Cooperative Target Localization Research Articles

A Cooperative Target Localization Method Based on UAV Aerial Images

A passive localization algorithm based on UAV aerial images and Angle of Arrival (AOA) is proposed to solve the target passive localization problem. In this paper, the images are captured using fixed-focus shooting. A target localization factor is defined to eliminate the effect of focal length and simplify calculations. To synchronize the positions of multiple UAVs, a dynamic navigation coordinate system is defined with the leader at its center. The target positioning factor is calculated based on image information and azimuth elements within the UAV photoelectric reconnaissance device. The covariance equation is used to derive AOA, which is then used to obtain the target coordinate value by solving the joint UAV swarm positional information. The accuracy of the positioning algorithm is verified by actual aerial images. Based on this, an error model is established, the calculation method of the co-localization PDOP is given, and the correctness of the error model is verified through the simulation of the Monte Carlo statistical method. At the end of the article, the trackless Kalman filter algorithm is designed to improve positioning accuracy, and the simulation analysis is performed on the stationary and moving states of the target. The experimental results show that the algorithm can significantly improve the target positioning accuracy and ensure stable tracking of the target.

Coordinated target localization and tracking with node scheduling for a network of autonomous underwater vehicles

Underwater mobile sensor networks (UMSNs) have the advantages of large area coverage, flexible network structure, and good local resolution. Particularly, spatial diversity can be exploited through node mobility to get more target information. To take those advantages in the context of regional cooperative target localization and tracking, a joint node scheduling and acoustic model fusion method is proposed for an UMSN consisting of multiple autonomous underwater vehicles (AUVs). First, a target tracking algorithm framework based on the random finite set theory is established. To take the acoustic environment into account, the a priori acoustic field and target distribution information are combined to evaluate the detection probability. A path-planning scheme with node scheduling is then developed based on the information gain to optimize the detection performance. To achieve effective node scheduling, three reward functions with different information gains respectively derived from the Rényi divergence and Fisher information matrix are proposed and compared. The rationale and the performance of the developed method are verified via simulations. A simplified version of the approach is also tested in field experiments with three AUVs, verifying the effectiveness of node scheduling in a realistic scenario.

A monocular visual single-axis rotation measurement method of multi-rudders based on cooperative targets

To tackle the problem that the rotation angle of multiple rudders needs to be measured in real time and high precision, a measurement method based on cooperative targets is proposed in this paper. Firstly, to detect and encode multiple similar rudders with sparse feature points in complex backgrounds, we design a novel visual cooperative target with encoding information, and study a cooperative target localization algorithm based on contour extraction as well as a corner point extraction algorithm based on a checkerboard grid. Then, through the PnP algorithms and numerous corner points extracted from the cooperative target, an unconstrained optimization model is developed based on the re-projection error, and the Levenberg-Marquard (LM) algorithm is applied to estimate the rotation angle. Finally, an experiment platform is set up to evaluate the accuracy and speed of the algorithm. Compared with the existing methods, the experimental results show that the proposed cooperative target exhibits favorable localization under various typical experimental environments with a detection success rate better than 98%, meanwhile, the actual measurement accuracy of the angle estimation model is better than 0.05° and the average execution time of each measurement is less than 20 ms. Moreover, the proposed method is easy to install and operate in actual measurement, and can achieve simultaneous measurement of the spatial rotation angle of multiple rudders, which is well suited to actual industrial production needs.

