Navigation-grade Inertial Measurement Unit Research Articles

LCNS Positioning of a Lunar Surface Rover Using a DEM-Based Altitude Constraint

With the renewed interest in lunar surface exploration, the European Space Agency envisions to stimulate the creation of lunar communications and navigation services (LCNS) to enable, among others, autonomous navigation capabilities for lunar rovers. As the number of satellites foreseen in such a service is much smaller compared to Earth-based global navigation satellite systems, different complementary technologies are pursued to improve the attainable navigation accuracy for lunar rovers. One way to improve the position accuracy provided by the LCNS satellites is to constrain their vertical position using a high resolution digital elevation model (DEM). This article presents the results of a variance covariance analysis of an extended Kalman filter implementation in which the LCNS ranging measurements are used together with the altitude provided by a DEM from the Lunar Orbiter Laser Altimeter instrument of the Lunar Reconnaissance Orbiter. Assuming a realistic orbit determination and time synchronization accuracy of the LCNS satellites, the usage of a navigation-grade inertial measurement unit and an oven-controlled crystal oscillator, a 3-sigma position accuracy of less than 10 m can be obtained. Furthermore, the availability is substantially improved as the DEM-aided solution enables a position solution in case of only 3 visible satellites.

SINS/DVL Integrated System With Current and Misalignment Estimation for Midwater Navigation

Motivated by the problem that water-track Doppler Velocity Log (DVL) cannot effectively suppress the error accumulation of strap-down inertial navigation system (SINS), this paper proposes a novel SINS/DVL integrated navigation algorithm for deep and long cruising range Human Occupied Vehicle (HOV). Such algorithm decomposes the navigation process into two tightly coupled working modes: alignment mode and navigation mode. In the alignment mode, HOV is controlled to perform horizontal circular motion for several minutes to realize Self-Aided SINS. Combining the precise navigation solutions provided by Self-Aided SINS and measurements from DVL with water track, recursive least square (RLS) algorithm is adopted to estimate heading misalignment angle between SINS and DVL and horizontal ocean current velocity. In the navigation mode, both horizontal ocean current velocity obtained in the alignment mode and DVL measurements are utilized to assist SINS, thus enabling SINS/DVL/Current integrated navigation. A square trajectory with a navigation-grade inertial measurement unit (IMU) is simulated to evaluate the proposed SINS/DVL integrated navigation algorithm. Simulation results show that the proposed SINS/DVL integrated navigation is capable of suppressing SINS error divergence effectively and efficiently. In addition, the feasibility and effectiveness of Self-Aided SINS based on horizontal circular motion is also verified by field test with real IMU data.

Performance of miniaturized atomic clocks in static laboratory and dynamic flight environments

Miniaturized atomic clocks with high frequency stability as local oscillators in global navigation satellite system (GNSS) receivers promise to improve real-time kinematic applications. For a number of years, such oscillators are being investigated regarding their overall technical applicability, i.e., transportability, and performance in dynamic environments. The short-term frequency stability of these clocks is usually specified by the manufacturer, being valid for stationary applications. Since the performance of most oscillators is likely degraded in dynamic conditions, various oscillators are tested to find the limits of receiver clock modeling in dynamic cases and consequently derive adequate stochastic models to be used in navigation. We present the performance of three different oscillators (Microsemi MAC SA.35m, Spectratime LCR-900 and Stanford Research Systems SC10) for static and dynamic applications. For the static case, all three oscillators are characterized in terms of their frequency stability at Physikalisch-Technische Bundesanstalt, Germany's national metrology institute. The resulting Allan deviations agree well with the manufacturer's data. Furthermore, a flight experiment was conducted in order to evaluate the performance of the oscillators under dynamic conditions. Here, each oscillator is replacing the internal oscillator of a geodetic-grade GNSS receiver and the stability of the receiver clock biases is determined. The time and frequency offsets of the oscillators are characterized with regard to the flight dynamics recorded by a navigation-grade inertial measurement unit. The results of the experiment show that the frequency stability of each oscillator is degraded by about at least one order of magnitude compared to the static case. Also, the two quartz oscillators show a significant g-sensitivity resulting in frequency shifts of − 1.2 × 10−9 and + 1.5 × 10−9, respectively, while the rubidium clocks are less sensitive, thus enabling receiver clock modeling and strengthening of the navigation performance even in high dynamics.

