Cooperative Localization Algorithm Research Articles

Information-Rich Formation Tracking: A Unified Scheme of Cooperative Control and Localization

A group of unmanned aerial vehicles (UAVs) can efficiently obtain the information of an unknown target or location of interest. Efficacy of localization gets enhanced if UAVs form an optimal formation geometry around the target. This work presents a unified scheme of cooperative localization and tracking for a group of fixed-wing unmanned aerial vehicles. The sensors having different types and qualities are mounted on each UAV to form a heterogeneous sensing network. These UAVs are used to localize a moving target with unknown states. The performance of an unbiased estimator can be quantified by the Cramer–Rao lower bound, which is the same as the inverse of Fisher information. In this work, the conditions of optimal UAV formations are derived to maximize the Fisher information of the unknown target. Next, a generalized protocol is presented to result in the desired formations around the moving target that maximizes the information of the target. A cooperative localization algorithm is used to localize the unknown target states using the noisy measurements obtained from the heterogeneous sensing network. The efficacy of the proposed unified framework is illustrated with numerical examples.

Noncooperative Mobile Target Tracking Using Multiple AUVs in Anchor-Free Environments

The noncooperative target tracking is an important issue for the Internet of Underwater Things (IoUT). Autonomous underwater vehicles (AUVs) are preferred options to achieve the target tracking especially in anchor-free environments, where no equipments with known positions, named anchors, are deployed. The self-organized mobile network of multiple AUVs can localize and continuously monitor the target. Thus, in this article, we investigate the problem of the noncooperative target tracking using multiple AUVs in anchor-free environments. In the target tracking, AUVs play as references and their positions need to be estimated first. We propose a multi-AUV cooperative localization and target tracking (MCLTT) framework based on belief propagation (BP). Under MCLTT, BP-based underwater cooperative localization (BPUCL) and noncooperative mobile target tracking (NcMTT) algorithms are designed. Gaussian approximations are used to reduce communication costs among AUVs. The designed BPUCL alleviates the impact of the accumulated errors in the inertial measurements of AUVs and slows down the growth of the localization error. In NcMTT, model-free position prediction processes are proposed and a novel form of the particle-based BP message is designed using time-difference-of-arrival (TDOA) measurements. The simulation results validate the proposed algorithms by comparing with state-of-the-art methods.

Optimizing communication and computational costs based on the edge participation probability and the evolved variational message passing algorithm in wireless sensor networks

Cooperative localization has become an important issue for several applications. One of the most efficient techniques for cooperative localization is the Evolved Variational Message Passing (E-VMP) algorithm. This algorithm offers accurate position estimates with less computational complexity compared to other cooperative localization algorithms. What is more, communication cost is a challenging issue facing any localization technique which increases in loopy networks. Consequently, we propose in this paper a re-evaluated E-VMP version which can meet this challenge. We propound a specific Edge Participation Probability technique, namely EPP, for each edge in the considered network. This technique facilitates the distributed implementation without graph transformation which is highly adequate in loopy networks. Experiments and realistic simulations show that the proposed technique offers considerable localization accuracy for very low processing complexity and cost.

Integrity for Multi-Sensor Cooperative Positioning

A cooperative integrity monitoring (CIM) algorithm is proposed in this work. Under the CIM architecture, the algorithm can fully exploit the global navigation satellite system (GNSS) data and inter-vehicle measurements data to improve the detection and isolation of faulty measurements due to multipath or non line of sight (NLOS). Taking the advantages of cooperative scheme, a residual decomposition method is used to model the measurement errors into common and specific parts, a greedy search strategy is used to exclude the faulty measurements based on its sub-statistics. Simulation results show that CIM has better detection of GNSS fault than traditional receiver autonomous integrity monitoring (RAIM). Also, CIM is capable of detecting the faulty outliers in inter-vehicle measurements. The results indicate that CIM can be applied to many existing multi-sensor cooperative positioning algorithms.

