A new measurement for yaw estimation of land vehicles using MARG sensors

Sensor Review ◽  
2019 ◽  
Vol 39 (5) ◽  
pp. 636-644
Author(s):  
Gang Shi ◽  
Xisheng Li ◽  
Zhe Wang ◽  
Yanxia Liu
Keyword(s):  
Estimation Accuracy ◽  
Fusion Algorithm ◽  
Measurement Equation ◽  
Content Type ◽  
Land Vehicles ◽  
Adaptive Measurement ◽  
Other Orientation ◽  
Vehicle Status ◽  
Conventional Algorithm ◽  
Magnetic Disturbances

Purpose The magnetometer measurement update plays a key role in correcting yaw estimation in fusion algorithms, and hence, the yaw estimation is vulnerable to magnetic disturbances. The purpose of this study is to improve the ability of the fusion algorithm to deal with magnetic disturbances. Design/methodology/approach In this paper, an adaptive measurement equation based on vehicle status is derived, which can constrain the yaw estimation from drifting when vehicle is running straight. Using this new measurement, a Kalman filter-based fusion algorithm is constructed, and its performance is evaluated experimentally. Findings The experiments results demonstrate that the new measurement update works as an effective supplement to the magnetometer measurement update in the present of magnetic disturbances, and the proposed fusion algorithm has better yaw estimation accuracy than the conventional algorithm. Originality/value The paper proposes a new adaptive measurement equation for yaw estimation based on vehicle status. And, using this measurement, the fusion algorithm can not only reduce the weight of disturbed sensor measurement but also utilize the character of vehicle running to deal with magnetic disturbances. This strategy can also be used in other orientation estimation fields.

scholarly journals An Improved Yaw Estimation Algorithm for Land Vehicles Using MARG Sensors

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3251 ◽  
Author(s):  
Gang Shi ◽  
Xisheng Li ◽  
Zhengfu Jiang
Keyword(s):  
Kalman Filter ◽  
Angular Rate ◽  
Kinematic Model ◽  
Estimation Algorithm ◽  
Attitude Estimation ◽  
The Other ◽  
Land Vehicles ◽  
Vehicle Status ◽  
Conventional Algorithm

This paper presents a linear Kalman filter for yaw estimation of land vehicles using magnetic angular rate and gravity (MARG) sensors. A gyroscope measurement update depending on the vehicle status and constraining yaw estimation is introduced. To determine the vehicle status, the correlations between outputs from different sensors are analyzed based on the vehicle kinematic model and Coriolis theorem, and a vehicle status marker is constructed. In addition, a two-step measurement update method is designed. The method treats the magnetometer measurement update separately after the other updates and eliminates its impact on attitude estimation. The performances of the proposed algorithm are tested in experiments and the results show that: the introduced measurement update is an effective supplement to the magnetometer measurement update in magnetically disturbed environments; the two-step measurement update method makes attitude estimation immune to errors induced by magnetometer measurement update, and the proposed algorithm provides more reliable yaw estimation for land vehicles than the conventional algorithm.

Robust-PCA-based hierarchical plane extraction for application to geometric 3D indoor mapping

2014 ◽  
Vol 41 (2) ◽  
pp. 203-212 ◽  
Author(s):  
Suyong Yeon ◽  
ChangHyun Jun ◽  
Hyunga Choi ◽  
Jaehyeon Kang ◽  
Youngmok Yun ◽  
...  
Keyword(s):  
Principal Component ◽  
Point Clouds ◽  
Previous Method ◽  
Divide And Conquer ◽  
Sensor Data ◽  
Estimation Accuracy ◽  
Content Type ◽  
Extraction Algorithm ◽  
Plane Extraction ◽  
Scan Data

Purpose – The authors aim to propose a novel plane extraction algorithm for geometric 3D indoor mapping with range scan data. Design/methodology/approach – The proposed method utilizes a divide-and-conquer step to efficiently handle huge amounts of point clouds not in a whole group, but in forms of separate sub-groups with similar plane parameters. This method adopts robust principal component analysis to enhance estimation accuracy. Findings – Experimental results verify that the method not only shows enhanced performance in the plane extraction, but also broadens the domain of interest of the plane registration to an information-poor environment (such as simple indoor corridors), while the previous method only adequately works in an information-rich environment (such as a space with many features). Originality/value – The proposed algorithm has three advantages over the current state-of-the-art method in that it is fast, utilizes more inlier sensor data that does not become contaminated by severe sensor noise and extracts more accurate plane parameters.