A Novel Airspace Planning Algorithm for Cooperative Target Localization

With the development of modern electromagnetic stealth technology and ARM, traditional active radar detection cannot accomplish its detection mission, limited by its ability. Relying on such superior advantages such as imperceptibility, anti-electromagnetic interference and electromagnetic stealth, passive transducers are playing an indispensable and significant role in situation awareness. While, in addition to different passive transducer localization modes and solutions of target’s location, the reasonable planning and optimal layout of passive transducers’ location are other major factors affecting the precision of localization. Planning an optimal airspace for passive transducers is the key problem to improve the monitoring efficiency. This paper proposes the optimal layout algorithm for the cooperative platform in the space based on the geometrical relationship of cooperative localization. For example, the principle of direction location in traditional methods is simple: only two passive sensors can work, but the location accuracy of long-distance targets is low. At the same time, TDOA (Time Difference Of Arrival) location has high accuracy and good stability, but it needs at least three passive sensors to work together, which requires the most resources. In this paper, a platform optimization layout algorithm based on direction and TDOA hybrid positioning is proposed. Compared with direction positioning, it improves the long-distance positioning accuracy, reduces the number of sensors required for TDOA positioning, and reduces the resource occupancy rate. However, the TDOA positioning data mixed with direction positioning data inevitably leads to the decline of overall accuracy. In order to solve these difficulties, the weighted least square method is used to optimize the accuracy. The simulation shows that, within the designated target airspace, the optimal action airspace can be generated automatically based on the platforms’ cooperation mode. If there is no resource limitation, the airspace planning based on TDOA positioning has the highest accuracy for the target. However, in practical application, considering the resource limitation, the hybrid positioning of direction and TDOA can also meet the requirements of high accuracy and high stability. The average error is reduced by more than 45% compared with direction positioning, and the airspace occupancy is reduced by more than 30% compared with TDOA positioning. The goal of minimizing the scope of platform airspace planning is realized.

Vision-aware air-ground cooperative target localization for UAV and UGV

In the moving target localization, due to the influence of image distortion on unmanned aerial vehicle (UAV) and the limitation of field of vision on unmanned ground vehicle (UGV), the localization accuracy of single domain is low. Therefore, the air-ground cooperation has become a new trend and inevitable choice for the target localization. In this context, the accurate recognition and organization cooperation are the essential but challenging tasks. This study devotes to improving the capability of air-ground cooperative target localization by the multi-feature fusion recognition in the outdoor environment. To be specific, a novel cooperative architecture is designed to control the observation locations of the UAV and the UGV throughout the localization process. For the heterogeneity of Unmanned Vehicle (UV), we creatively introduce the target recognition rate of the UGV as decision factor and use the target motion property to guarantee the accuracy of the target localization. Based on this architecture, we propose a BidiRectional feedbAck VErification algorithm, BRAVE, whose includes multi-layer decision to ensure that each UV can be in the optimal location for positioning the target, the proposed BRAVE skillfully integrates both the target recognition and cooperation decision. To demonstrate the superiority of our method, we devise several groups of experiments to analyze the performance of BRAVE in the outdoor environment. The experimental results demonstrate that the localization error of BRAVE is only 1.61 m. Moreover, the accuracy is also improved by 0.5 m compared with the air-air cooperation.

3D target localization based on multi–unmanned aerial vehicle cooperation

Ground target three-dimensional positions measured from optical remote-sensing images taken by an unmanned aerial vehicle play an important role in related military and civil applications. The weakness of this system lies in its localization accuracy being unstable and its efficiency being low when using a single unmanned aerial vehicle. In this paper, a novel multi–unmanned aerial vehicle cooperative target localization measurement method is proposed to overcome these issues. In the target localization measurement stage, three or more unmanned aerial vehicles simultaneously observe the same ground target and acquire multiple remote-sensing images. According to the principle of perspective projection, the target point, its image point, and the camera’s optic center are collinear, and nonlinear observation equations are established. These equations are then converted to linear equations using a Taylor expansion. Robust weighted least-squares estimation is used to solve the equations with the objective function of minimizing the weighted square sum of re-projection errors from target points to multiple pairs of images, which can make the best use of the effective information and avoid interference from the observation data. An automatic calculation strategy using a weight matrix is designed, and the weight matrix and target-position coordinate value are updated in each iteration until the iteration stopping condition is satisfied. Compared with the stereo-image-pair cross-target localization method, the multi–unmanned aerial vehicle cooperative target localization method can use more observation information, which results in higher rendezvous accuracy and improved performance. Finally, the effectiveness and robustness of this method is verified by numerical simulation and flight testing. The results show that the proposed method can effectively improve the precision of the target’s localization and demonstrates great potential for providing more accurate target localization in engineering applications.

