Current-Aided Multiple-AUV Cooperative Localization and Target Tracking in Anchor-Free Environments

In anchor-free environments,where no devices with known positions are available,the error growth of autonomous underwater vehicle(AUV)localization and target tracking is unbounded due to the lack of references and the accumulated errors in inertial measurements.This paper aims to improve the localization and tracking accuracy by involving current information as extra references.We first integrate current measurements and maps with belief propagation and design a distributed current-aided message-passing scheme that theoretically solves the localization and tracking problems.Based on this scheme,we propose particle-based cooperative localization and target tracking algorithms,named CaCL and CaTT,respectively.In AUV localization,CaCL uses the current measurements to correct the predicted and transmitted position information and alleviates the impact of the accumulated errors in inertial measurements.With target tracking,the current maps are applied in CaTT to modify the position prediction of the target which is calculated through historical estimates.The effectiveness and robustness of the proposed methods are validated through various simulations by comparisons with alternative methods under different trajectories and current conditions.

Cooperative localization against GPS signal loss in multiple UAVs flight

Based on multiple unmanned aerial vehicles（UAVs） flight at a constant altitude,a fault-tolerant cooperative localization algorithm against global positioning system（GPS） signal loss due to GPS receiver malfunction is proposed.Contrast to the traditional means with single UAV,the proposed method is based on the use of inter-UAV relative range measurements against GPS signal loss and more suitable for the small-size and low-cost UAV applications.Firstly,for re-localizing an UAV with a malfunction in its GPS receiver,an algorithm which makes use of any other three healthy UAVs in the cooperative flight as the reference points for re-localization is proposed.Secondly,by using the relative ranges from the faulty UAV to the other three UAVs,its horizontal location can be determined after the GPS signal is lost.In order to improve an accuracy of the localization,a Kalman filter is further exploited to provide the estimated location of the UAV with the GPS signal loss.The Kalman filter calculates the variance of observations in terms of horizontal dilution of positioning（HDOP） automatically.Then,during each discrete computing time step,the best reference points are selected adaptively by minimizing the HDOP.Finally,two simulation examples in Matlab/Simulink environment with five UAVs in cooperative flight are shown to evaluate the effectiveness of the proposed method.

An Area Optimization Based Cooperative Localization Algorithm with Node Selection

In recent years,position information has become a key feature to drive location and context aware services in mobile communication.Researchers from all over the world have proposed many solu-tions for indoor positioning over the past several years.However,due to weak signals,multipath or non-line-of-sight signal propagation,accurately and efficiently localizing targets in harsh indoor environments re-mains a challenging problem.To improve the perfor-mance in harsh environment with insufficient anchors,cooperative localization has emerged.In this paper,a novel cooperative localization algorithm,named area optimization and node selection based sum-product al-gorithm over a wireless network(AN-SPAWN),is de-scribed and analyzed.To alleviate the high compu-tational complexity and build optimized cooperative cluster,a node selection method is designed for the cooperative localization algorithm.Numerical experi-ment results indicate that our proposed algorithm has a higher accuracy and is less impacted by NLOS errors than other conventional cooperative localization algo-rithms in the harsh indoor environments.

Distributed cooperative localization for sparse communication network with multi-locating messages

In cooperative localization with sparse communication networks, an agent maybe only receives part of locating messages from the others. It is difficult for the receiver to utilize the part instead of global knowledge. Under the extended Kalman filtering, the utilization of the locating message is maximized by two aspects： the locating message generating and multi-locating messages fusing. For the former, the covariance upper-bound technique, by introducing amplification coefficients, is employed to remove the dependency of locating messages on the global knowledge. For the latter, an optimization model is setup; the covariance matrix determinant of the receiver＇s state estimate, expressed as a function of the amplification coefficients, is selected as the optimization criterion, under linear constraints on the amplification coefficient characteristics and the communication connectivity. Using the optimization solution, the local optimal state of the receiver agent is obtained by the weighting fusion. Simulation with seven agents is shown to evaluate the effectiveness of the proposed algorithm.