Impact of temperature stabilization on the strapdown airborne gravimetry: a case study in Central Turkey

Airborne gravimetry with strapdown inertial sensors has been a valuable tool for many years to fill in the gravity data gaps on the areas not accessible by land. Accuracies of 1 mGal level with off-the-shelf navigation-grade inertial measurement units (IMU) can only be achieved provided that the accelerometer drifts mainly caused by the temperature variations inside the IMU housing are separated from the gravity signal. Although there are several strategies proposed in the literature to deal with this inseparability problem, we use a thermal stabilization system (iTempStab) added on an iNAT-RQH navigation-grade IMU and investigate its performance over a test region in central Turkey with moderate topography and highly qualified ground truth gravity data. Two test flights were performed in 2017 and 2018 with and without iTempStab add-on following almost the same flight trajectories. During the first flight in 2017 with iNAT-RQH only, which lasted almost 5.5 h, there were considerable temperature variations inside the IMU housing from 39.1 to 46.0 °C. A simple thermal correction based on a laboratory calibration done before the flight was applied to the vertical Z-accelerometer in the pre-processing stage. However, temperature changes were within 0.1 °C during the second test flight in 2018 with TempStab add-on. The temperature stabilization gained by the iTempStab add-on produced better cross-over statistics. While the RMSE of the non-adjusted cross-over residuals was about 2.6 mGal, it reduced by 50% with iTempStab add-on. The adjusted cross-over differences of the 2018 flight yielded an RMSE of about 0.5 mGal, which is a remarkable precision for the strapdown gravimetry. The comparison with upward continued ground gravity data at flight altitudes suggests that the thermal stabilization system shows also remarkable improvements in the residual statistics. The range of the residuals decreases from ± 10 to ± 5 mGal, the standard deviation decreases from 2.19 to 0.94 mGal, and the RMSE decreases from 2.24 to 1.48 mGal, respectively, with the iTempStab add-on. It can be concluded that the thermal stabilization system significantly improves the accelerometer stability and therefore the precision and accuracy of the strapdown airborne gravity estimates.

Consistent ST-EKF for Long Distance Land Vehicle Navigation Based on SINS/OD Integration

This paper presents detailed derivations and further explanations of the state transformation extended Kalman filter (ST-EKF) from the common frame error definition perspective. Both the system error and measurement models of strapdown inertial navigation system (SINS)/Odometer (OD) tightly-coupled navigation are derived based on the ST-EKF. Both theoretical analysis and test results indicate the effectiveness of the proposed velocity error definition on mitigating the covariance-inconsistency problem that is caused by the specific force calculation error in the EKF state transition matrix. The excellent covariance-consistency of the ST-EKF makes it not necessary to remove gyro and accelerometer bias errors from the initial alignment Kalman filter states. This phenomenon can ensure the subsequent integrated navigation performance. Single-position ground SINS alignment experiments by using a navigation grade inertial measurement unit (IMU) showed that the estimated yaw angle from the conventional 15-state EKF slowly diverged over time. Furthermore, this phenomenon became more distinct when the initial yaw angle error was larger. In contrast, the estimated yaw angle from the proposed 15-state ST-EKF was significantly more stable and less degraded by large initial yaw angle errors. Long- distance land-vehicle SINS/OD integrated navigation tests also exhibited the ST-EKF's higher positioning and heading accuracy than the EKF.

