an attitude estimation of skis is achieved without further sensor calibration. Finally, results based on both the simulated reference data and the real experimental measurement data are presented, which proves the practicability and the validity of the proposed approach.

A new dynamic calibration method for IMU deterministic errors of the INS on the Hypersonic Cruise Vehicles

An autonomous method based on the modulation rotation of two inertial measurement units is adopted to address the attitude problem. In this paper, a novel algorithm for processing the measurements of two units during the dynamic motion of an object is proposed to estimate the error components of inertial sensors without additional navigation information including biases, scale factor errors, and misalignment angles. Then a prototype of the attitude and heading reference system (AHRS) consisting of two micromechanical units of the consumer-grade gyro MPU6050 is developed, and its simulation and tests on a turntable are carried out to evaluate the effectiveness of the proposed method. The experimental data have shown that the errors in determining the attitude and heading angles do not exceed 8° during 1 h of autonomous operation. It is concluded that the AHRS designed using the proposed method can improve the accuracy of attitude angles more than five times in comparison with the system including only one MPU6050.

The scalar method of fault diagnosis systems of the inertial measurement unit (IMU) is described. All inertial navigation systems consist of such IMU. The scalar calibration method is a base of the scalar method for quality monitoring and diagnostics. Algorithms of fault diagnosis systems are developed in accordance with scalar calibration method. Algorithm verification is implemented in result of quality monitoring of IMU. A failure element determination is based in diagnostics algorithm verification and after that the reason of such failure is cleared. The process of verifications consists of comparison of the calculated estimations of biases, scale factor errors and misalignments angles of sensors to their data sheet certificate, which kept in internal memory of navigation computer. In result of such comparison the conclusion for working capacity of each one IMU sensor can be made and also the failure sensor can be determined.

EMCORE has demonstrated high-end tactical grade and navigation grade performance of a compact inertial measurement unit (IMU) based on quartz MEMS inertial sensor technology, originally developed by Systron Donner Inertial. Experimental results for the gyroscope reveal angle random walk lower than 0.001 °/$\surd$hr, and bias instability of 0.005 °/hr; bias over a dynamic temperature environment (−55 °C to +90 °C) is <0.2 °/hr and scale factor error is < 50 ppm. Experimental results for the accelerometer have demonstrated a velocity random walk of 0.3 $\mu$g/$\surd$Hz, with bias instability of $0.1 \mu \mathrm{g}$; accelerometer bias and scale factor drift over a full temperature range (−55 °C to +90 °C) were 20 mg and 25 ppm, respectively. This performance was achieved within the form factor of the SDI500 IMU (19 cubic inches, coffee mug size).

GNSS/INS integration is widely used for train positioning, but railways tunnels and mountains can interfere GNSS signals and will lead to performance degradation when the system is operated in the standalone INS mode. This paper proposes a Long Short Term Memory (LSTM)-assisted GNSS/INS integration system using recomputed Inertial Measurement Unit (IMU) error, to suppress the error divergence of an INS in the case of GNSS solution non-availability. The IMU error recomputation method is firstly proposed, where the GNSS/INS-derived position, velocity and attitude (PVA) information is utilized when is GNSS available. The train's attitude computed using the GNSS dual-antenna moving baseline method is used as the heading constraint for GNSS/INS integration so as to provide accurate attitude information. The recomputed IMU sensor error is then used for model training, and the system switches to LSTM-assisted INS mode when GNSS solutions are unavailable. The system predicts the IMU sensor error using the train motion state, and corrects the IMU measurements to suppress the accumulating IMU sensor error.The proposed system was evaluated through a train experiment on the Shuozhou-Huanghua railway. The IMU error Recomputation Method (IMU-RM) was evaluated on four time slots of varying lengths, and the proposed LSTM-assisted GNSS/INS integration system using IMU-RM was evaluated in two “difficult” GNSS signal areas, of curved and straight railtrack segments, were simulated. Results showed significant improvement in horizontal position accuracy compared to conventional methods, with suppression of INS sensor error divergence by 79% and 63% for curved and straight segments, respectively.

In a dual-axis rotational inertial navigation system (RINS), there are two kinds of installation errors, nonorthogonal installation errors of inertial sensors, and installation errors between the inertial measurement unit (IMU) and rotation axes. Traditionally, these two errors are not considered simultaneously. Thus, they are calibrated separately by different estimation algorithms and rotation schemes. In this paper, a system-level self-calibration method for installation errors of a dual-axis RINS is proposed. Based on the Kalman filter, the measurement model is reestablished to ensure that all installation errors can be estimated together. First, the relationship between the initial attitude and subsequent attitude of IMU during rotation is used as a constraint to estimate nonorthogonal installation errors of accelerometers, and installation errors between the IMU and rotation axes. Then, the angular rate of the rotation mechanism is used as a reference to estimate nonorthogonal installation errors of the gyros. The rotation scheme of the IMU is designed to make all installation errors observable, and the observability of the system is analyzed based on the piecewise constant system method. Simulation and laboratory experiment results suggest that installation errors can be effectively estimated by the proposed method, thereby avoiding the complex separating process.

In this paper, a mean value test (MVT) is proposed to detect and isolate failures. Non-linear filtering is used to estimate the combinations of sensor errors affecting the parity vector, the estimates are used to form a compensated parity vector. The compensated parity vector is used to make an FDI decision for improving FDI performance. To illustrate its application, the MVT approach is used to detect and isolate the failure in an optimal mounting configuration of five single-degree-of-freedom sensors. Some results are obtained by comparing the MVT method with the generalized likelihood test (GLT) method. Simulation results prove that MVT method can detect and isolate the failure effectively when two sensors fail simultaneously.