Cloud-Assisted Cooperative Localization for Vehicle Platoons: A Turbo Approach

Due to the high resolution of angles of arrivals (AoAs) provided by the massive MIMO base station in 5 G wireless systems, it is promising to integrate 5G-based localization technology into autonomous driving to improve the accuracy and robustness of vehicle localization. In this paper, we investigate the problem of 5G cloud-assisted cooperative localization for vehicle platoons. The existing 5G-based localization algorithms focused on single-user localization and are not efficient for the localization of vehicle platoon where the positions of the vehicles are highly correlated. To the best of our knowledge, cloud-assisted cooperative localization tailored to vehicle platoons has not been studied before. To address this challenging problem, we first propose a Gamma-Markov-Group-Sparse (GMGS) model to capture the joint distribution of the vehicle positions in a vehicle platoon. Then we formulate the vehicle platoon cooperative localization as a sparse Bayesian inference (SBI) problem. The existing standard SBI algorithms such as variational Bayesian inference (VBI) and approximate message passing (AMP) cannot be applied to our platoon localization problem due to the complicated GMGS prior and the ill-conditioned measurement matrix. As such, we propose a novel turbo vehicle platoon cooperative localization (Turbo-VPCL) algorithm to fully exploit the correlations of the vehicle positions (as captured by the GMGS prior) under the ill-conditioned measurement matrix. Simulation results verify that the proposed Turbo-VPCL can achieve significant gain over the-state-of-art SBI algorithms.

An Extended Joint Spatial and Temporal Cooperation Model for the Range-Based Localization Problem

Location awareness is becoming essential for various wireless applications, and cooperation among nodes is highly beneficial for localization performance. However, the existing cooperative localization (CL) algorithms may suffer from major performance loss when applied to the mobile environments with fast changing network topologies. In this paper, we extend the joint spatial and temporal cooperation (JSTC) model to the range-based localization problem. Based on mobility constraints and the maximum likelihood estimation (MLE) criterion, we formulate the CL problems in both centralized and distributed cases. Different from the conventional distributed CL problems, the proposed one takes the transmission delay and the cooperation among multi-hop nodes into account, which is more practical. Moreover, the performance limits of the considered problems are investigated by the Cramer-Rao lower bound (CRLB). Finally, these two problems are solved by the Sequential Quadratic Programming (SQP) algorithm. Both simulation and experiment results show that the extended JSTC model can effectively improve the localization reliability and accuracy.

Hybrid Cooperative Positioning for Vehicular Networks

This paper proposes a hybrid cooperative positioning (CP) algorithm suitable for vehicular network applications which can fuse the measurements from global navigation satellites, ground stations, signals of opportunity, inter-node ranging from neighbouring vehicles and onboard inertial navigation systems (INS). By applying the framework of generalized approximate message passing (GAMP), the complex CP problem is transformed into an iterative yet lower computational load process. In each iteration, the time recurrence of navigation states and initialization of GAMP computation are conducted based on Kalman filter. The proposed algorithm guarantees the overall positioning performance of multiple vehicles in a hybrid navigation scenario, and improves the robustness and accuracy of CP navigation systems. Simulation results show that the proposed algorithm has better estimation accuracy than traditional CP algorithms, and has 20 times less computational load than the best existing algorithm with equivalent accuracy.

Sampling-Based Path Planning for Cooperative Autonomous Maritime Vehicles to Reduce Uncertainty in Range-Only Localization

The following letter presents an adaptive path planning algorithm for cooperative localization in the maritime environment. It considers the scenario where an Autonomous Surface Vehicle (ASV) acts as a Communication and Navigation Aid (CNA) to support Autonomous Underwater Vehicles (AUVs) with range measurements. As AUVs have no access to GPS while submerged, range measurements can bind the otherwise continuously growing navigational error. This can be done by methods such as range-only Extended Kalman Filter (EKF). In such methods, the resulting uncertainty and positional error depends on the geometry between transmitter (CNA) and receiver (AUV). This letter proposes a planning algorithm that combines priority based expansion of a search tree with random sampling-based exploration to position the CNA at strategic positions to transmit ranging messages at optimal times to reduce the uncertainty and error at the AUVs’ position. The approach is validated and shows an increased confidence for AUVs’ localization in simulated environments as well as real experiments using a dataset gathered from AUV Sirius.