125. The use of combinatorial supervised machine learning and wearable sensors to objectively measure activities of daily living from the Oswestry Disability Index

BACKGROUND CONTEXT Disease specific health status is measured using survey-based patient-reported outcomes (PROs) (eg, Oswestry Disability Index, ODI) including questions about walking, sitting, standing, traveling (driving) and sleeping (lying down). However, survey data are difficult to collect routinely and subject to multiple biases. Multiple sensor and rules-based algorithms have shown positive predictive values (PPV) greater than 85% for key functional metrics such as walking, jogging, sitting and lying down in a laboratory setting, suggesting the potential to assess measured function rather than reported function. The purpose of this study is to test the predictive capability of a single wearable sensor containing a high-end inertial measurement unit (IMU) for core elements of the ODI survey in a real-world setting. PURPOSE To determine the feasibility of using a single wearable sensor in a real-world environment to measure a subset of activities in the ODI. STUDY DESIGN/SETTING Technology feasibility. PATIENT SAMPLE Five healthy volunteers willing to wear a sensor and undergo a 60-minute activity protocol. OUTCOME MEASURES Sensor generated predictions of activity measured at the 5 ms boundary. METHODS Training data were generated from five healthy volunteers wearing a single navigation-grade IMU (SBG Systems Ellipse2-N) for 60 minutes in a real-world environment. Volunteers were asked to sit, stand, lie supine, drive and walk in an unscripted manner. Data were continuously recorded at 200Hz. A combinatorial learning approach utilized multinomial logistic regression (MLR) to classify posture and sleeping results before feeding into a layer of support-vector-machines (SVM). Twenty percent of the data were held out for testing. RESULTS A total of 1,989,878 data points were analyzed from five healthy volunteers. After the two layers (MLR followed by SVM), accuracy was found to be 98.6% across all activities. Positive predictive values were 98.4% for walking, 97.8% for sitting, 98.5% for driving, 100% for lying and 97.9% for standing. CONCLUSIONS The use of a high-end IMU combined with machine learning algorithms to classify a subset of ODI activities in a real-world setting achieved greater accuracy than multiple sensors and rules-based algorithms reported in prior studies. This type of technology offers the potential for measured function to replace or supplement patient-reported function for key patient-centric activities routinely considered reflective of health status. FDA DEVICE/DRUG STATUS This abstract does not discuss or include any applicable devices or drugs.

Optimized Multi-Position Calibration Method with Nonlinear Scale Factor for Inertial Measurement Units.

Navigation grade inertial measurement units (IMUs) should be calibrated after Inertial Navigation Systems (INSs) are assembled and be re-calibrated after certain periods of time. The multi-position calibration methods with advantage of not requiring high-precision equipment are widely discussed. However, the existing multi-position calibration methods for IMU are based on the model of linear scale factors. To improve the precision of INS, the nonlinear scale factors should be calibrated accurately. This paper proposes an optimized multi-position calibration method with nonlinear scale factor for IMU, and the optimal calibration motion of IMU has been designed based on the analysis of sensitivity of the cost function to the calibration parameters. Besides, in order to improve the accuracy and robustness of the optimization, an estimation method on initial values is presented to solve the problem of setting initial values for iterative methods. Simulations and experiments show that the proposed method outperforms the calibration method without nonlinear scale factors. The navigation accuracy of INS can be improved by up to 17% in lab conditions and 12% in the moving vehicle experiment, respectively.

A Feasibility Analysis of Land-Based SINS/GNSS Gravimetry for Groundwater Resource Detection in Taiwan.