Autonomous navigation algorithm based on AUKF filter about fusion of geomagnetic and sunlight directions

2018 ◽  
Vol 11 (4) ◽  
pp. 471-485 ◽  
Author(s):  
Bing Hua ◽  
Zhiwen Zhang ◽  
Yunhua Wu ◽  
Zhiming Chen
Keyword(s):  
Orbit Determination ◽  
Geomagnetic Field ◽  
Autonomous Navigation ◽  
Unscented Kalman Filter ◽  
Satellite Orbit ◽  
Field Vector ◽  
Low Earth Orbit ◽  
Fusion Algorithm ◽  
Content Type ◽  
Navigation Algorithm

Purpose The geomagnetic field vector is a function of the satellite’s position. The position and speed of the satellite can be determined by comparing the geomagnetic field vector measured by on board three-axis magnetometer with the standard value of the international geomagnetic field. The geomagnetic model has the disadvantages of uncertainty, low precision and long-term variability. Therefore, accuracy of autonomous navigation using the magnetometer is low. The purpose of this paper is to use the geomagnetic and sunlight information fusion algorithm to improve the orbit accuracy. Design/methodology/approach In this paper, an autonomous navigation method for low earth orbit satellite is studied by fusing geomagnetic and solar energy information. The algorithm selects the cosine value of the angle between the solar light vector and the geomagnetic vector, and the geomagnetic field intensity as observation. The Adaptive Unscented Kalman Filter (AUKF) filter is used to estimate the speed and position of the satellite, and the simulation research is carried out. This paper also made the same study using the UKF filter for comparison with the AUKF filter. Findings The algorithm of adding the sun direction vector information improves the positioning accuracy compared with the simple geomagnetic navigation, and the convergence and stability of the filter are better. The navigation error does not accumulate with time and has engineering application value. It also can be seen that AUKF filtering accuracy is better than UKF filtering accuracy. Research limitations/implications Geomagnetic navigation is greatly affected by the accuracy of magnetometer. This paper does not consider the spacecraft’s environmental interference with magnetic sensors. Practical implications Magnetometers and solar sensors are common sensors for micro-satellites. Near-Earth satellite orbit has abundant geomagnetic field resources. Therefore, the algorithm will have higher engineering significance in the practical application of low orbit micro-satellites orbit determination. Originality/value This paper introduces a satellite autonomous navigation algorithm. The AUKF geomagnetic filter algorithm using sunlight information can obviously improve the navigation accuracy and meet the basic requirements of low orbit small satellite orbit determination.

Precision navigation system design for Moon penetrator

2016 ◽  
Vol 88 (6) ◽  
pp. 791-798
Author(s):  
Xiaogang Wang ◽  
Wutao Qin ◽  
Yuliang Bai ◽  
Naigang Cui
Keyword(s):  
System Design ◽  
Kalman Filtering ◽  
Navigation System ◽  
Estimation Accuracy ◽  
Line Of Sight ◽  
Accurate Estimation ◽  
Navigation Systems ◽  
Vision Sensor ◽  
Content Type ◽  
Sigma Point

Purpose Penetrator plays an important role in the exploration of Moon and Mars. The navigation method is a key technology during the development of penetrator. To meet the high accuracy requirements of Moon penetrator, this paper aims to propose two kinds of navigation systems. Design/methodology/approach The line of sight of vision sensor between the penetrator and Moon orbiter could be utilized as the measurement during the navigation system design. However, the analysis of observability shows that the navigation system cannot estimate the position and velocity of penetrator, when the line of sight measurement is the only resource of information. Therefore, the Doppler measurement due to the relative motion between penetrator and the orbiter is used as the supplement. The other option is the relative range measurement between penetrator and the orbiter. The sigma-point Kalman Filtering is implemented to fuse the information from the vision sensor and Doppler or rangefinder. The observability of two navigation system is analyzed. Findings The sigma-point Kalman filtering could be used based on vision sensor and Doppler radar or laser rangefinder to give an accurate estimation of Moon penetrator position and velocity without increasing the payload of Moon penetrator or decreasing the estimation accuracy. However, the simulation result shows that the last method is better. The observability analysis also proves this conclusion. Practical implications Two navigation systems are proposed, and the simulations show that both systems can provide accurate estimation of states of penetrator. Originality/value Two navigation methods are proposed, and the observability of these navigation systems is analyzed. The sigma-point Kalman filtering is first introduced to the vision-based navigation system for Moon penetrator to provide precision navigation during the descent phase of Moon penetrator.