A Hybrid CRBP-VMP Cooperative Positioning Algorithm for Distributed Multi-UAVs

Positioning information plays a significant role in multi-unmanned aerial vehicles (UAVs) applications. Traditionally, the positioning information is widely provided by Global Navigation Satellite System (GNSS) due to its good performance and global coverage. However, owing to complicated flight environment or signal blockage, jamming and unintentional interference, the UAVs may fail to locate themselves by using GNSS alone. As a new method to resolve these problems, cooperative positioning, by incorporating peer-to-peer range measurements and assisted information, has attracted more and more attentions due to its ability to enhance the accuracy and availability of positioning. However, achieving good performance of cooperative positioning of multi-UAVs is challenging as their mobility, arbitrary nonlinear state-evolution, measurement models and limited computation and communication resources. In this paper, we present a factor graph (FG) representation and message passing methodology to solve cooperative positioning problem among UAVs in 3-dimensional environment where GNSS cannot provide services. Moreover, to deal with the nonlinear state-evolution and measurement models while decreasing the computation complexity and communication cost, we develop a distributed algorithm for dynamic and hybrid UAVs by means of Spherical-Radial Cubature Rules (CR) method with belief propagation (BP) and variational message passing (VMP) methods (CRBP-VMP) on the FG. The proposed CRBP deals with the highly non-linear state-evolution models and non-Gaussian distributions, the VMP method is employed for ranging message, gets the simpler message representation and can reduce communication cost in the joint estimation problem. Simulation results demonstrate that the higher positioning accuracy, the better convergence as well as low computational complexity and communication cost of the proposed CRBP-VMP algorithm, which can be achieved compared with sum-product algorithm over a wireless network (SPAWN) and traditional Cubature Kalman Filters (CKF) method.

Cooperative Localization Approach for Multi-Robot Systems Based on State Estimation Error Compensation.

In order to improve the localization accuracy of multi-robot systems, a cooperative localization approach with communication delays was proposed in this paper. In the proposed method, the reason for the time delay of the robots’ cooperative localization approach was analyzed first, and then the state equation and measure equation were reconstructed by introducing the communication delays into the states and measurements. Furthermore, the cooperative localization algorithm using the extended Kalman filtering technique based on state estimation error compensation was proposed to reduce the state estimation error of delay filtering. Finally, the simulation and experiment results demonstrated that the proposed algorithm can achieve good performance in location in the presence of communication delay while having reduced computational and communicative cost.

A Multitag Cooperative Localization Algorithm Based on Weighted Multidimensional Scaling for Passive UHF RFID

Radio frequency identification (RFID) technology, which is one of the important implementation ways of Internet-of-Things (IoT), has achieved much attention in indoor localization areas. Passive ultrahigh frequency (UHF) RFID tag localization has a great development recently. Most of traditional passive UHF RFID localization algorithms can only achieve the position of one tag at a time while multitag localization is desired in many RFID applications. In this paper, we proposed the weighted multidimensional scaling (WMDS) based on received signal strength (RSS) method and the tag-to-tag communication system to achieve multitag cooperative localization. The targets are marked with passive UHF RFID tags. RSS method is used to determine the distance between readers and target tags through channel models. We can also obtain the estimated distances between target tags by the tag-to-tag communication system. The estimated locations of the tags are determined by the calculation of Euclidean distance matrix through a few iterations. Simulation results show that the WMDS algorithm achieves higher localization accuracy than traditional algorithms and the cooperation between tags improves the localization accuracy.

Simulation Tool for the Analysis of Cooperative Localization Algorithms for Wireless Sensor Networks.

Within the context of the Internet of Things (IoT) and the Location of Things (LoT) service, this paper presents an interactive tool to quantitatively analyze the performance of cooperative localization techniques for wireless sensor networks (WSNs). In these types of algorithms, nodes help each other determine their location based on some signal metrics such as time of arrival (TOA), received signal strength (RSS), or a fusion of them. The developed tool is intended to provide researchers and designers a fast way to measure the performance of localization algorithms considering specific network topologies. Using TOA or RSS models, the Crámer-Rao lower bound (CRLB) has been implemented within the tool. This lower bound can be used as a benchmark for testing a particular algorithm for specific channel characteristics and WSN topology, which allows determination if the necessary accuracy for a specific application is possible. Furthermore, the tool allows us to consider independent characteristics for each node in the WSN. This feature allows the avoidance of the typical “disk graph model,” which is usually applied to test cooperative localization algorithms. The tool allows us to run Monte-Carlo simulations and generate statistical reports. A set of basic illustrative examples are described comparing the performance of different localization algorithms and showing the capabilities of the presented tool.

Robust Least-SquareLocalization Based on Relative Angular Matrix in Wireless Sensor Networks.