The integration of the Strapdown Inertial Navigation System and Global Navigation Satellite System (SINS/GNSS) has been implemented for land-based gravimetry and has been proven to perform well in estimating gravity. Based on the mGal-level gravimetry results, this research aims to construct and develop a land-based SINS/GNSS gravimetry device containing a navigation-grade Inertial Measurement Unit. This research also presents a feasibility analysis for groundwater resource detection. A preliminary comparison of the kinematic velocities and accelerations using multi-combination of GNSS data including Global Positioning System, Global Navigation Satellite System, and BeiDou Navigation Satellite System, indicates that three-system observations performed better than two-system data in the computation. A comparison of gravity derived from SINS/GNSS and measured using a relative gravimeter also shows that both agree reasonably well with a mean difference of 2.30 mGal. The mean difference between repeat measurements of gravity disturbance using SINS/GNSS is 2.46 mGal with a standard deviation of 1.32 mGal. The gravity variation because of the groundwater at Pingtung Plain, Taiwan could reach 2.72 mGal. Hence, the developed land-based SINS/GNSS gravimetry can sufficiently and effectively detect groundwater resources.

Drift reduction in strapdown airborne gravimetry using a simple thermal correction

Previous work has shown, that strapdown airborne gravimeters can have a comparable or even superior performance in the higher frequency domain (resolution of few kilometres), compared to classical stable-platform air gravimeters using springs, such as the LaCoste and Romberg (LCR) S-gravimeter. However, the longer wavelengths (tens of kilometres and more) usually suffer from drifts of the accelerometers of the strapdown inertial measurement unit (IMU). In this paper, we analyse the drift characteristics of the QA2000 accelerometers, which are the most widely used navigation-grade IMU accelerometers. A large portion of these drifts is shown to come from thermal effects. A lab calibration procedure is used to derive a thermal correction, which is then applied to data from 18 out of 19 flights from an airborne gravity campaign carried out in Chile in October 2013. The IMU-derived gravity closure error can be reduced by 91 % on average, from 3.72 mGal/h to only 0.33 mGal/h (RMS), which is an excellent long-term performance for strapdown gravimetry. Also, the IMU results are compared to the LCR S-gravimeter, which is known to have an excellent long-term stability. Again, the thermal correction yields a significant reduction of errors, with IMU and LCR aerogravity results being consistent at the 2 mGal level.

Accelerometer calibration with nonlinear scale factor based on multi-position observation

The calibration of an inertial measurement unit (IMU) is a key technique to improve the accuracy of an inertial navigation system. Adding more parameters into the model and reducing the estimation errors is essential for improving the calibration methods. Given its advantage of not requiring high-precision equipment, the multi-position calibration method has been widely discussed and has shown great potential in recent years. In this paper, the multi-position calibration method is improved by introducing the accelerometer nonlinear scale factor. The observation equations for the improved multi-position calibration method are established based on a nonlinear accelerometer model. The particle swarm optimization algorithm is adopted to solve the complicated nonlinear equations. In addition, Allan variance is used to determine the optimal data collection time. The accuracy and the robustness of the proposed calibration method are verified by the simulation test. The laboratory and field experiment results for a navigation-grade IMU prove that the proposed method can successfully identify the accelerometer nonlinear scale factor and improve the multi-position calibration accuracy. The comparison of several other calibration methods highlights the superior performance of the proposed method without precise orientation control.

Fiber-based rotary strapdown inertial navigation system

Navigation involves the integration of methodologies and systems for estimating the time-varying attitude of moving objects. A fiber strapdown inertial navigation system (FSINS) is presently used in several applications related to vehicle navigation. However, the absolute attitude and position from FSINS contain an error that increases with time. In order to improve the performance of FSINS based on our present inertial sensors, the auto-compensation of inertial sensor bias in rotation error modulation was proposed. The aim is to develop a rotary FSINS, in which the significant sensor bias is automatically compensated by rotating the inertial measurement unit (IMU), to offer the comparable navigation performance to navigation-grade IMU. In the proposed rotational technology, the IMU is rotated back and forth in azimuth through four orthogonal positions relative to the vehicle’s longitudinal axis. Simulation and experimental testing are conducted for the prototype, and the results showed that the rotary FSINS’s navigation performance is improved.