scholarly journals Construction tender price estimation standardization (TPES) in Malaysia

2018 ◽  
Vol 25 (3) ◽  
pp. 443-457 ◽  
Author(s):  
Salihudin Hassim ◽  
Ratnasamy Muniandy ◽  
Aidi Hizami Alias ◽  
Pedram Abdullah
Keyword(s):  
Neural Network ◽  
Coming Out ◽  
Cost Estimation ◽  
Influential Factors ◽  
Estimation Accuracy ◽  
Content Type ◽  
Estimation Process ◽  
Price Estimation ◽  
Fuzzy Neural ◽  
Artificial Neural Network Method

Purpose The pre-tender estimation process is still a hazy and inaccurate process, despite it has been practiced over decades, especially in Malaysia. The methods evolved over time largely depend on the amount of information available at the time of estimation. More often than not, the estimate produced during the pre-tender stage is far more than the tender cost of the project and sometimes, it is perilously underestimated and caused major problems to the client in the monetary planning. The purpose of this paper is to determine the most influential factors on the deviation of pre-tender cost estimation in Malaysia by conducting a survey. Design/methodology/approach Fuzzy logic, combined with artificial neural network method (fuzzy neural network) was then used to develop an estimating model to aid the pre-tender estimation process. Findings The results showed that the model is able to shift the cost estimation toward accuracy. This model can be used to improve the pre-tender estimation accuracy, enabling the client to take the necessary early measures in preparing the funding for a building project in Malaysia. Originality/value To the authors’ knowledge, this is the first study on tender price estimation standardization for a construction project in Malaysia. In addition, the authors have used factors from literature for the model, which shows the thoroughness of the developed model. Thus, the findings and the model developed in this study should be able to assist contractors in coming out with a more accurate tender price estimation.

A novel tracking system for human following robots with fusion of MMW radar and monocular vision

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yipeng Zhu ◽  
Tao Wang ◽  
Shiqiang Zhu
Keyword(s):  
Sensor Fusion ◽  
Tracking System ◽  
Cost Effective ◽  
Point Clouds ◽  
Human Detection ◽  
Multimodal Fusion ◽  
Target Person ◽  
Fusion Algorithm ◽  
Content Type ◽  
Tracking Method

Purpose This paper aims to develop a robust person tracking method for human following robots. The tracking system adopts the multimodal fusion results of millimeter wave (MMW) radars and monocular cameras for perception. A prototype of human following robot is developed and evaluated by using the proposed tracking system. Design/methodology/approach Limited by angular resolution, point clouds from MMW radars are too sparse to form features for human detection. Monocular cameras can provide semantic information for objects in view, but cannot provide spatial locations. Considering the complementarity of the two sensors, a sensor fusion algorithm based on multimodal data combination is proposed to identify and localize the target person under challenging conditions. In addition, a closed-loop controller is designed for the robot to follow the target person with expected distance. Findings A series of experiments under different circumstances are carried out to validate the fusion-based tracking method. Experimental results show that the average tracking errors are around 0.1 m. It is also found that the robot can handle different situations and overcome short-term interference, continually track and follow the target person. Originality/value This paper proposed a robust tracking system with the fusion of MMW radars and cameras. Interference such as occlusion and overlapping are well handled with the help of the velocity information from the radars. Compared to other state-of-the-art plans, the sensor fusion method is cost-effective and requires no additional tags with people. Its stable performance shows good application prospects in human following robots.

