A Dual-Satellite GNSS Positioning Algorithm of High Accuracy in Incomplete Condition

Global navigation satellite system could provide accurate positioning results in signal complete condition. However, the performance is severe when signal denied, especially for the single-mode Bei Dou receiver. This paper proposes a dual-satellite positioning algorithm to promote the positioning performance in the satellite signal gap. The new algorithm utilizes the previous positioning data stored in complete condition to simplify the positioning equations. As the clock bias persists for a short period, this proposed method could work out accurate positioning results by only two visible satellites, without the need of computing the clock bias. Also, the Kalman filtering algorithm is used to smooth the trajectories, and improve the positioning results. During the incomplete period, only two satellites for 30 seconds and three satellites for 60 seconds, the preliminary experiment result shows that, the presented method could provide almost the same positioning results as in complete condition.

Artificial neural-network-based visible light positioning algorithm with a diffuse optical channel

Visible light positioning becomes popular recently. However, its performance is degraded by the indoor diffuse optical channel. An artificial neural-network-based visible light positioning algorithm is proposed in this Letter, and a trained neural network is used to achieve positioning with a diffuse channel. Simulations are made to evaluate the proposed positioning algorithm. Results show that the average positioning error is reduced about 13 times, and the positioning time is reduced about two magnitudes. Moreover, the proposed algorithm is robust with a different field-of-view of the receiver and the refiectivity of the wall, which is suitable for various position- ing applications.

Improved positioning algorithm based on two step least square in NLOS environments

Non-line-of-sight （NLOS） propagation is a major source of error for accurate time-of-arrival （TOA） location estimation. The two-step least square （LS） method, which does not need any information about the distribution of the NLOS error, has been studied in related researches to provide efficient location estimation of the mobile terminal （MT）. By observing the estimation of the error covariance matrix in the two-step LS method, this paper finds that the measured distances are more suitable than the initial estimated distances by maximum likelihood （ML） algorithm in NLOS environments. Moreover, by making use of the geometry relations among fixed terminals （FTs）, some measured distances which are corrupted by big NLOS errors may be reduced. Further, an iteration method can be adopted to further improve the performance of the algorithm. Accordingly, an improved algorithm based on the two-step LS method is proposed. Simulation results demonstrate that the improved algorithm has better performance than the two-step LS method and converges more quickly. Meanwhile, it is robust in different NLOS environments.

Analysis and Design of Surgical Instrument Localization Algorithm

With the help of surgical navigation system,doctors can operate on patients more intuitively and accurately.The positioning accuracy and real-time performance of surgical instruments are very important to the whole system.In this paper,we analyze and design the detection algorithm of surgical instrument location mark,and estimate the posture of surgical instrument.In addition,we optimized the pose by remapping.Finally,the algorithm of location mark detection proposed in this paper and the posture analysis data of surgical instruments are verified and analyzed through experiments.The final result shows a high accuracy.

An indoor positioning system for mobile target tracking based on VLC and IMU fusion

An indoor positioning system( IPS) is designed to realize positioning and tracking of mobile targets,by taking advantages of both the visible light communication( VLC) and inertial measurement unit( IMU). The platform of the IPS is designed,which consists of the light-emitting diode( LED)based transmitter,the receiver and the positioning server. To reduce the impact caused by measurement errors,both inertial sensing data and the received signal strength( RSS) from the VLC are calibrated. Then,a practical propagation model is established to obtain the distance between the transmitter and the receiver from the RSS measurements. Furthermore,a hybrid positioning algorithm is proposed by using the adaptive Kalman filter( AKF) and the weighted least squares( WLS)trilateration to estimate the positions of the mobile targets.Experimental results show that the developed IPS using the proposed hybrid positioning algorithm can extend the localization area of VLC,mitigate the IMU drifts and improve the positioning accuracy of mobile targets.

Novel indoor positioning system based on ultra-wide bandwidth

To tackle challenges such as interference and poor accuracy of indoor positioning systems,a novel scheme based on ultra-wide bandwidth(UWB)technology is proposed.First,we illustrate a distance measuring method between two UWB devices.Then,a Taylor series expansion algorithm is developed to detect coordinates of the mobile node using the location of anchor nodes and the distance between them.Simulation results show that the observation error under our strategy is within 15 cm,which is superior to existing algorithms.The final experimental data in the hardware system mainly composed of STM32 and DW1000 also confirms the performance of the proposed scheme.

CALIBRATION OF A 6-DOF SPACE ROBOT USING GENETIC ALGORITHM

The kinematic error model of a 6-DOF space robot is deduced, and the cost function of kinematic parameter identification is built. With the aid of the genetic algorithm （GA） that has the powerful global adaptive probabilistic search ability, 24 parameters of the robot are identified through simulation, which makes the pose （position and orientation） accuracy of the robot a great improvement. In the process of the calibration, stochastic measurement noises are considered. Lastly, generalization of the identified kinematic parameters in the whole workspace of the robot is discussed. The simulation results show that calibrating the robot with GA is very stable and not sensitive to measurement noise. Moreover, even if the robot＇s kinematic parameters are relative, GA still has strong search ability to find the optimum solution.

A POSITIVE INTERIOR-POINT ALGORITHM FOR NONLINEAR COMPLEMENTARITY PROBLEMS

A new iterative method,which is called positive interior-point algorithm,is presented for solving the nonlinear complementarity problems.This method is of the desirable feature of robustness.And the convergence theorems of the algorithm is established.In addition,some numerical results are reported.

