scholarly journals Position and Attitude Estimation Method Integrating Visual Odometer and GPS

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2121 ◽  
Author(s):  
Yu Yang ◽  
Qiang Shen ◽  
Jie Li ◽  
Zilong Deng ◽  
Hanyu Wang ◽  
...  
Keyword(s):  
Estimation Error ◽  
Estimation Method ◽  
Attitude Estimation ◽  
Estimation Methods ◽  
Hardware In The Loop ◽  
Trajectory Estimation ◽  
Gps Positioning ◽  
Visual Odometer ◽  
State Of Art ◽  
Better Than

The monocular visual odometer is widely used in the navigation of robots and vehicles, but it has defects of the unknown scale of the estimated trajectory. In this paper, we presented a position and attitude estimation method, integrating the visual odometer and Global Position System (GPS), where the GPS positioning results were taken as a reference to minimize the trajectory estimation error of visual odometer and derive the attitude of the vehicle. Hardware-in-the-loop simulations were carried out; the experimental results showed that the positioning error of the proposed method was less than 1 m, and the accuracy and robustness of the attitude estimation results were better than those of the state-of-art vision-based attitude estimation methods.

scholarly journals Estimating fusion properties for functionalised acids

2011 ◽  
Vol 11 (16) ◽  
pp. 8385-8394 ◽  
Author(s):  
S. Compernolle ◽  
K. Ceulemans ◽  
J.-F. Müller
Keyword(s):  
Vapour Pressure ◽  
Estimation Error ◽  
Estimation Method ◽  
Organic Aerosol ◽  
Estimation Methods ◽  
Fusion Temperature ◽  
Subcooled Liquid ◽  
Fusion Data ◽  
Simple Estimation ◽  
Data Estimation

Abstract. Multicomponent organic aerosol (OA) is likely to be liquid, or partially liquid. Hence, to describe the partitioning of these components, their liquid vapour pressure is desired. Functionalised acids (e.g. diacids) can be a significant part of OA. But often measurements are available only for solid state vapour pressure, which can differ by orders of magnitude from their liquid counterparts. To convert such a sublimation pressure to a subcooled liquid vapour pressure, fusion properties (two out of these three quantities: fusion enthalpy, fusion entropy, fusion temperature) are required. Unfortunately, experimental knowledge of fusion properties is sometimes missing in part or completely, hence an estimation method is required. Several fusion data estimation methods are tested here against experimental data of functionalised acids, and a simple estimation method is developed, specifically for this family of compounds, with a significantly smaller estimation error than the literature methods.

scholarly journals Two-hop distance estimation in wireless sensor networks

2017 ◽  
Vol 13 (2) ◽  
pp. 155014771668968 ◽  
Author(s):  
Sunyong Kim ◽  
Sun Young Park ◽  
Daehoon Kwon ◽  
Jaehyun Ham ◽  
Young-Bae Ko ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Estimation Error ◽  
Estimation Method ◽  
Distance Estimation ◽  
Wireless Sensor ◽  
Estimation Methods ◽  
Accurate Estimation ◽  
Distance Information ◽  
Wide Range

In wireless sensor networks, the accurate estimation of distances between sensor nodes is essential. In addition to the distance information available for immediate neighbors within a sensing range, the distance estimation of two-hop neighbors can be exploited in various wireless sensor network applications such as sensor localization, robust data transfer against hidden terminals, and geographic greedy routing. In this article, we propose a two-hop distance estimation method, which first obtains the region in which the two-hop neighbor nodes possibly exist and then takes the average of the distances to the points in that region. The improvement in the estimation accuracy achieved by the proposed method is analyzed in comparison with a simple summation method that adds two single-hop distances as an estimate of a two-hop distance. Numerical simulation results show that in comparison with other existing distance estimation methods, the proposed method significantly reduces the distance estimation error over a wide range of node densities.