A fast algorithm for the adaptive discretization of 3D parametric curves

2019 ◽  
Vol 37 (5) ◽  
pp. 1663-1682
Author(s):  
Jianming Zhang ◽  
Chuanming Ju ◽  
Baotao Chi
Keyword(s):  
Mesh Generation ◽  
Fast Algorithm ◽  
Design Methodology ◽  
Three Dimensional ◽  
Parametric Curves ◽  
Content Type ◽  
Gaussian Integration ◽  
Optimal Discretization ◽  
Adaptive Discretization ◽  
Conventional Algorithm

Purpose The purpose of this paper is to present a fast algorithm for the adaptive discretization of three-dimensional parametric curves. Design/methodology/approach The proposed algorithm computes the parametric increments of all segments to obtain the parametric coordinates of all discrete nodes. This process is recursively applied until the optimal discretization of curves is obtained. The parametric increment of a segment is inversely proportional to the number of sub-segments, which can be subdivided, and the sum of parametric increments of all segments is constant. Thus, a new expression for parametric increment of a segment can be obtained. In addition, the number of sub-segments, which a segment can be subdivided is calculated approximately, thus avoiding Gaussian integration. Findings The proposed method can use less CPU time to perform the optimal discretization of three-dimensional curves. The results of curves discretization can also meet requirements for mesh generation used in the preprocessing of numerical simulation. Originality/value Several numerical examples presented have verified the robustness and efficiency of the proposed algorithm. Compared with the conventional algorithm, the more complex the model, the more time the algorithm saves in the process of curve discretization.

A novel position estimation method using accelerometer based error correction

2016 ◽  
Vol 33 (6) ◽  
pp. 1784-1799 ◽  
Author(s):  
Chien-Hsing Chen ◽  
Ming-Chih Chen
Keyword(s):  
Error Correction ◽  
Wireless Network ◽  
Estimation Method ◽  
Correction Method ◽  
Position Estimation ◽  
Estimation Accuracy ◽  
Received Signal Strength Indicator ◽  
Content Type ◽  
Signal Drift ◽  
Major Factors

Purpose – The purpose of this paper is to present a novel position estimation method to accurately locate an object. An accelerometer-based error correction method is also developed to correct the positioning error caused by signal drift of a wireless network. Finally, the method is also utilized to locate cows in a farm for monitoring the action of standing heat. Design/methodology/approach – The proposed method adopts the received signal strength indicator (RSSI) of a wireless sensor network (WSN) to compute the position of an object. The RSSI signal can be submitted from an endpoint device. A complex environment destabilizes the RSSI value, making the position estimation inaccurate. Therefore, a three-axial accelerometer is adopted to correct the position estimation accuracy. Timer and acceleration are two major factors in computing the error correction value to adjust the position estimate. Findings – The proposed method is tested on a farm management system for positioning dairy cows accurately. Devices with WSN module and three-axial accelerometer are mounted on the cows to monitor their positions and actions. Research limitations/implications – If cows in a crowded farm are close to each other, then the position estimation method is unable to position each cow correctly because too many close objects cause interference in the wireless network. Practical implications – Experimental results demonstrate that the proposed method improves the position accuracy, and monitor the heat action of the cows effectively. Originality/value – No position estimation method has been utilized to locate cows in a farm, especially for monitoring their actions via WSN and accelerometer. The proposed method adopts an accelerometer to efficiently improve the position error caused from the signal drift of WSN.

An Optimal Estimation of Aerodynamic Parameters for GPS Guided Missile

2013 ◽  
Vol 325-326 ◽  
pp. 734-737
Author(s):  
Li Yan ◽  
Chen Yang ◽  
Chuan Bing Ding
Keyword(s):  
Optimal Estimation ◽  
Estimation Accuracy ◽  
State Variables ◽  
Aerodynamic Coefficients ◽  
Measurement Equation ◽  
Aerodynamic Coefficient ◽  
Error Sources ◽  
Disturbance Source ◽  
Aerodynamic Parameters ◽  
Source Errors

The disturbance factors in the missile flight are obvious,so the analytical equations of missile longitudinal disturbance of movement is derived . The missile body longitudinal aerodynamic parameters are fitting out with wind tunnel experimental data. The equation of system state takes the aerodynamic coefficient errors interference source and GPS error sources as the state variables, and the system measurement equation adopts GPS pseudo-range measurements. In passive segment, the aerodynamic coefficients disturbing errors sources of th missile longitudinal plane is estimated optimally with extended Kalman filter. The results show that the method accelerate the convergence speed of the disturbance source errors, and improve the estimation accuracy of the perturbation errors.

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