Joint Tracking Research Articles

Distributed Joint Cooperative Self-Localization and Target Tracking Algorithm for Mobile Networks.

Location information is a key issue for applications of the Internet of Things. In this paper, we focus on mobile wireless networks with moving agents and targets. The positioning process is divided into two phases based on the factor graph, i.e., a prediction phase and a joint self-location and tracking phase. In the prediction phase, we develop an adaptive prediction model by exploiting the correlation of trajectories within a short period to formulate the prediction message. In the joint positioning phase, agents calculate the cooperative messages according to variational message passing and locate themselves. Simultaneously, the average consensus algorithm is employed to realize distributed target tracking. The simulation results show that the proposed prediction model is adaptive to the random movement of nodes. The performance of the proposed joint self-location and tracking algorithm is better than the separate cooperative self-localization and tracking algorithms.

Simultaneous Monitoring of Penetration Status and Joint Tracking During Laser Keyhole Welding

Visual monitoring is currently a research hotspot in the monitoring of laser welding. In this paper, an active coaxial monitoring system for laser keyhole welding is built. This system reduces the intense light interference caused by the laser, plasma plume, and specular reflection of the molten pool. The significant differences in the temperatures of the joint, keyhole, and molten pool are also overcome. Images that include the clear keyhole, penetration hole, and joint are captured by the single-sensor monitoring system. The ratio of the penetration hole area to the keyhole area and the fault-tolerant disposal based on the permissive area scope of the keyhole are used to propose an algorithm for judging the penetration status. The deviations between the keyhole centroid and the joint centerline and the fault-tolerant disposal based on the maximum allowable slope change rate of the joint centerline are used to track the joint. Experiments are also performed to monitor the penetration status and to track the joint tracking simultaneously. The experimental results show that the method of simultaneous monitoring that is proposed in this paper is feasible. This single-sensor and multitarget monitoring method will provide basis for the online control of the laser welding process.

Research on the Weld Position Detection Method for Sandwich Structures from Face-Panel Side Based on Backscattered X-ray.

Web-core sandwich panels are a typical lightweight structure utilized in a variety of fields, such as naval, aviation, aerospace, etc. Welding is considered as an effective process to join the face panel to the core panel from the face panel side. However, it is difficult to locate the joint position (i.e., the position of core panel) due to the shielding of the face panel. This paper studies a weld position detection method based on X-ray from the face panel side for aluminum web-core sandwich panels used in aviation and naval structures. First, an experimental system was designed for weld position detection, able to quickly acquire the X-ray intensity signal backscattered by the specimen. An effective signal processing method was developed to accurately extract the characteristic value of X-ray intensity signals representing the center of the joint. Secondly, an analytical model was established to calculate and optimize the detection parameters required for detection of the weld position of a given specimen by analyzing the relationship between the backscattered X-ray intensity signal detected by the detector and the parameters of the detection system and specimen during the detection process. Finally, several experiments were carried out on a 6061 aluminum alloy specimen with a thickness of 3 mm. The experimental results demonstrate that the maximum absolute error of the detection was 0.340 mm, which is sufficiently accurate for locating the position of the joint. This paper aims to provide the technical basis for the automatic tracking of weld joints from the face panel side, required for the high-reliability manufacturing of curved sandwich structures.

Joint Smoothing and Tracking Based on Continuous-Time Target Trajectory Function Fitting

We present a continuous time state estimation framework that unifies traditionally individual tasks of smoothing, tracking, and forecasting (STF), for a class of targets subject to smooth motion processes, e.g., the target moves with nearly constant acceleration or affected by insignificant noises. Fundamentally different from the conventional Markov transition formulation, the state process is modeled by a continuous trajectory function of time (FoT) and the STF problem is formulated as an online data fitting problem with the goal of finding the trajectory FoT that best fits the observations in a sliding time-window. Then, the state of the target, whether the past (namely, smoothing), the current (filtering) or the near-future (forecasting), can be inferred from the FoT. Our framework releases stringent statistical modeling of the target motion in real time, and is applicable to a broad range of real world targets of significance such as passenger aircraft and ships which move on scheduled, (segmented) smooth paths but little statistical knowledge is given about their real time movement and even about the sensors. In addition, the proposed STF framework inherits the advantages of data fitting for accommodating arbitrary sensor revisit time, target maneuvering and missed detection. The proposed method is compared with state of the art estimators in scenarios of either maneuvering or non-maneuvering target.

