Initial Position Error Research Articles

Globally exponentially stable single beacon underwater navigation with unknown sound velocity estimation

Single beacon navigation methods with unknown effective sound velocity (ESV) have recently been proposed to solve the performance degeneration induced by ESV setting error. In these methods, a local linearization-based state estimator, which only exhibits local convergence, is adopted to estimate the navigation state. When the initial ESV setting error or vehicle initial position error is large, the local linearization-based state estimators have difficulty guaranteeing the filtering convergence. With this background, this paper proposes a linear time-varying single beacon navigation model with an unknown ESV that can realize global convergence under the condition of system observability. A Kalman filter is adopted to estimate the model state, and the corresponding stochastic model is inferred for the application of the Kalman filter. Numerical simulation confirms that the proposed linear time-varying single beacon navigation model can realize fast convergence in the case of a large initial error, and has superior steady-state performance compared with the existing methods.

X-ray pulsar navigation based on two-stage estimation of Doppler frequency and phase delay

In X-ray pulsar-based navigation (XPNAV), the appearance of Doppler effects caused by the dynamic motion of the spacecraft would severely degrade the navigation accuracy. In this paper, we develop a new XPNAV method based on a two-stage estimation method of Doppler frequency and phase delay to overcome the Doppler effects. Based on the pre-established phase model of the pulsar at the SSB and the predicted states of the spacecraft, the real-time phase model at the spacecraft is modeled with respect to the initial position and velocity errors of the spacecraft. It turns out that within an observation interval, a constant Doppler frequency can be estimated to overcome the effect of the initial velocity error, and then the phase delay caused by the initial position error can be estimated by using the computationally efficient methods based on epoch folding. Under this frame, the H-test for uniformity, which needs no prior knowledge of the light curve, is used to estimate the Doppler frequency and the fast maximum likelihood method is used to estimate the phase delay. The dynamic model and measurement model of the proposed navigation method are also given. Photon-level simulation results obtained by using the ground-based semi-physical simulation system for X-ray pulsar signals show a significant improvement over the traditional XPNAV method.

Error analysis of new 3UPS_UP parallel mechanism

A new mechanism based on composite ball hinge is introduced. Since the structure of composite ball hinge is inconvenient to consider the attitude of moving platform, the ball center is set as the reference point to simplify the kinematics solution. The Jacobian matrix and error analysis model are derived. The effect of the bar length error and the initial position error of the moving platform are analyzed emphatically, and the distribution law of different plane errors is basically consistent, and the bar length error direction has certain influence on the position error. Through simulation analysis and numerical calculation, the rules are analyzed, which lays a foundation for improving the mechanism performance and further research.

자율주행자동차용 3D 라이다의 초기 장착 오차 보정

자율주행자동차용 3D 라이다의 초기 장착 오차 보정

M-M Estimation-Based Robust Cubature Kalman Filter for INS/GPS Integrated Navigation System

In the engineering applications, Kalman filter (KF) is the most commonly used for INS/GPS integration. This KF-based integration is prone to divergence because of two typical problems. One is that the predicted state and measurement are contaminated by non-Gaussian noise. The other is that the initial attitude error is too large. Until now, there arise many algorithms dealing with only one of these two problems, but the algorithms which can deal with both two problems are few. Motived by this situation, an M-M estimation-based cubature Kalman filter (MMCKF) is proposed which innovatively combines the M-M estimation and nonlinear cubature Kalman filter (CKF) to enhance the performance of INS/GPS integration system in the case that above two problems occur at the same time. Simulation and vehicle-mounted experiment results validate its accuracy and robustness.

