Low-grade Inertial Measurement Unit Research Articles

Rapid Initial Heading Alignment Using Single-Antenna GNSS and a Low-Grade IMU

For the integrated system incorporating a low-cost inertial measurement unit (IMU) and global navigation satellite system (GNSS), rapid and accurate initial heading alignment is still a significant challenge, especially for marine applications in challenging ocean conditions. To overcome this difficulty, we propose an in-motion coarse heading alignment method using single-antenna GNSS and a low-grade IMU based on the basic principle of trajectory similarity. The heading angle is determined by dead reckoning (DR)-derived trajectory and GNSS-derived trajectory using gyro measurements and GNSS positioning solutions. The proposed alignment method can be implemented in real-time and the alignment period is just 1s, which indicates that the method could continuously output heading when the carrier is in motion. Therefore, the continuous heading output calculated by the proposed algorithm can be applied to the state and fault detection of the carrier. Experimental results show that the heading could be determined with an accuracy of 0.48°, 3.14°, and 2.43° at a 95% confidence level for the land vehicle, unmanned surface vessel (USV), and unmanned underwater vehicle (UUV) tests, respectively. Besides, the RMS of the alignment errors calculated by our method reaches 0.20°, 1.42°, and 1.22° for the land vehicle, USV, and UUV tests, respectively.

A Machine Learning Approach for an Improved Inertial Navigation System Solution.

The inertial navigation system (INS) is a basic component to obtain a continuous navigation solution in various applications. The INS suffers from a growing error over time. In particular, its navigation solution depends mainly on the quality and grade of the inertial measurement unit (IMU), which provides the INS with both accelerations and angular rates. However, low-cost small micro-electro-mechanical systems (MEMSs) suffer from huge error sources such as bias, the scale factor, scale factor instability, and highly non-linear noise. Therefore, MEMS-IMU measurements lead to drifts in the solutions when used as a control input to the INS. Accordingly, several approaches have been introduced to model and mitigate the errors associated with the IMU. In this paper, a machine-learning-based adaptive neuro-fuzzy inference system (ML-based-ANFIS) is proposed to leverage the performance of low-grade IMUs in two phases. The first phase was training 50% of the low-grade IMU measurements with a high-end IMU to generate a suitable error model. The second phase involved testing the developed model on the remaining low-grade IMU measurements. A real road trajectory was used to evaluate the performance of the proposed algorithm. The results showed the effectiveness of utilizing the proposed ML-ANFIS algorithm to remove the errors and improve the INS solution compared to the traditional one. An improvement of 70% in the 2D positioning and of 92% in the 2D velocity of the INS solution were attained when the proposed algorithm was applied compared to the traditional INS solution.

Applied system‐level method in calibration validation for personal navigation system in field

A system-level method to validate previous calibration results for low-precision inertial measurement unit (IMU) in personal navigation system (PNS) is proposed. Micro-electro mechanical systems inertial sensors have gained popularity in PNS because they are small, compact and inexpensive. However, the calibration results of these sensors which belong to low-grade IMU will change as time and environment change. It is significant to know whether previous calibration results are reliable for navigation after a long time. However, users have no devices such as turntable to validate calibration results precisely. Therefore, a triple two-position method without any external aiding is presented to validate the bias errors relative to previous calibration results. If the errors are less than the bias range which is a priori information calculated in previous lab test, previous calibration results are available, otherwise the calibration results is invalid. The two-position method based on the strong tracking filter, which takes velocity and azimuth errors as observations, can estimate the bias and scale factor errors of accelerometers and gyros. The experimental results show that the proposed method is effective, compared with the conventional 24-position calibration method on the turntable and the system-level multi-position method usually used for high-grade IMU.

An Innovative Architecture of UTC GPS/INS System with Improved Performance under Severe Jamming

Ultratightly coupled (UTC) architecture is believed to be the best architecture for Global Positioning System (GPS) and Inertial Navigation System (INS) integration system due to the advanced data fusion strategy and effective mutual assistance between the subsystems. However the performance of UTC GPS/INS system will be degraded by severe jamming interference, especially when low-grade inertial measurement unit (IMU) is used. To solve this problem an innovative architecture of UTC GPS/INS system is proposed. Since GPS receiver’s antijamming ability is closely related to tracking loop bandwidth, adaptive tracking loop bandwidth based on the fuzzy logics is proposed to enhance antijamming ability for GPS receiver. The bandwidth will be adapted through a fuzzy logic controller according to the calculated carrier to noise intensity ratio(C/N0). Moreover, fuzzy adaptive integration Kalman filter (IKF) is developed to improve estimation accuracy of IKF when measurement noises change. A simulation platform is established to evaluate the innovative architecture and results demonstrate that the proposed scheme improves navigation performance significantly under severe jamming conditions.