Compensation Algorithm Based on Estimation State for Transmission Delay in Cooperative Localization

In order to maximize the utilization of the observation information in the cooperative localization,a compensation algorithm based on the estimation state is presented for transmission delay.Under the framework of the Kalman filter,two different processes of state estimating with and without transmission delay are investigated and contrasted.The expression of difference quantity caused by transmission delay is derived.It is used to compensate the present estimation state instead of the observed information compensation.According to the characteristics of state transition matrix,an equivalent expression of which successively impacts on the covariance factor in delay time is obtained.The simulation results show that the present estimated state is effectively corrected by transmission information and the relevance among agents is accurately updated.As a result,a higher positioning accuracy is achieved.Meanwhile,the consumption of recording and multiplication of the state transition matrix is saved.

A Multi-Vehicle Cooperative Localization Method Based on Belief Propagation in Satellite Denied Environment

The global navigation satellite system(GNSS)is currently being used extensively in the navigation system of vehicles.However,the GNSS signal will be faded or blocked in complex road environments,which will lead to a decrease in positioning accuracy.Owing to the higher-precision synchronization provided in the sixth generation(6G)network,the errors of ranging-based positioning technologies can be effectively reduced.At the same time,the use of terahertz in 6G allows excellent resolution of range and angle,which offers unique opportunities for multi-vehicle cooperative localization in a GNSS denied environment.This paper introduces a multi-vehicle cooperative localization method.In the proposed method,the location estimations of vehicles are derived by utilizing inertial measurement and then corrected by exchanging the beliefs with adjacent vehicles and roadside units.The multi-vehicle cooperative localization problem is represented using a factor graph.An iterative algorithm based on belief propagation is applied to perform the inference over the factor graph.The results demonstrate that our proposed method can offer a considerable capability enhancement on localization accuracy.

Multi-Robot Cooperative Localization:Time-Varying Unobservable Subspace Analysis and Consistent Algorithm Design

This paper investigates the problem of cooperative localization(CL)for a multi-robot system(MRS)under dynamic measurement topology,which involves a group of robots collectively estimating their poses with respect to a common reference frame using ego-motion measurements and robot-to-robot relative measurements.The authors provide a theoretical analysis of the time-varying unobservable subspace and propose a consistent cooperative localization algorithm.First,the authors introduce the relative measurement graph(RMG)to represent the relative pose measurements obtained by the MRS at each instant.Then,the authors derive the local observability matrix over a time interval.An equivalent relationship is established between the local observability matrix and the spectral matrices of the RMG.Moreover,the authors present a method for constructing the unobservable subspace based on the RMG under different topology conditions.Based on this analysis,the authors design a consistent cooperative localization algorithm that satisfies the constraints of the time-varying unobservable subspace.An analytical optimal solution is derived for the constrained optimization problem.Monte Carlo numerical simulations are conducted to demonstrate the consistency and accuracy of the proposed method.

Cooperative navigation and localization for multiple UUVs

The authors proposed a moving long baseline algorithm based on the extended Kalman filter （EKF） for cooperative navigation and localization of multi-unmanned underwater vehicles （UUVs）. Research on cooperative navigation and localization for multi-UUVs is important to solve navigation problems that restrict long and deep excursions. The authors investigated improvements in navigation accuracy. In the moving long base line （MLBL） structure, the master UUV is equipped with a high precision navigation system as a node of the moving long baseline, and the slave UUV is equipped with a low precision navigation system. They are both equipped with acoustic devices to measure relative location. Using traditional triangulation methods to calculate the position of the slave UUV may cause a faulty solution. An EKF was designed to solve this, combining the proprioceptive and exteroceptive sensors. Research results proved that the navigational accuracy is improved significantly with the MLBL method based on EKF.

Cooperative localization method for multi-robot based on PF-EKF

A method of cooperative localization for multi-robot in an unknown environment is described. They share information and perform localization by using relative observations and necessary communication. At initial time, robots do not know their positions. Once the robot that can obtain the absolute position information has its position, other robots use particle filter to fuse relative observations and maintain a set of samples respectively representing their positions. When the particles are close to s Gsussian distribution after a number of steps, we switch to an EKF to track the pose of the robots. Simulation results and real experiment show that PF-EKF method combines the robustness of PF and the efficiency of EKF. Robots can share the absolute position information and effectively localize themselves in an unknown environment.