Decentralized Consensus Based Target Localization in Wireless Sensor Networks

Target localization is an attractive subject for modern systems that utilize different types of distributed sensors for location based services such as navigation, public transport, retail services and so on. Target localization could be performed in both centralized and decentralized manner. Due to drawbacks of centralized systems such as security and reliability issues, decentralized systems are become more desirable. In this paper, we introduce a new decentralized and cooperative target localization algorithm for wireless sensor networks. In cooperative consensus based localization, each sensor knows its own location and estimates the targets position using the ranging techniques such as received signal strength. Then, all nodes cooperate with their neighbours and share their information to reach a consensus on targets location. In our proposed algorithm, we weight the received information of neighbour nodes according to their estimated distance toward the target node. Simulation results confirm that our proposed algorithm is faster, less sensitive to targets location and improves the localization accuracy by 85% in comparison with distributed Gauss–Newton algorithm.

Range-based cooperative underwater target localization

The paper addresses the problem of range-based cooperative underwater target localization. In its simplest form, target localization aims to estimate the position of a stationary or mobile target by using range measurements between the target and a vehicle, called a tracker, undergoing a trajectory that can be measured on-line. In this context, the tracker must execute sufficiently exciting maneuvers so as to maximize the range-based information available for target localization. The paper extends this set-up to the case where the tracker works in cooperation with another vehicle, called companion, that can also measure ranges to the target and shares this info with the tracker. The latter may have access to the position of the companion or, in some cases, only to the range between the two vehicles. We consider three different operating scenarios where the motion of the tracker is chosen so as to increase the accuracy with which the position of the target can be estimated. The scenarios reflect the situations where the motion of the companion vehicle satisfies one of three conditions: i) the motion is not defined a priori and can also be optimized, ii) the motion is fixed a priori and is known to the tracker (scenario in which the tracker benefits from the extra information acquired by the companion vehicle, which tracks a desired trajectory in the context of a separate, independent mission), and iii) the motion is not known a priori and must be learned in the course of the mission. By adopting an estimation-theoretical settings, optimal trajectories are computed by maximizing the determinant of an appropriately defined Fisher information matrix (FIM), subjected to vehicle maneuvering constraints and inter-vehicle collision avoidance. Simulation results illustrate the methodology adopted for cooperative target localization.

Robust, fast and accurate vision-based localization of a cooperative target used for space robotic arm

When a space robotic arm deploys a payload, usually the pose between the cooperative target fixed on the payload and the hand-eye camera installed on the arm is calculated in real-time. A high-precision robust visual cooperative target localization method is proposed. Combing a circle, a line and dots as markers, a target that guarantees high detection rates is designed. Given an image, single-pixel-width smooth edges are drawn by a novel linking method. Circles are then quickly extracted using isophotes curvature. Around each circle, a square boundary in a pre-calculated proportion to the circle radius is set. In the boundary, the target is identified if certain numbers of lines exist. Based on the circle, the lines, and the target foreground and background intensities, markers are localized. Finally, the target pose is calculated by the Point-3-Perspective algorithm. The algorithm processes 8 frames per second with the target distance ranging from 0.3m to 1.5m. It generated high-precision poses of above 97.5% on over 100,000 images regardless of camera background, target pose, illumination and motion blur. At 0.3m, the rotation and translation errors were less than 0.015° and 0.2mm. The proposed algorithm is very suitable for real-time visual measurement that requires high precision in aerospace.

Cooperative target localization using multiple UAVs with out-of-sequence measurements

PurposeThe time delay would occurs when the measurements of multiple unmanned aerial vehicles (UAVs) are transmitted to the date processing center during cooperative target localization. This problem is often named as the out-of-sequence measurement (OOSM) problem. This paper aims to present a nonlinear filtering based on solving the Fokker–Planck equation to address the issue of OOSM.Design/methodology/approachAccording to the arrival time of measurement, the proposed nonlinear filtering can be divided into two parts. The non-delay measurement would be fused in the first part, in which the Fokker–Planck equation is utilized to propagate the conditional probability density function in the forward form. The time delay measurement is fused in the second part, in which the Fokker–Planck is used in the backward form approximately. The Bayes formula is applied in both parts during the measurement update.FindingsUnder the Bayesian filtering framework, this nonlinear filtering is not only suitable for the Gaussian noise assumption but also for the non-Gaussian noise assumption. The nonlinear filtering is applied to the cooperative target localization problem. Simulation results show that the proposed filtering algorithm is superior to the previous Y algorithm.Practical implicationsIn this paper, the research shows that a better performance can be obtained by fusing multiple UAV measurements and treating time delay in measurement with the proposed algorithm.Originality/valueIn this paper, the OOSM problem is settled based on solving the Fokker–Planck equation. Generally, the Fokker–Planck equation can be used to predict the probability density forward in time. However, to associate the current state with the state related to OOSM, it would be used to propagate the probability density backward either.