Smartphone-Based Indoor Positioning Using BLE iBeacon and Reliable Lightweight Fingerprint Map

The introduction of Bluetooth Low Energy (BLE) technology provides new opportunities that the Global Positioning System (GPS) could not provide for indoor localization. In this article, we propose a real-time indoor tracking and positioning system using BLE beacon and smartphone sensors. Up to now, most of the system is using Pedestrian Dead Reckoning (PDR). The initial position is considered to have a high impact on the accuracy of PDR, so, based on the analysis of received signal strength (RSS), we present a method to estimate the approximate distance, then, estimate the initial position through Trilateration technique. Next, we propose a lightweight and reliable fingerprint method. This method addresses two problems: (1) to correct errors due to the initial position error and orbital drift of PDR, (2) to reduce the amount of data, number of reference points and collecting data time. The proposed system is implemented on the smartphone as an application. To verify the accuracy of the system, we performed some experiments. The results show that the system not only achieves high accuracy but also the high performance with average complexity and low cost.

Terminal Recurrent Neural Networks for Time-Varying Reciprocal Solving With Application to Trajectory Planning of Redundant Manipulators

Time-varying matrix reciprocal problems are widely appeared in different matrix computations and engineering fields. Neural networks as a powerful tool have been developed to solve the time-varying problems. Recurrent neural networks (RNNs) are designed considering mainly for two aspects: 1) convergent precision and 2) convergent time. The core part of the existed neural methods is to design various kinds of activation function for time-varying matrix solving. However, most of the activation functions of neural networks are with infinite value, which demands long convergent time and are not applicable in practical engineering fields. This note proposes theoretical analyses and simulation results on the performance of terminal RNN (TRNN) and accelerated TRNN (ATRNN) with finite-time convergence, which is not only designed for constant matrix inversions but also for time-varying reciprocal matrix. Compared to the traditional RNNs, TRNNs are of limit-valued activation function and possess a finite time convergence property. The simulation results for time-varying reciprocal solving validate the perfect performance solved by TRNN and ATRNN. In addition, a quadratic program (QP) of velocity minimization based on TRNN is proposed to solve the trajectory tracking problems without considering the initial position error of the redundant manipulators. Finally, practical experiments of the redundant manipulators based on PUMA560 show the effectiveness and accuracy of the proposed approaches.

Assessment of NCMRWF Global Ensemble System with differing ensemble populations for Tropical cyclone prediction

Ensemble Prediction System (EPS) estimates the probability distribution of atmospheric states through multiple integrations of the Numerical Weather Prediction (NWP) model/s with number of perturbed initial conditions. The choice of an ensemble size can affect the skill of the ensemble forecast. Since more ensemble members require more computational requirements, it is important to determine whether the larger ensemble size significantly improves the forecast uncertainty estimates. The EPS at National Centre for Medium Range Weather Forecasting (NEPS) is assessed for the prediction of track and intensity of Tropical Cyclones (TC) over Bay of Bengal (BoB). Medium range forecast of four tropical cyclones over BoB with three different ensemble sizes namely E11 (11 perturbed members), E22 (22 perturbed members) and E44 (44 perturbed members) are presented here. The results of the study indicate that doubling of the ensemble population reduced the mean initial position errors by 25% and increase of ensemble size by four times (E11 to E44) reduced the errors by about 38%. The ensemble mean forecast track has lower errors compared to the deterministic forecast (CNTL) at shorter lead times for all ensemble sizes. The impact of ensemble size is significant on the improvement of mean track prediction skill at longer lead times. Median of central pressure error is very close to zero for all the sizes and the forecasts are slightly underestimated compared to the observed values. The initial error in Maximum Sustained surface Wind speed (MSW) is smaller for E44 as compared to E22 and E11. Interestingly, in all lead time forecasts and all the ensemble sizes, ensemble mean MSW errors are nearly within ±5 ms−1. There is only slight improvement in the ensemble mean precipitation forecasts with the increase of ensemble size. NEPS fails to resolve the heavier rainfall distribution and has the tendency to over-predict light precipitation. Ensembles with larger size predict the TC strike area of intense storms with higher probabilities due to better consensus among member tracks fueled by improved steering flow. But few cases have shown E11 or E22 perform equally well in forecasting TC propagation and in some cases even better compared to E44. Threshold oriented verification was carried out for winds at 850 hPa considering all the cases collectively. Brier Score (BS) and its components are very close to one another for higher thresholds of winds. In general, E44 has lower BS for all different thresholds with small lead time forecasts. Smaller ensembles have slightly better reliability while large ensembles improved the resolution for lower thresholds of wind speed. Continuous ranked probability score indicate that predicted probability distribution agrees well with the analyzed cumulative distribution. Area under relative operating characteristic curve exhibits better discriminating ability of probabilistic events with larger ensemble sizes. This study clearly showed that an increase in the ensemble size has significant beneficial impact on the reduction of the percentage of outliers thereby improving the potential predictive skill of the unexpected events.