Accurate position information plays an important role in wireless sensor networks (WSN), and cooperative positioning based on cooperation among agents is a promising methodology of providing such information. Conventional cooperative positioning algorithms, such as least squares (LS), rely on approximate position estimates obtained from prior measurements. This paper explores the fundamental mechanism underlying the least squares algorithm’s sensitivity to the initial position selection and approaches to dealing with such sensitivity. This topic plays an essential role in cooperative positioning, as it determines whether a cooperative positioning algorithm can be implemented ubiquitously. In particular, a sufficient and unnecessary condition for the least squares cost function to be convex is found and proven. We then propose a robust algorithm for wireless sensor network positioning that transforms the cost function into a globally convex function by detecting the null space of the relative angle matrix when all the targets are located inside the convex polygon formed by its neighboring nodes. Furthermore, we advance one step further and improve the algorithm to apply it in both the time of arrival (TOA) and angle of arrival/time of arrival (AOA/TOA) scenarios. Finally, the performance of the proposed approach is quantified via simulations, and the results show that the proposed method has a high positioning accuracy and is robust in both line-of-sight (LOS) and non-line-of-sight (NLOS) positioning environments.

Cooperative Localization Under Limited Connectivity

In this article, we report two decentralized multiagent cooperative localization algorithms in which, to reduce the communication cost, interagent state estimate correlations are not maintained but accounted for implicitly. In our first algorithm, to guarantee filter consistency, we account for unknown interagent correlations via an upper bound on the joint covariance matrix of the agents. In the second method, we use an optimization framework to estimate the unknown interagent cross-covariance matrix. In our algorithms, each agent localizes itself in a global coordinate frame using a local filter driven by local dead reckoning and occasional absolute measurement updates, and opportunistically corrects its pose estimate whenever it can obtain relative measurements with respect to other mobile agents. To process any relative measurement, only the agent that has taken the measurement and the agent the measurement is taken from need to communicate with each other. Consequently, our algorithms are decentralized algorithms that do not impose restrictive network-wide connectivity condition. Moreover, we make no assumptions about the type of agents or relative measurements. We demonstrate our algorithms in simulation and a robotic experiment.

An Advanced Cooperative Positioning Algorithm Based on Improved Factor Graph and Sum-Product Theory for Multiple AUVs

In this research, a novel autonomous underwater vehicle (AUV) cooperative positioning algorithm is proposed to solve the implementation problem of multi-sensor-fusion applications. Different from the traditional methods [i.e., the extended Kalman filter (EKF), unscented Kalman filter (UKF), and iteration extended Kalman filter (IEKF)], which have large linearity error under the condition of nonlinear observation equation when multiple AUV are cooperative positioning, the proposed algorithm utilized the Baysis filter to solve the AUV cooperative problem. Factor graph and sum-product (FGS)-based cooperative positioning algorithm is established to mathematically implement the Bayse filter by converting the global function estimation problem into a local function sum-product estimation problem. Furthermore, to improve the performance of the proposed algorithm, a robust data processing method is presented by introducing a transform matrix to the estimated position information. To demonstrate and verify the proposed methods, the simulation and real tests in different scenarios are performed in this research. Compared with the traditional EKF, UKF, and IEKF cooperative positioning algorithm, the positioning error of the proposed improved FGS (IFGS) cooperative positioning algorithm is obviously smaller than that of the other three algorithms. Moreover, the IFGS algorithm can reduce the complexity of the algorithm, available improving the computational speed of the whole system. This proposed algorithm has important theoretical and practical value for the both industry and academic areas.

Cooperative Positioning for Multi-AUVs Based on Factor Graph and Maximum Correntropy

Cooperative positioning (CP) is considered as a promising positioning method for multiple autonomous underwater vehicles (multi-AUVs), because CP is characterized by low cost and high precision. In this research, a novel autonomous underwater vehicle (AUV) CP algorithm is proposed to enhance the global localization accuracy of the follower AUV. However, in traditional CP algorithm, the positioning error is large under the condition that the outlier data exists in the observation, which happens commonly. So in this research, a novel CP algorithm based on the factor graph and maximum correntropy(FGMC) for AUV is proposed to enhance the global localization accuracy of the AUV. Different from the traditional algorithms, this presented FGMC-based CP algorithm implements mathematically the Bayes filter by converting the global function estimation problem into the local one. And furthermore, the maximum correntropy is used as the cost function in the factor graph to estimation problem, this can reduce the influence of outliers on positioning accuracy. FGMC based cooperative positioning algorithm is established to mathematically implement the Bayes filter by converting the global function estimation problem into local function estimation problem. Furthermore, the maximum correntropy is used as the cost function in the factor graph to estimate the variables. To demonstrate and verify the proposed algorithm, simulation and real tests in different scenarios are performed in this research. Compared with the traditional CP algorithms, the positioning error of the proposed FGMC cooperative positioning algorithm is obviously smaller than that of the other algorithms.