Cooperative Localization and Tracking in Distributed Robot-Sensor Networks

This paper addresses the problem of real-time position and orientation estimation of networked mobile robots in two-dimensional Euclidean space with simultaneous tracking of a rigid unknown object based on exteroceptive sensory information extracted from distributed vision systems. The sufficient and necessary conditions for team localization are proposed. A localization and object tracking approach based on statistical operators and graph searching algorithms is presented for a team of robots localized with het- erogeneous sensors. The approach was implemented in an experimental platform consisting of car-like mobile robots equipped with omnidirectional video cameras and IEEE 802.11b wireless networking. The experimental results validate the approach.

Cooperative localization for next-generation converged networks with closed-form solution

In this article, a novel cooperative wireless localization scheme based on information fusion is proposed. The scheme combines large-scale arrival time and small-scale distance measurements obtained from the next-generation converged networks. The maximum likelihood （ML） estimate of the terminal＇s position is derived with closed-form solution, and the Cramer-Rao lower bound （CRLB） of the estimate error is investigated. Both theoretical analysis and simulation results verify that the proposed localization scheme can significantly enhance the location precision. Moreover, the mean square error of position estimate approximates the CRLB when the number of reference stations increases, which indicates that the proposed ML estimator is asymptotically efficient.

Cooperative Nodes Localization for Three-Dimensional Underwater Wireless Sensor Network Based on Weighted Centroid Localization Algorithm

The underwater wireless sensor network(UWSN) has the features of mobility by drifting,less beacon nodes,longer time for localization and more energy consumption than the terrestrial sensor networks,which makes it more difficult to locate the nodes in marine environment.Aiming at the characteristics of UWSN,a kind of cooperative range-free localization method based on weighted centroid localization(WCL) algorithm for three-dimensional UWSN is proposed.The algorithm assigns the cooperative weights for the beacon nodes according to the received acoustic signal strength,and uses the located unknown nodes as the new beacon nodes to locate the other unknown nodes,so a fast localization can be achieved for the whole sensor networks.Simulation results indicate this method has higher localization accuracy than the centroid localization algorithm,and it needs less beacon nodes and achieves higher rate of effective localization.

Networking-GPS: Cooperative Vehicle Localization Using Commodity GPS in Urban Area

A challenging issue in intelligent transportation systems （ITS） is to accurately locate moving vehicles in urban area. Considerable ef- forts have been made to improve the localization accuracy of standalone GPS receivers. However, through empirical study, we found that the latitude and longitude values generated by GPS receivers fluctuate significantly because of the muhipath effect in urban ar- eas. The relative distances between neighboring vehicles with similar GPS signal data in terms of satellite sets and signal strength are much more stable in such a scenario. In this paper, we propose a cooperative localization algorithm, Networking-GPS, to improve the accuracy of location information for vehicular networks in urban area using commodity GPS receivers. First, atom redundantly rigid graphs of vehicles are constructed according to the similarity of neighboring GPS data. Then, through rigidity expansion, local accura- cy can enforce global accuracy. Extensive simulations based on the real road network and trace data of vehicle mobility demonstrate that Networking-GPS can improve the accuracy of the entire system.

Graph rigidity and localization of multi-robot formations

This paper provides theoretical foundation for the problem of localization in multi-robot formations. Sufficient and necessary conditions for completely localizing a formation of mobile robots/vehicles in SE(2) based on distributed sensor networks and graph rigidity are proposed. A method for estimating the quality of localizations via a linearized weighted least-squares algorithm is presented, which considers incomplete and noisy sensory information. The approach in this paper had been implemented in a multi-robot system of five car-like robots equipped with omni-directional cameras and IEEE 802.11b wireless network.