Terrain matching localization for hybrid underwater vehicle in the Challenger Deep of the Mariana Trench

The maximum ocean depth so far reported is about 11 000 m, and is located in the Mariana Trench in the Western Pacific Ocean. The hybrid unmanned underwater vehicle, Haidou, is developed to perform scientific survey at the deepest parts of the Earth oceans. For vehicles working at the full-ocean depth, acoustic positioning is the most effective and popular method. The 11 000 m class acoustic positioning system is relatively massive and complex, and it requires specialized research vessels equipped with compatible acoustic instruments. As a compact testbed platform, it is impractical for Haidou to carry an LBL/USBL beacon with its large volume and weight. During the descent to about 11 000 m, horizontal drift could not be eliminated because of the hydrodynamics and uncertain ocean currents in the sea trials. The maximum depth recorded by Haidou is 10 905 m, and determining the precise location of the deepest point is challenging. With the bathymetric map produced by a multibeam sonar, the terrain contour matching (TERCOM) method is adopted for terrain matching localization. TERCOM is stable in providing an accurate position because of its insensitivity to the initial position errors. The final matching results show the best estimate of location in the reference terrain map.

Velocity-free spacecraft attitude stabilization using two control torques

The attitude stabilization of an inertial-pointing underactuated rigid-body spacecraft using two torques under time-varying disturbances and inertia matrix uncertainties is examined. An observer-based time-varying output feedback control law is proposed where angular velocity measurements are not available. The stability of the combined controller–observer system is proven rigorously to show local ultimate boundedness of all states. A comprehensive numerical example is presented with sampled-data implementation of the observer-based control law to demonstrate convergence even with very large initial attitude errors.

Peg-hole disassembly using active compliance.

When considered in two-dimensional space, a cylindrical peg being withdrawn from a clearance-fit hole can exhibit one of four contact states: no contact, one-point contact, two-point contact and line contact. Jamming and wedging can occur during the two-point contact. Effective control of the two-point contact region can significantly reduce resistance in peg–hole disassembly. In this paper, we explore generic peg–hole disassembly processes with compliance and identify the effects of key parameters including the degree of compliance, the location of the compliance centre and initial position errors. A quasi-static analysis of peg–hole disassembly has been performed to obtain the boundary conditions of the two-point contact region. The effects of key variables on the two-point contact region have been simulated. Finally, peg–hole disassemblies with different locations of compliance centre achieved using active compliance have been experimentally investigated. The proposed theoretical model can be implemented to predict the range and position of the two-point contact region from the perspective of peg–hole disassembly.

A Trapezoidal Velocity Profile Generator for Position Control Using a Feedback Strategy

Position control is usually achieved using a position controller and a profile generator. The profile generator produces a desired position trajectory from a position reference and predefined profiles. The position controller forces the actual position to trace the generated position trajectory. A time-based profile generator is the most famous profile generator due to its capability of generating various profiles. However, time base difference in analysis and implementation causes a steady-state error. In order to remove the steady-state error, this paper proposes a novel profile generator for a trapezoidal velocity profile generation. The proposed generator is based on a cascaded P-PI position controller which is designed to trace the position reference. A dynamic range limiter is adopted to provide the acceleration and velocity restrictions which are basic functions for generating the trapezoidal profile. In spite of these restrictions, it cannot make a desired velocity profile only using the limiter because deceleration point is inaccurate. To adjust the deceleration point, a feedback compensator is designed which requires the velocity of the deceleration point. The velocity of the deceleration point is estimated from the initial position error. The compensator moves the deceleration point to the appropriate point which can generate the desired velocity profile. The proposed profile generator can remove the steady-state error, and the position response can be easily adjusted to be either overdamped or underdamped by selecting the two gains appropriately. Several experimental results are presented to verify the usefulness of the proposed generator.

