A whole-net cooperative positioning method based on CDGNSS and inter-vehicle ranging

Relative positioning is recognized as an important issue for vehicles in urban environments.Multi-vehicle Cooperative Positioning(CP)techniques which fuse the Global Navigation Satellite System(GNSS)and inter-vehicle ranging have attracted attention in improving the performance of baseline estimation between vehicles.However,current CP methods estimate the baselines separately and ignore the interactions among the positioning information of different baselines.These interactions are called’information coupling’.In this work,we propose a new multivehicle precise CP framework using the coupled information in the network based on the Carrier Differential GNSS(CDGNSS)and inter-vehicle ranging.We demonstrate the benefit of the coupled information by deriving the Cramer-Rao Lower Bound(CRLB)of the float estimation in CP.To fully use this coupled information,we propose a Whole-Net CP(WN-CP)method which consists of the Whole-Net Extended Kalman Filter(WN-EKF)as the float estimation filter,and the Partial Baseline Fixing(PBF)as the ambiguity resolution part.The WN-EKF fuses the measurements of all baselines simultaneously to improve the performance of float estimation,and the PBF strategy fixes the ambiguities of the one baseline to be estimated,instead of full ambiguity resolution,to reduce the computation load of ambiguity resolution.Field tests involving four vehicles were conducted in urban environments.The results show that the proposed WN-CP method can achieve better performance and meanwhile maintain a low computation load compared to the existing methods.

Function of Cooperative Learning in Developing Positive Affect

This paper focus on the function of cooperative learning in developing positive affect,Including reducing anxiety,increasing motivation,facilitating the development of positive attitudes toward learning and language learning,promoting self-esteem,as well as supporting different learning styles and encouraging perseverance in the difficult and confusing process of learning a foreign language.

Improved GNSS Cooperation Positioning Algorithm for Indoor Localization

For situations such as indoor and underground parking lots in which satellite signals are obstructed,GNSS cooperative positioning can be used to achieve highprecision positioning with the assistance of cooperative nodes.Here we study the cooperative positioning of two static nodes,node 1 is placed on the roof of the building and the satellite observation is ideal,node 2 is placed on the indoor windowsill where the occlusion situation is more serious,we mainly study how to locate node 2 with the assistance of node 1.Firstly,the two cooperative nodes are located with pseudo-range single point positioning,and the positioning performance of cooperative node is analyzed,therefore the information of pseudo-range and position of node 1 is obtained.Secondly,the distance between cooperative nodes is obtained by using the baseline method with double-difference carrier phase.Finally,the cooperative location algorithms are studied.The Extended Kalman Filtering(EKF),Unscented Kalman Filtering(UKF)and Particle Filtering(PF)are used to fuse the pseudo-range,ranging information and location information respectively.Due to the mutual influences among the cooperative nodes in cooperative positioning,the EKF,UKF and PF algorithms are improved by resetting the error covariance matrix of the cooperative nodes at each update time.Experimental results show that after being improved,the influence between the cooperative nodes becomes smaller,and the positioning performance of the nodes is better than before.

Use of land's cooperative object to estimate UAV's pose for autonomous landing

The research of unmanned aerial vehicles＇（UAVs＇）autonomy navigation and landing guidance with computer vision has important signifcance.However,because of the image blurring,the position of the cooperative points cannot be obtained accurately,and the pose estimation algorithms based on the feature points have low precision.In this research,the pose estimation algorithm of UAV is proposed based on feature lines of the cooperative object for autonomous landing.This method uses the actual shape of the cooperative-target on ground and the principle of vanishing line.Roll angle is calculated from the vanishing line.Yaw angle is calculated from the location of the target in the image.Finally,the remaining extrinsic parameters are calculated by the coordinates transformation.Experimental results show that the pose estimation algorithm based on line feature has a higher precision and is more reliable than the pose estimation algorithm based on points feature.Moreover,the error of the algorithm we proposed is small enough when the UAV is near to the landing strip,and it can meet the basic requirements of UAV＇s autonomous landing.

A Novel Adaptive Cooperative Location Algorithm for Wireless Sensor Networks

To overcome the disadvantages of the location algorithm based on received signal strength indication(RSSI) in the existing wireless sensor networks(WSNs),a novel adaptive cooperative location algorithm is proposed.To tolerate some minor errors in the information of node position,a reference anchor node is employed.On the other hand,Dixon method is used to remove the outliers of RSSI,the standard deviation threshold of RSSI and the learning model are put forward to reduce the ranging error of RSSI and improve the positioning precision effectively.Simulations are run to evaluate the performance of the algorithm.The results show that the proposed algorithm offers more precise location and better stability and robustness.

Binding equilibrium study between Mn( Ⅱ ) and HSA or BSA

The binding of Mn( Ⅱ ) to human serum albumin (HSA) or bovine serum albumin (BSA) has been studied by equilibrium dialysis at physiological pH (7. 43). The Scatchard analysis indicates that there are 1.8 and 1.9 strong binding sites of Mn( Ⅱ ) in HSA and BSA, respectively. The successive stability constants which are reported for the first time are obtained by non-linear least-squares methods fitting Bjerrum formula. For both Mn( Ⅱ )-HSA and Mn( Ⅱ )-BSA systems, the order of magnitude of K1 was found to be 104. The analyses of Hill plots and free energy coupling show that the positive cooperative effect was found in both Mn( Ⅱ )-HSA and Mn( Ⅱ )-BSA systems . The results of Mn ( Ⅱ ) competing with Cu ( Ⅱ ) 、 Zn(Ⅱ)、Cd( Ⅱ) or Ca( Ⅱ ) to bind to HSA or BSA further support the conjecture that there are two strong binding sites of Mn( Ⅱ) in both HSA and BSA. One is most probably located at the tripeptide segment of N- terminal sequence of HSA and BSA molecules involving four groups composed of n