Lower Bound on the Accuracy of Parameter Estimation Methods for Linear Sensorimotor Synchronization Models

2015 ◽  
Vol 3 (1-2) ◽  
pp. 32-51 ◽  
Author(s):  
Nori Jacoby ◽  
Peter E. Keller ◽  
Bruno H. Repp ◽  
Merav Ahissar ◽  
Naftali Tishby
Keyword(s):  
Parameter Estimation ◽  
Lower Bound ◽  
Computational Models ◽  
Linear Models ◽  
Estimation Error ◽  
Explanatory Power ◽  
Estimation Method ◽  
Sensorimotor Synchronization ◽  
Estimation Methods ◽  
Unbiased Estimation

The mechanisms that support sensorimotor synchronization — that is, the temporal coordination of movement with an external rhythm — are often investigated using linear computational models. The main method used for estimating the parameters of this type of model was established in the seminal work of Vorberg and Schulze (2002), and is based on fitting the model to the observed auto-covariance function of asynchronies between movements and pacing events. Vorberg and Schulze also identified the problem of parameter interdependence, namely, that different sets of parameters might yield almost identical fits, and therefore the estimation method cannot determine the parameters uniquely. This problem results in a large estimation error and bias, thereby limiting the explanatory power of existing linear models of sensorimotor synchronization. We present a mathematical analysis of the parameter interdependence problem. By applying the Cramér–Rao lower bound, a general lower bound limiting the accuracy of any parameter estimation procedure, we prove that the mathematical structure of the linear models used in the literature determines that this problem cannot be resolved by any unbiased estimation method without adopting further assumptions. We then show that adding a simple and empirically justified constraint on the parameter space — assuming a relationship between the variances of the noise terms in the model — resolves the problem. In a follow-up paper in this volume, we present a novel estimation technique that uses this constraint in conjunction with matrix algebra to reliably estimate the parameters of almost all linear models used in the literature.

Attitude Estimation with Complementary Filter

2010 ◽  
Vol 44-47 ◽  
pp. 3781-3784
Author(s):  
Rui Hua Chang ◽  
Xiao Dong Mu ◽  
Xiao Wei Shen
Keyword(s):  
Solid State ◽  
Real Time ◽  
Inertial Sensors ◽  
Low Cost ◽  
Estimation Error ◽  
Estimation Method ◽  
Attitude Estimation ◽  
Complementary Filter

An attitude estimation method is presented for a robot using low-cost solid-state inertial sensors. The attitude estimates are obtained from a complementary filter by combining the measurements from the integration of a tri-axis gyro and an aiding system mechanized using a tri-axis accelerometer and a tri-axis magnetometer. The results show that the estimation error is less than 1 degree compare to the reference attitude. It is a simple, yet effective method for attitude estimation, suitable for real-time implementation on a robot.

scholarly journals Estimating Surgical Blood Loss Volume Using Continuously Monitored Vital Signs

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6558
Author(s):  
Yang Chen ◽  
Chengcheng Hong ◽  
Michael R. Pinsky ◽  
Ting Ma ◽  
Gilles Clermont
Keyword(s):  
Blood Loss ◽  
Early Stage ◽  
Estimation Error ◽  
Estimation Method ◽  
Vital Signs ◽  
Estimation Methods ◽  
Clinical Implementation ◽  
Arterial Blood ◽  
Blood Loss Volume ◽  
Estimation Models

Background: There are currently no effective and accurate blood loss volume (BLV) estimation methods that can be implemented in operating rooms. To improve the accuracy and reliability of BLV estimation and facilitate clinical implementation, we propose a novel estimation method using continuously monitored photoplethysmography (PPG) and invasive arterial blood pressure (ABP). Methods: Forty anesthetized York Pigs (31.82 ± 3.52 kg) underwent a controlled hemorrhage at 20 mL/min until shock development was included. Machine-learning-based BLV estimation models were proposed and tested on normalized features derived by vital signs. Results: The results showed that the mean ± standard deviation (SD) for estimating BLV against the reference BLV of our proposed random-forest-derived BLV estimation models using PPG and ABP features, as well as the combination of ABP and PPG features, were 11.9 ± 156.2, 6.5 ± 161.5, and 7.0 ± 139.4 mL, respectively. Compared with traditional hematocrit computation formulas (estimation error: 102.1 ± 313.5 mL), our proposed models outperformed by nearly 200 mL in SD. Conclusion: This is the first attempt at predicting quantitative BLV from noninvasive measurements. Normalized PPG features are superior to ABP in accurately estimating early-stage BLV, and normalized invasive ABP features could enhance model performance in the event of a massive BLV.