Adaptive Robust Nonlinear Filtering for Spacecraft Attitude Estimation Based on Additive Quaternion

This paper proposes a new Huber-based and covariance matching-based adaptive robust spacecraft attitude estimation algorithm. By constructing a nonlinear regression model, the measurement noise based on robust filtering is derived. The error function is used to improve the Huber density function, which can enhance the estimation accuracy of the robust algorithm. In addition, the process noise is corrected by introducing a fading factor. The Kalman filter gain derived from the additive quaternion is employed to satisfy the quaternion normalization constraint and avoid the covariance singularity. Simulation results show that the proposed adaptive robust attitude estimation algorithms are more robust than the existing methods under large initial attitude error conditions.

Waveguide-Invariant-Based Ranging and Receiver Localization Using Tonal Sources of Opportunity

Acoustic emissions from cargo ships transiting coastal waterways, measured by a single hydrophone, can be exploited to estimate both the time-varying source–receiver range and receiver location. In this paper, two parameter-search-based maximum-likelihood estimators are presented: one for $\beta$ , the waveguide invariant parameter, and one for source range. $\beta$ characterizes the interference structure inherent to ducted acoustic propagation and is central to the spectral analysis involved in the range estimation. The source ranging method extends prior work by the authors and focuses on the strong narrowband tonals that typically dominate the acoustic spectra of cargo ships. Source ranging results are presented using real data from the SWellEx-96 experiment and are shown to be in close agreement with simulation results obtained using the KRAKEN normal mode program. A technique for estimating the position of a receiver, which can be stationary or moving, is also proposed. The localization technique requires knowledge of the source track, hydrophone data, and an initial estimate of the receiver's position. An application to autonomous underwater vehicle (AUV) navigation is included in which the waveguide-invariant-based range estimates are used to mitigate the position estimation error due to drift in inertial measurement units on submerged AUVs. A specific scenario is examined, using SWellEx-96 data, in which an initial position error of 3 km is reduced to under 1 km using the proposed technique.

Autonomous temperature-based orbit estimation

Orbit estimation (OE) is a required significant task in almost all space missions. Accordingly, a wide variety of sensors and estimation algorithms have been developed within the last few decades to this aim. However, the current study proposes a novel autonomous OE method that is purely based on temperature data of six orthogonal surfaces of a three-axis stabilized satellite as it orbits around the Earth. While the utility of satellite surface temperature data has been recently investigated for satellite attitude estimation (AE) assuming its navigational information, the present paper is focused on OE via only temperature data that has not been attended to in the related literature. To this end, it is assumed that satellite surfaces are equipped with small plates that are thermally isolated from the internal heat sources so that their temperature changes mainly arise from environmental radiation emanated mainly from the Sun and the Earth. In this sense, a thermal model is developed and demonstrated to show how the satellite surface temperatures and their time rates are the only ingredients needed, as measurement quantities, for the proposed OE method to produce the satellite navigational data in terms of its position and velocity vectors. In addition, the effect of sensor configuration on state observability and estimation accuracy is investigated while the unscented Kalman filter (UKF) is exploited in the estimation process. Performance and viability of the proposed temperature-based OE are verified through Monte Carlo simulations and a comprehensive sensitivity analysis over orbital parameters, satellite initial conditions, sensor accuracy and attitude error.