Distributed cooperative target detection and localization in decentralized wireless sensor networks

In many network applications such as surveillance systems, it is crucial to detect the target and estimate its location. Distributed processing algorithms are capable of providing fast, secure, sca...

Cooperative Improved Target Localization in harsh Environments using Direction of Arrival

<p>Target localization is an important issue for many applications in wireless sensor networks. However, it is rather difficult to maintain the localization accuracy in mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environments as NLOS propagation leads to larger error than what LOS does. In this paper, we propose a new target localization method in mixed environments where NLOS is dominant and only one base node might be in LOS toward target. We use the cooperation between receiver nodes and the direction of arrival (DOA) of received signals to estimate the target’s location. The proposed cooperative target localization method tries to identify a base node that has LOS with respect to target node and use the LOS information for precise positioning of target node. We simulate the proposed method to analyze its performance. Simulation results confirm that our proposed method improves the localization accuracy on average by 20 percent in comparison with traditional cooperative methods.</p>

Security Vulnerabilities and Countermeasures for Target Localization in Bio-NanoThings Communication Networks

The emergence of molecular communication has provided an avenue for developing biological nanonetworks. Synthetic biology is a platform that enables reprogramming cells, which we refer to as Bio-NanoThings, that can be assembled to create nanonetworks. In this paper, we focus on specific Bio-NanoThings, i.e, bacteria, where engineering their ability to emit or sense molecules can result in functionalities, such as cooperative target localization. Although this opens opportunities, e.g., for novel healthcare applications of the future, this can also lead to new problems, such as a new form of bioterrorism. In this paper, we investigate the disruptions that malicious Bio-NanoThings (M-BNTs) can create for molecular nanonetworks. In particular, we introduce two types of attacks: 1) blackhole and 2) sentry attacks. In blackhole attack M-BNTs emit attractant chemicals to draw-in the legitimate Bio-NanoThings (L-BNTs) from searching for their target, while in the sentry attack, the M-BNTs emit repellents to disperse the L-BNTs from reaching their target. We also present a countermeasure that L-BNTs can take to be resilient to the attacks, where we consider two forms of decision processes that includes Bayes’ rule as well as a simple threshold approach. We run a thorough set of simulations to assess the effectiveness of the proposed attacks as well as the proposed countermeasure. Our results show that the attacks can significantly hinder the regular behavior of Bio-NanoThings, while the countermeasures are effective for protecting against such attacks.

Probabilistic Cooperative Target Localization

This work addresses the problem of localizing a ground target observed by multiple heterogeneous unmanned vehicles. Specifically, the case of a team of unmanned aerial and ground vehicles is analyzed. Effective collaboration between unmanned aerial and ground vehicles can utilize the strengths of both platforms while mediating their individual weaknesses. In this research, a probabilistic framework is proposed to improve target localization accuracy by utilizing the epipolar geometry and inter-robot localization measurements. A virtual simulation environment is implemented to fully test the proposed methods. Hardware experiments are carried out to validate the proposed methods.

Cooperative Target Localization and Tracking with Incomplete Measurements

This study investigates a problem on target localization and tracking for two cases where either the slant range information of dual stations is lost or the slant range information of one station and the pitch angle information of the other one are missing. The models of cooperative localization with incomplete measurements are presented and the Kalman filtering algorithm is applied for target tracking. For improving tracking precision, a strategy of observers path planning based on the gradient of circular error probability (CEP) is integrated into the Kalman filtering algorithm. Several numerical examples are used to illustrate the tracking performance of the proposed algorithm with the corresponding root mean square error (RMSE) and Cramer-Rao lower bound (CRLB). The Monte Carlo simulation results validate the effectiveness of the presented algorithm.

Distributed Full-State Observers With Limited Communication and Application to Cooperative Target Localization

We present a fully decentralized motion control algorithm for the coordination of platoons of mobile agents with highly restricted communication capabilities. In order to address very low bandwidth communication between agents and time varying communication network topologies, we utilize a distributed full-state observer onboard each agent. Agent motion and data fusion algorithms are implemented locally by each agent based on the state of the local full-state estimator. Although no separation principle exists between decentralized agent motion control and distributed data fusion in general, we introduce a gradient-based framework in which simultaneously we achieve asymptotic agreement among full-state estimators and convergence of desired agent motion.