scholarly journals Estimating trajectories of meteors: an observational Monte Carlo approach – I. Theory

2019 ◽  
Vol 491 (2) ◽  
pp. 2688-2705 ◽  
Author(s):  
Denis Vida ◽  
Peter S Gural ◽  
Peter G Brown ◽  
Margaret Campbell-Brown ◽  
Paul Wiegert
Keyword(s):  
High Precision ◽  
Estimation Method ◽  
Optical Measurements ◽  
Estimation Methods ◽  
Comparative Performance ◽  
Trajectory Estimation ◽  
Wide Range ◽  
Solution Methods ◽  
Optimization Function ◽  
Systematic Biases

ABSTRACT It has recently been shown by Egal et al. that some types of existing meteor in-atmosphere trajectory estimation methods may be less accurate than others, particularly when applied to high-precision optical measurements. The comparative performance of trajectory solution methods has previously only been examined for a small number of cases. Besides the radiant, orbital accuracy depends on the estimation of pre-atmosphere velocities, which have both random and systematic biases. Thus, it is critical to understand the uncertainty in velocity measurement inherent to each trajectory estimation method. In this first of a series of two papers, we introduce a novel meteor trajectory estimation method that uses the observed dynamics of meteors across stations as a global optimization function and that does not require either a theoretical or an empirical flight model to solve for velocity. We also develop a 3D observational meteor trajectory simulator that uses a meteor ablation model to replicate the dynamics of meteoroid flight, as a means to validate different trajectory solvers. We both test this new method and compare it to other methods, using synthetic meteors from three major showers spanning a wide range of velocities and geometries (Draconids, Geminids, and Perseids). We determine which meteor trajectory solving algorithm performs better for all-sky, moderate field-of-view, and high-precision narrow-field optical meteor detection systems. The results are presented in the second paper in this series. Finally, we give detailed equations for estimating meteor trajectories and analytically computing meteoroid orbits, and provide the python code of the methodology as open-source software.

scholarly journals A Sparse-Based Off-Grid DOA Estimation Method for Coprime Arrays

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3025 ◽  
Author(s):  
Weijian Si ◽  
Fuhong Zeng ◽  
Changbo Hou ◽  
Zhanli Peng
Keyword(s):  
Covariance Matrix ◽  
Estimation Error ◽  
Estimation Method ◽  
Doa Estimation ◽  
Sample Covariance Matrix ◽  
Estimation Methods ◽  
Matrix Estimation ◽  
Estimation Process ◽  
Sample Covariance ◽  
Coprime Arrays

Recently, many sparse-based direction-of-arrival (DOA) estimation methods for coprime arrays have become popular for their excellent detection performance. However, these methods often suffer from grid mismatch problem due to the discretization of the potential angle space, which will cause DOA estimation performance degradation when the target is off-grid. To this end, we proposed a sparse-based off-grid DOA estimation method for coprime arrays in this paper, which includes two parts: coarse estimation process and fine estimation process. In the coarse estimation process, the grid points closest to the true DOAs, named coarse DOAs, are derived by solving an optimization problem, which is constructed according to the statistical property of the vectorized covariance matrix estimation error. Meanwhile, we eliminate the unknown noise variance effectively through a linear transformation. Due to finite snapshots effect, some undesirable correlation terms between signal and noise vectors exist in the sample covariance matrix. In the fine estimation process, we therefore remove the undesirable correlation terms from the sample covariance matrix first, and then utilize a two-step iterative method to update the grid biases. Combining the coarse DOAs with the grid biases, the final DOAs can be obtained. In the end, simulation results verify the effectiveness of the proposed method.

scholarly journals Multi-DOA estimation based on the KR image tensor and improved estimation network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ye Yuan ◽  
Shuang Wu ◽  
Yong Yang ◽  
Naichang Yuan
Keyword(s):  
Deep Neural Networks ◽  
Estimation Error ◽  
Doa Estimation ◽  
Estimation Problem ◽  
Estimation Methods ◽  
Training Scheme ◽  
Great Performance ◽  
Angle Difference ◽  
Label Strategy ◽  
Better Than