DVL-Aided SINS In-Motion Alignment Filter Based on a Novel Nonlinear Attitude Error Model

A novel nonlinear attitude error model based on square root cubature information filter (SR-CIF) is proposed aiming to speed up the convergence rate of DVL-aided strapdown inertial navigation system (SINS) in-motion initial alignment for autonomous underwater vehicle (AUV) when both large initial heading error and large initial level attitude errors exist The new nonlinear attitude error model and measurement model are applicable for initial alignment after a short-time coarse alignment with large attitude errors exist. The SR-CIF embeds the whole iterative process into the data structure framework of the information filtering, replacing the covariance matrix in the square root cubature Kalman filter (SR-CKF) with an information matrix which simplifies the filter initialization. The square root technology ensures the symmetry and positive definiteness of the information matrix with enhanced stability of the filter. The simulation and experimental results indicate that the proposed DVL-aided alignment filter is effective with large initial attitude errors. The rate of convergence and estimation accuracy of the SR-CIF is higher than that of the conventional SR-CKF with large attitude misalignments.

A Robust Registration Method for Autonomous Driving Pose Estimation in Urban Dynamic Environment Using LiDAR

The registration of point clouds in urban environments faces problems such as dynamic vehicles and pedestrians, changeable road environments, and GPS inaccuracies. The state-of-the-art methodologies have usually combined the dynamic object tracking and/or static feature extraction data into a point cloud towards the solution of these problems. However, there is the occurrence of minor initial position errors due to these methodologies. In this paper, the authors propose a fast and robust registration method that exhibits no need for the detection of any dynamic and/or static objects. This proposed methodology may be able to adapt to higher initial errors. The initial steps of this methodology involved the optimization of the object segmentation under the application of a series of constraints. Based on this algorithm, a novel multi-layer nested RANSAC algorithmic framework is proposed to iteratively update the registration results. The robustness and efficiency of this algorithm is demonstrated on several high dynamic scenes of both short and long time intervals with varying initial offsets. A LiDAR odometry experiment was performed on the KITTI data set and our extracted urban data-set with a high dynamic urban road, and the average of the horizontal position errors was compared to the distance traveled that resulted in 0.45% and 0.55% respectively.

Research on In-Flight Alignment for Micro Inertial Navigation System Based on Changing Acceleration using Exponential Function.

In order to guarantee the stable flight of a guided projectile, it is difficult to realize in-flight alignment for the micro inertial navigation system (MINS) during its short flight time. In this paper, a method based on changing acceleration using exponential function is proposed. First, double-vector observations were derived. Then the initial attitude for the guided projectiles was estimated by the regressive quaternion estimation (QUEST) algorithm. Further, the estimated errors were analyzed, and the reason for using the changing acceleration for the in-flight alignment was explained. A simulation and semi-physical experiment was performed to show the effectiveness of the proposed method. The results showed that the initial attitude error for the rolling angle was about 0.35°, the pitch angle was about 0.1° and the heading angle was about 0.6°, in which the initial shooting angle was between 15° and 55°. In future studies, the field experiments will be carried out to test the stability of the proposed in-flight alignment for guided projectiles.

Gyro-Free Satellite Attitude Determination Based on Special Orthogonal Group

The attitude determination problem of Earth Orientation gyro-less spacecraft is studied in this paper. Considering the sensitivity of the initial attitude error for EKF, a new non linear attitude determination filter is developed by using the Special Orthogonal Group SO (3) which avoids any algebraic reconstruction of the attitude from reference vectors. Lyapunov analysis results for the proposed the filter is derived that ensure global stability of the error. The simulation results show that the estimation of attitude and angular velocity converge to the real in the Earth Orientation mode.

Unscented Particle Smoother and Its Application to Transfer Alignment of Airborne Distributed POS

This paper deals with the problem of state estimation for the transfer alignment of airborne distributed position and orientation system (distributed POS). For a nonlinear system, especially with large initial attitude errors, the performance of linear estimation methods will degrade. In this paper, a nonlinear smoothing algorithm called the unscented particle smoother (UPS) is proposed and utilized in the off-line transfer alignment of airborne distributed POS. In this algorithm, the measurements are first processed by the forward unscented particle filter (UPF) and then a backward smoother is used to achieve the improved solution. The performance of this algorithm is compared with that of a similar smoother known as the unscented Rauch-Tung-Striebel smoother. The simulation results show that the UPS effectively improves the estimation accuracy and this work offers a new off-line transfer alignment approach of distributed POS for multiantenna synthetic aperture radar and other airborne earth observation tasks.