Cooperative Target Localization with a Communication-Aware Unmanned Aircraft System

This paper presents an approach to generating information-gathering trajectories for a cooperative unmanned aircraft system that takes into account the reliability of multihop networked communication. The algorithms presented here implicitly consider the communication limitations of the aircraft within a path-planning algorithm in which there is a position dependency on both the sensing and communication capabilities of the network. A communication-aware performance metric is derived by combining the extended information filter with a packet- erasure channel model and stochastic model of packet routing. Informative trajectories that maximize this metric are generated by a distributed, hierarchical planning algorithm. Flight results are used to characterize the communication channel model for use in a multivehicle simulation. Finally, simulation studies are used to evaluate the path-planning approach through the specific application of stationary WiFi source localization. These studies show that the approach leads to improved estimation performance relative to path-planning that does not consider communication reliability. Further, the approach leads to the emergence of natural roles whereby some of the aircraft contribute largely to the sensing portion of the objective, while others improve communication reliability by serving mainly as communication relays.

Cooperative target localization by multiple unmanned aircraft systems using sensor fusion quality

Over the past several years, researchers at the U.S. Air Force Academy developed cooperative, distributed aerial sensor networks (Pack et al. in IEEE Trans Syst Man Cybern Part B Cybern 39(4):959–970, 2009) using multiple small unmanned aerial vehicles (SUAVs) to search, detect, and locate ground targets. The use of distributed SUAVs, however, introduced a set of problems, including difficulties in reliable air-to-air communication and clock synchronization among onboard systems of multiple SUAVs. The communication problems further aggravate the synchronization problem contributing to a large target localization error. Conventional methods use multiple sensor outputs of the same target seen from different perspectives to increase the target localization accuracy. These methods are effective only when the pose errors of sensor platforms based on GPS data are modeled accurately, which is not a reasonable assumption for SUAVs, especially when SUAVs operate in an environment with wind gusts. In this paper, we propose a robust, novel technique that analyzes what we call ‘sensor fusion quality’ to assign an appropriate sensor reliability value to each set of updated sensor data. In the proposed approach, we characterize the quality of a set of newly acquired sensor data, containing a target, by examining the joint target location probability density function. The validity of the proposed method is demonstrated using flight test data.

Optimal Node Selection for Target Localization in Wireless Camera Sensor Networks

This paper studies the node-selection problem for target localization in wireless camera sensor networks. The goal of node selection is to optimize the tradeoff between the energy consumption of wireless camera sensor networks and the quality of target localization. We propose a cooperative target localization algorithm, which is implemented by two phases: 1) target detecting phase and 2) target locating phase. For the target detecting phase, we develop a probing environment and adaptive sleeping (PEAS)-based density control algorithm to select the proper subset of deployed camera sensors for maintaining the desired density of nodes in the detecting mode. For the locating phase, we map the node-selection problem into an optimization problem and then propose an optimal node-selection algorithm to select a subset of camera sensors for estimating the location of a target while minimizing the energy cost. We conduct extensive experiments and simulations to validate and evaluate our proposed schemes.

On the Use of Convex Optimization in Sensor Network Localization and Synchronization

In this paper we report some new results obtained in the field of multi-agent systems that are based on convex optimization. First, we provide review of a set of polynomial function optimization tools including sum of squares (SOS) and semidefinite programming (SDP). Then we present several applications of these tools in various multiagent system localization and synchronization tasks. As the first application, we propose a method based on SOS relaxation for agent localization using noisy measurements and describe the solution through SDP. Later, we apply this method to address the problems of cooperative target localization in the presence of noise and robot pose determination based on range measurements. Then we introduce the problem of anchor selection for minimizing the effect of noise in sensor networks via SDP. We use the same machinery to propose a method based on SDP to enhance synchronizability in networks. We do so by proposing a distributed algorithm for adding new edges to the network to enhance synchronizability. Finally, we present a method to identify the node in a network loss of which inflicts the most damage on the synchronizability of the network. Conclusions are presented in the last section.