AbstractDeep neural networks have shown great performance for direction-of-arrival (DOA) estimation problem, but it is necessary to design some suitable networks to solve the multi-DOA estimation problem. In this paper, we use Khatri–Rao product to increase the degree of freedom of antenna array and obtain the image tensor of covariance matrix, then we propose an improved estimation network to process the tensor. We use the curriculum learning scheme and partial label strategy to develop a CurriculumNet training scheme. The training/validation results shows that the proposed training scheme can increase the generalization of the estimation network and improve the accuracy of network around $$10\%$$ 10 % . The estimation performance of the proposed network shows high-resolution results, which can distinguish two adjacent signals with angle difference of $$3^\circ $$ 3 ∘ . Moreover, the proposed estimation network has root mean square estimation error lower than $$1^\circ $$ 1 ∘ when signal noise ratio equals $$-\,10\,{\mathrm {dB}}$$ - 10 dB and can estimate DOAs precisely by only 8 snapshots, which performs much better than prior deep neural network based estimation methods and can estimate multi-DOA results under hostile estimation environments.

scholarly journals Technical Note: Estimating fusion properties for polyacids

2011 ◽  
Vol 11 (3) ◽  
pp. 7535-7553 ◽  
Author(s):  
S. Compernolle ◽  
K. Ceulemans ◽  
J.-F. Müller
Keyword(s):  
Vapor Pressure ◽  
Estimation Error ◽  
Estimation Method ◽  
Technical Note ◽  
Estimation Methods ◽  
Subcooled Liquid ◽  
Fusion Data ◽  
Simple Estimation ◽  
Data Estimation ◽  
Liquid Vapor

Abstract. Organic aerosol (OA) components are generally assumed to be liquid-like. Hence, to describe the partitioning of these components, the liquid vapor pressure of these components is desired. Polyacids and functionalized polyacids can be a significant part of OA. But often, measurements are available only for solid state vapor pressure, which can differ by orders of magnitude from their liquid counterparts. To convert such a sublimation pressure to a subcooled liquid vapor pressure, fusion properties (two out of these three quantities: fusion enthalpy, fusion entropy, fusion temperature) are required. Unfortunately, experimental knowledge of fusion properties is sometimes missing in part or totally, hence an estimation method is required. Several fusion data estimation methods are tested here against experimental data of polyacids. Next, we develop a simple estimation method, specifically for this kind of compounds, reducing significantly the estimation error.

A Markley Variables-based Attitude Estimation Method Using Optical Flow and a Star Vector for Spinning Spacecraft

2018 ◽  
Vol 71 (6) ◽  
pp. 1589-1598 ◽  
Author(s):  
Xiaolin Ning ◽  
Zonghe Ding ◽  
Mingzhu Xu ◽  
Jiancheng Fang ◽  
Gang Liu
Keyword(s):  
Optical Flow ◽  
Angular Rate ◽  
Estimation Method ◽  
Attitude Estimation ◽  
Spin Axis ◽  
Estimation Methods ◽  
Star Sensor ◽  
Axis Orientation ◽  
Sun Sensor ◽  
The Impact

Markley variables have advantages of slow variation, easy numerical integration and high precision in describing the attitude of spinning spacecraft. Previous attitude estimation methods based on Markley variables for spinning spacecraft usually employ a sun vector from the sun sensor, a magnetic vector from the magnetometer, or the angular rate from the gyro as the measurement. This paper proposes a Markley variables-based attitude estimation method using optical flow and a star vector from a star sensor as the measurement, where optical flow provides rate information and the star vector provides direction information. This method can estimate the direction of the spin axis and spin angular rate very well by using only one star sensor. In addition, the star sensor has higher accuracy than the traditional sun sensor and magnetometer, and the star sensor can also replace the gyro in case the gyro is out of action. The impact factors of this method are also analysed, which include spin angular rate, spin axis orientation and spacecraft moment of inertia tensor error.

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