Consistent batch fusion for decentralized multi-robot cooperative localization

This paper investigates the problem of decentralized multi-robot cooperative localization.This problem involves collaboratively estimating the poses of a group of robots with respect to a common reference coordinate system using robot-to-robot relative measurements and intermittent absolute measurements in a distributed manner.To address this problem,we present a decentralized fusion method that enables batch updating to handle relative measurements from multiple robots simultaneously.This method can improve both the accuracy and computational efficiency of cooperative localization.To reduce communication costs and reliance on connectivity,we do not maintain the inter-robot state correlations.Instead,we adopt a covariance intersection(CI)technique to design an upper bound that replaces unknown joint correlations.We propose an optimization method to determine a tight upper bound for the correlations in the joint update.The consistency and convergence of our proposed algorithm is theoretically analyzed.Furthermore,we conduct Monte Carlo numerical simulations and real-world experiments to demonstrate that the proposed method outperforms existing approaches in terms of both accuracy and consistency.

Multi-robot mapping based on the adaptive differential evolution

Map building by multi-robot is very important to accomplish autonomous navigation,and one of the basic problems and research hotspots is how to merge the maps into a single one in the field of multi-robot map building.A novel approach is put forward based on adaptive differential evolution to map building for the multi-robot system.The multi-robot mapping-building system adopts the methods of decentralized exploration and concentrated mapping.The adaptive differential evolution algorithm is used to search in the space of possible transformation,and the iterative search is performed with the goal of maximizing overlapping regions.The map is translated and rotated so that the two maps can be overlapped and merged into a single global one successfully.This approach for map building can be realized without any knowledge of their relative positions.Experimental results show that the approach is effective and feasibile.

Operating in Tandem Chinese experts in Sao Tome and Principe cooperate with local farmers to raise medical standards in the breeding industry

Santomean pig farmer Simao Vicente was hopeful when he came to ask Zou Rui for help. His pig was suffering from hernia, and Zou, a 42-year-old Chinese agricultural expert working in Sao Tomé and Príncipe, was the only person on the island who could provide emergency surgery.

China-Africa Local Government Cooperation Essential for Growth

The First Forum on China-Africa Local Government Cooperation was held in Beijing on August 27-28, 2012. Vice President of the Chinese People＇s Association for Friendship with Foreign Countries （CPAFFC） Feng Zuoku, shared his insights on the Forum and China-Africa local-level cooperation with ChinAfrica reporter Yu Nan. Excerpts follow：

Topological Similarity-Based Multi-Target Correlation Localization for Aerial-Ground Systems

In this paper,an algorithm for solving the multi-target correlation and co-location problem of aerial-ground heterogeneous system is investigated.Aiming at the multi-target correlation problem,the fusion algorithm of visual axis correlation method and improved topological similarity correlation method are adopted in view of large parallax and inconsistent scale between the aerial and ground perspectives.First,the visual axis was preprocessed by the threshold method,so that the sparse targets were initially associated.Then,the improved topological similarity method was used to further associate dense targets with the relative position characteristics between targets.The shortcoming of dense target similarity with small di®erence was optimized by the improved topological similarity method.For the problem of colocation,combined with the multi-target correlation algorithm in this paper,the triangulation positioning model was used to complete the co-location of multiple targets.In the experimental part,simulation experiments and°ight experiments were designed to verify the e®ectiveness of the algorithm.Experimental results show that the proposed algorithm can e®ectively achieve multi-target correlation positioning,and that the positioning accuracy is obviously better than other positioning methods.

Distributed location model and algorithm with position uncertainty

The localization of multiple mobile terminals （MTs） is an encouraging paradigm of applications in wireless networks. Peer-to-peer communication between MTs facilitates the cooperative localization of multiple MTs. For sake of low complexity and high robustness, investigations often focus on the distributed algorithm in cooperative localization. However, the impact from position uncertainty of cooperative MT lacks of analysis in related works. A new distributed location model, as well as corresponding algorithm, is devised when considering both the distance measurement error and the position uncertainty of MTs, which is more judicious than the traditional model for distributed cooperative localization scenario. In addition, the performance of proposed algorithm is analyzed through Cramér-Rao lower bound （CRLB）. Simulations indicate that the algorithm outperforms traditional methods in terms of accuracy and robustness.