Strapdown INS/DGPS airborne gravimetry tests in the Gulf of Mexico

Combining data from a Strapdown Inertial Navigation System and a Differential Global Positioning System (SINS/DGPS) has shown great promise in estimating gravity on moving platforms. Previous studies on a ground-vehicle system obtained 1–3 mGal precision with 2 km spatial resolution. High-accuracy Inertial Measurement Units (IMU) and cm-level positioning solutions are very important in obtaining mGal-level gravity disturbance estimates. However, these ideal configurations are not always available or achievable. Because the noise level in the SINS/DGPS gravimetric system generally decreases with an increase of speed and altitude of the platform, the stringent constraints on the IMU and GPS may be relieved in the airborne scenario. This paper presents an investigation of one navigation-grade and one tactical-grade IMU for the possibility of low-cost INS/GPS airborne gravimetry. We use the data collected during the Gravity-Lidar Study of 2006 (GLS06), which contains aerogravity, GPS, and INS along the northern coastline of the Gulf of Mexico. The gravity disturbance estimates from the navigation-grade IMU show 0.5–3.2 mGal precision compared with the onboard gravimeter’s measurements and better than 3 mGal precision compared with the upward continued surface control data. Due to relatively large (240 s) smoothing window, the results have about 34 km along-track resolution. But the gravity estimates from the tactical-grade IMU have much poorer precisions. Nonetheless, useful contributions from the tactical-grade IMU could be extracted for longer wavelengths.

Modeling of Sensor Error Equations for GPS/INS Hybrid Systems

In this paper, we develop identication methods for IMU (Inertial Measure- ment Unit) sensor error models in the GPS/INS hybrid systems. GPS provides high- accuracy position, velocity. The main factor limiting the use of GPS is the requirement for line-of-sight between the receiver antenna and the satellites. On the other hand, an Inertial Navigation System (INS) provides position, velocity and attitude autonomously at a rate of several tens of Hz. However, its errors are accumulated owing to drift of IMU. In order to overcome the inherent drawbacks of each system, integrated GPS/INS systems have been developed. In this paper, for more accurate positioning, we develop the identication methods for IMU sensor error models in the hybrid navigation system. In the hybrid navigation for keeping accurate positioning, GPS/IMU coupled methods andltering techniques have being investigated for past two decades. IMU sensor errors models (bias, scale factor and noise) are assumed as stochastic models such as Gauss Markov (GM) model. For most navigation-grade IMU such as ring laser gyro (RLG), 1st order Gauss-Markov models are usually used for the hybrid navigation. This is also true for low-cost IMU sensors such asber optical gyro (FOG) and micro electro mechanical systems (MEMS) although sometimes a random walk process is utilized instead. In this paper, we discuss IMU stochastic error modeling applying autoregressive (AR) models of orders higher than one (2). Namely, the IMU sensor errors are modeled as the high- order vector autoregressive (V AR) models. The best order of AR models is determined by Akaike's information criterion (1). First, we discuss nonlinearltering construct and In-Motion alignment methods to estimate the initial attitude and heading of INS. Next, we examine sensor error model and sensor error state equation. Based on the above method, we show the experimental results under considering the static situation. The sensor error models applying V AR models execute the decreasing of error factor of AR

Benefits of Using a Tactical-Grade IMU for High-Accuracy Positioning

: Integration of GPS with inertial sensors can provide many benefits for navigation, from improved accuracy to increased reliability. The extent of such benefits, however, is typically a function of the quality of the inertial system used. Traditionally, high-cost, navigation-grade inertial measurement units (IMUs) have been used to obtain the highest position and velocity accuracies. However, the work documented in this paper uses a Honeywell HG-1700 IMU (1 deg/h) to assess the benefits of a tactical-grade IMU in aiding GPS for high-accuracy (centimeter-level) applications. To this end, the position and velocity accuracy of the integrated system during complete and partial GPS data outages is investigated. The benefit of using inertial data to improve the ambiguity resolution process after such data outages is also addressed in detail. Centralized and decentralized filtering strategies are compared in terms of system performance.