Adaptive Cooperative Localization Using Relative Position Estimation for Networked Systems With Minimum Number of Communication Links

In this paper, a novel solution for cooperative localization problem involving a network with multiple mobile agents accessing to only one beacon agent is presented. The solution can be applied to a network with a minimum number of communication links (subjecting the network topology to be an undirected spanning tree). The objective is to provide each agent in the network with information on its absolute position without using any on-board global positioning sensors such as GPS receivers. The proposed solution uses an adaptive relative position estimation algorithm for each pair of neighboring mobile agents. The proposed estimation algorithm requires each agent to measure the relative inter-agent distance. The local velocity vector is also measured and transmitted to the neighboring agents. The estimation mechanism incorporating a signum function outperforms the recently established relative position estimation algorithms, in terms of positioning and tracking errors. An adaptive cooperative localization (ACL) algorithm is formed by augmenting the relative position estimation in a cooperative observer scheme suitably applicable for accomplishing localization task involving a network of mobile agents. The salient feature of the proposed ACL algorithm is that the communication graph among the agents needs only to have one undirected path between two agents in the network. Such convenience promotes easy practical implementation and lite computation for each agent. The proof of the proposed algorithm is provided using the Lyapunov stability theorem. Three simulation case studies are presented to evaluate the performance of the solution in different scenarios, including the stationary and moving beacon agent as well as the non-cooperatively controlled and cooperatively controlled network of mobile agents. The comparative studies reveal that the ACL algorithm is superior to the recently investigated linear-convex algorithm. The number of communication links required for the localization task to be carried out by the proposed algorithm is minimum, thus promoting a more preferable energy-efficient solution.

Robust Distributed Cooperative Localization With NLOS Mitigation Based on Multiplicative Convex Model

The accuracy of cooperative localization degrades significantly in the non-line-of-sight (NLOS) environments. In addition, the computational complexity of the localization problem often increases dramatically as the scale of a wireless sensor network (WSN) grows. To address these challenges, we propose a distributed NLOS cooperative localization algorithm. First, we propose a new multiplicative model based on the physical mechanism of the NLOS propagation and relax the proposed non-convex model into its convex envelope. This model has a powerful capability to mitigate the NLOS impact and remarkable robustness in changing environments. Second, we design a redundant formulation to decompose the convex problem into numerous sub-problems and then develop an efficient distributed algorithm, which enables each sensor node to locally solve each sub-problem in a parallel way, to decrease the computational complexity. The theoretical analysis and simulations show that the proposed algorithm is superior to the existing methods in both processing speed and localization accuracy.

Applications of intelligent computing in vehicular networks

Purpose This paper aims to introduce vehicular network platform, routing and broadcasting methods and vehicular positioning enhancement technology, which are three aspects of the applications of intelligent computing in vehicular networks. From this paper, the role of intelligent algorithm in the field of transportation and the vehicular networks can be understood. Design/methodology/approach In this paper, the authors introduce three different methods in three layers of vehicle networking, which are data cleaning based on machine learning, routing algorithm based on epidemic model and cooperative localization algorithm based on the connect vehicles. Findings In Section 2, a novel classification-based framework is proposed to efficiently assess the data quality and screen out the abnormal vehicles in database. In Section 3, the authors can find when traffic conditions varied from free flow to congestion, the number of message copies increased dramatically and the reachability also improved. The error of vehicle positioning is reduced by 35.39% based on the CV-IMM-EKF in Section 4. Finally, it can be concluded that the intelligent computing in the vehicle network system is effective, and it will improve the development of the car networking system. Originality/value This paper reviews the research of intelligent algorithms in three related areas of vehicle networking. In the field of vehicle networking, these research results are conducive to promoting data processing and algorithm optimization, and it may lay the foundation for the new methods.

Cooperative Localization Algorithm Based on Hybrid Topology Architecture for Multiple Mobile Robot System

With the development of robotics, the multiple mobile robot system (MMRS) has been an attention-getting subject in recent years and has been widely used in military, civilian and other fields. Cooperative localization is the key technology of the MMRS. And observability analysis for MMRS and nonlinear problem are the foremost problems to cooperative localization. In order to enhance the accuracy of cooperative localization for MMRS, a novel cooperative localization algorithm based on hybrid topology architecture under the framework of extended Kalman filter (EKF) is proposed in this paper. In this novel algorithm, a hybrid topology architecture based on the relative position measurement graph method is adopted to enhance the usage efficiency of observations, improving the cooperative localization accuracy. Meanwhile, EKF is used in this novel cooperative localization algorithm to solve the nonlinearity of the MMRS. Finally, a real dataset is used to verify the availability and superiority of this novel algorithm.