Rate-Gyro-Integral Constraint for Ambiguity Resolution in GNSS Attitude Determination Applications

In the field of Global Navigation Satellite System (GNSS) attitude determination, the constraints usually play a critical role in resolving the unknown ambiguities quickly and correctly. Many constraints such as the baseline length, the geometry of multi-baselines and the horizontal attitude angles have been used extensively to improve the performance of ambiguity resolution. In the GNSS/Inertial Navigation System (INS) integrated attitude determination systems using low grade Inertial Measurement Unit (IMU), the initial heading parameters of the vehicle are usually worked out by the GNSS subsystem instead of by the IMU sensors independently. However, when a rotation occurs, the angle at which vehicle has turned within a short time span can be measured accurately by the IMU. This measurement will be treated as a constraint, namely the rate-gyro-integral constraint, which can aid the GNSS ambiguity resolution. We will use this constraint to filter the candidates in the ambiguity search stage. The ambiguity search space shrinks significantly with this constraint imposed during the rotation, thus it is helpful to speeding up the initialization of attitude parameters under dynamic circumstances. This paper will only study the applications of this new constraint to land vehicles. The impacts of measurement errors on the effect of this new constraint will be assessed for different grades of IMU and current average precision level of GNSS receivers. Simulations and experiments in urban areas have demonstrated the validity and efficacy of the new constraint in aiding GNSS attitude determinations.

An innovative scheme for SINS/GPS ultra-tight integration system with low-grade IMU

In the traditional strapdown inertial navigation system/global positioning system (SINS/GPS) ultra-tight integration structure, the mutual aiding between SINS and GPS forms a positive feedback loop, through which measurement errors of both subsystems are coupled deeply. In signal jamming or/and dynamic conditions, the Doppler aiding error derived from the SINS using low-grade inertial measurement unit (IMU) can increase rapidly, and cause GPS measurement errors to be correlated with the SINS velocity errors. Such correlations can result in poor estimation accuracy of the integration Kalman filter, losing lock of tracking loops or even yielding system instability. To solve this problem, we propose to model tracking errors of the SINS aided phase lock loop and to derive a new tracking-error estimator. Then, an innovative scheme for SINS/GPS ultra-tight integration using low-grade IMU is investigated. Simulations experiments are implemented to verify this innovative scheme under challenging environments.

Observability of Error States in GPS/INS Integration

Observability properties of errors in an integrated navigation system are studied with a control-theoretic approach in this paper. A navigation system with a low-grade inertial measurement unit and an accurate single-antenna Global Positioning System (GPS) measurement system is considered for observability analysis. Uncertainties in attitude, gyro bias, and GPS antenna lever arm were shown to determine unobservable errors in the position, velocity, and accelerometer bias. It was proved that all the errors can be made observable by maneuvering. Acceleration changes improve the estimates of attitude and gyro bias. Changes in angular velocity enhance the lever arm estimate. However, both the motions of translation and constant angular velocity have no influence on the estimation of the lever arm. A covariance simulation with an extended Kalman filter was performed to confirm the observability analysis.

A Car Test for the Estimation of GPS/INS Alignment Errors

Misalignment can be an important error source in the integration of the global positioning system (GPS) and inertial navigation systems. This paper presents car test results on the estimation of alignment errors in the integration of a low-grade inertial measurement unit (IMU) with accurate GPS measurement systems. The car test was conducted with a low-cost solid-state IMU and carrier-phase differential GPS measurement systems. Test results showed that changes in the angular velocity improve the estimation of the lever arm between the GPS antenna and IMU. They also showed that changes in acceleration improve the estimation of the relative attitude between the GPS antenna array and IMU. The lever arm was estimated with a 10-cm error. The relative attitude was estimated with a half-degree error. An iterative scheme was used to improve the estimation of the alignment errors during postprocessing. The scheme was shown to be useful when the test car could not have sufficient changes in motion due to limitations in its path. With the given set of test data, the estimation error decreased as the number of iterations increased.