Research on Library Management System based on RFID Technology

This paper analyses the demand of intelligent bookshelf system, designed the overall structure of intelligent Library shelves system based on RFID and create the system use case model. The analysis and design of the intelligent bookshelf system structure from two aspects of hardware and software, puts forward the three layer system structure model of intelligent bookshelf system based on RFID technology. The paper provide the analysis method and the design ideas of library intelligent bookshelf system, has the very high application value, contribute to the realization of library intelligent management and improve the level of library services, provide a useful reference to the development of current book positioning system and intelligent goods shelf system solution.

Positioning performance analysis of the time sum of arrival algorithm with error features

The theoretical positioning accuracy of multilateration(MLAT) with the time difference of arrival(TDOA) algorithm is very high. However, there are some problems in practical applications. Here we analyze the location performance of the time sum of arrival(TSOA) algorithm from the root mean square error(RMSE) and geometric dilution of precision(GDOP) in additive white Gaussian noise(AWGN) environment. The TSOA localization model is constructed. Using it, the distribution of location ambiguity region is presented with 4-base stations. And then, the location performance analysis is started from the 4-base stations with calculating the RMSE and GDOP variation. Subsequently, when the location parameters are changed in number of base stations, base station layout and so on, the performance changing patterns of the TSOA location algorithm are shown. So, the TSOA location characteristics and performance are revealed. From the RMSE and GDOP state changing trend, the anti-noise performance and robustness of the TSOA localization algorithm are proved. The TSOA anti-noise performance will be used for reducing the blind-zone and the false location rate of MLAT systems.

Simultaneous Position Estimation and Omnidirectional Camera Parameter Calibration for Multiple Mobile Robots

This paper proposed an algorithm on simultaneous position estimation and calibration of omnidirectional camera parameters for a group of multiple mobile robots. It is aimed at developing of exploration and information gathering robotic system in unknown environment. Here, each mobile robot is not possible to know its own position. It can only estimate its own position by using the measurement value including white noise acquired by two omnidirectional cameras mounted on it. Each mobile robot is able to obtain the distance to those robots observed from the images of two omnidirectional cameras while making calibration during moving but not in advance. Simulation of three robots moving straightly shows the effectiveness of the proposed algorithm.

A Stochastic Approach for Cooperative Position Estimation of Multiple Mobile Robots

This paper proposes the cooperative position estimation of a group of mobile robots, which pertbrms disaster relief tasks in a wide area. When searching the wide area, it becomes important to know a robot＇s position correctly. However, for each mobile robot, it is impossible to know its own position correctly. Therefore, each mobile robot estimates its position from the data of sensor equipped on it. Generally, the sensor data is incorrect since there is sensor noise, etc. This research considers two types of the sensor data errors from omnidirectional camera. One is the error of white noise of the image captured by omnidirectional camera and so on. Another is the error of position and posture between two omnidirectional cameras. To solve the error of latter case, we proposed a self-position estimation algorithm for multiple mobile robots using two omnidirectional cameras and an accelerometer. On the other hand, to solve the error of the former case, this paper proposed an algorithm of cooperative position estimation for multiple mobile robots. In this algorithm, each mobile robot uses two omnidirectional cameras to observe the surrounding mobile robot and get the relative position between mobile robots. Each mobile robot estimates its position with only measurement data of each other mobile robots. The algorithm is based on a Bayesian filtering. Simulations of the proposed cooperative position estimation algorithm for multiple mobile robots are performed. The results show that position estimation is possible by only using measurement value from each other robot.

Total Positivity of UE Spline Basis

A new geometric method to prove the total positivity of UE spline basis was proposed. UE spline basis is a kind of basis defined over algebraic-trigonometric unified space. UE spline basis shares most properties of the usual polynomial B-Splines. Total positivity is an important property for spline basis, it is highly related with shape preserving and variation diminishing properties. Knot inserted algorithm is the most useful algorithm for spline curves since many other useful properties are based on it. It is necessary to prove the total positivity of UE spline basis using knot inserted algorithm intuitively, not only enrich the UE spline basis theory, but also can be treated as supplement to the total positivity in algebraic sense. This approach also can be extended to other analogical bases.

Context-Aware Adaptive Routing for Opportunistic Network

Opportunistic networking-forwarding messages in a disconnected mobile ad hoc network via any encountered nodes offers a new mechanism for exploiting the mobile devices that many users already carry. However, forwarding messages in such a network is trapped by many particular challenges, and some protocols have contributed to solve them partly. In this paper, we propose a Context-Aware Adaptive opportunistic Routing algorithm（CAAR）. The algorithm firstly predicts the approximate location and orientation of the destination node by using its movement key positions and historical communication records, and then calculates the best neighbor for the next hop by using location and velocity of neighbors. In the unpredictable cases, forwarding messages will be delivered to the more capable forwarding nodes or wait for another transmission while the capable node does not exist in the neighborhood. The proposed algorithm takes the movement pattern into consideration and can adapt different network topologies and movements. The experiment results show that the proposed routing algorithm outperforms the epidemic forwarding（EF） and the prophet forwarding（PF） in packet delivery ratio while ensuring low bandwidth overhead.