Skew-Aware Joint Multi-Track Equalization for Array Reader-Based Interlaced Magnetic Recording

Interlaced magnetic recording (IMR) enhanced with an array reader, named array reader-based IMR (ARIMR), has been introduced recently showing a tangible areal density capability gain over the conventional recording system. Joint read equalization is one of the key enablers to improve the bit-error-rate performance of the readback signals, which even shows potential for on-line processing of joint multi-track retrievals. However, the performance gain of ARIMR may not be guaranteed for the entire disk due to wide variations of the cross-track separation (CTS) between read sensors by the skew of the array and recording tracks. In order to mitigate the undesired skew effect in ARIMR, a novel joint multi-track equalization scheme is proposed in this paper with customized allocation of the array reader depending on the skew as well as the joint track center. Furthermore, individual readers in the array are not necessarily identical and their performance difference should be accounted for together with CTS for reader allocations. Therefore, the effective joint center of the target tracks is estimated in the proposed study to allocate the array reader for jointly accessing multiple tracks in ARIMR, even with undesired track mis-registration offsets. For performance evaluation, the proposed joint equalization with effective cross-track center allocation scheme is investigated by numerical ARIMR channel simulations with the micro-pixelated magnetic channel model. In the evaluation, varying CTS conditions are simulated to account for the skew impacts on ARIMR reading dual tracks and the corresponding effective cross-track center for placing the reader. Overall, off-track capability widths common for the two neighboring tracks can be evaluated for a broad range of array readers, which shows the feasibility of joint reading of dual tracks by ARIMR.

External Force Self-Sensing Based on Cable-Tension Disturbance Observer for Surgical Robot End-Effector

The external force sensing ability of the end-effector plays a significant role in restricting the refine operation of a surgical robot. This paper proposes an external force self-sensing method based on the cable-tension disturbance observer for a four-degree-of-freedom (4-DoF) surgical robot end-effector, which is actuated by three backdrivable cable-driven series elastic actuators (BCDSEAs). First, the design of the surgical robot end-effector is introduced with the experimental prototype of a 1-DoF flexible finger joint. Second, four kinds of dynamic models of the flexible finger joint are established and experimentally identified. A joint angle estimator is proposed based on a simplified dynamic model. Then, a closed-loop motion control method for the flexible finger joint is proposed. Third, a driving cable-tension estimator is constructed as the benchmark for the cable-tension disturbance observer under free motion. Meanwhile, an external force self-sensing estimator is proposed based on the motion control strategy and the cable-tension disturbance observer. Finally, the experimental results of joint tracking control present a good performance. The compensation algorithm for the external force under free motion is studied to reduce the system error. The experimental results of the external force self-sensing show an overall estimation accuracy of 85.97% with a range of 0.10–2.00 N.

An iterative tuning approach for feedforward control of parallel manipulators by considering joint couplings

Feedforward control is an effective way to improve the joint tracking accuracy of robotic systems. This paper presents an iterative approach for feedforward controller parameter tuning of parallel manipulators that considers joint couplings (cross-talk). Based upon a compound control strategy, increments of the feedforward tuning parameters are iteratively updated by minimizing the sum of squares of joint tracking errors at each step. A plant-free identification Jacobian is formulated using the measured data associated with a number of sequential statuses within each iteration cycle. Experiments on the 3-DOF parallel mechanism within a 5-DOF hybrid robot verify parameter convergence and the extrapolation capability of the proposed approach. Compared to otherwise similar feedforward control not considering joint couplings, the root mean square of joint tracking errors was reduced by up to 22% when the mechanism moved at high speed along a path in the neighborhood of the reference configuration.

On the Modelling of Rail Joint

Jointed track is still used to build the secondary lines where the maximum speed does not exceed 100-120 km/h. Jointed track construction is based by the fact that the rails are joined end-to-end via the rail joints in order to assure the continuity of the rolling surface of the rails. The rails are jointed using two metal joint bars (fishplates) bolted to the ends of adjoining rails. The rail joints are featured with small gaps to allow the thermic expansion of the rails when the environmental temperature is higher than that during the fitting. In this paper, an analytical model for the rail joint considering the influence of the joint gap is presented and analysed. The model consists of three Euler-Bernoulli beams, two for the rail ends of the rail joint and the third beam for the two joint bars, connected to the rail ends by a Winkler layer. The concept of weakness of the rail joint (rail joint deflection/continuous rail deflection) is introduced and used to analyse the static behaviour of three types of rail joints used at CFR (Romanian Railways). The influence of the joint bars length and bending stiffness, and the influence of the joint gap length upon the rail joint weakness is pointed out.

Numerical Analysis of the Vertical Bogie Accelerations at Failure of the Damper in the Primary Suspension of the Railway Vehicle

Jointed track is still used to build the secondary lines where the maximum speed does not exceed 100-120 km/h. Jointed track construction is based by the fact that the rails are joined end-to-end via the rail joints in order to assure the continuity of the rolling surface of the rails. The rails are jointed using two metal joint bars (fishplates) bolted to the ends of adjoining rails. The rail joints are featured with small gaps to allow the thermic expansion of the rails when the environmental temperature is higher than that during the fitting. In this paper, an analytical model for the rail joint considering the influence of the joint gap is presented and analysed. The model consists of three Euler-Bernoulli beams, two for the rail ends of the rail joint and the third beam for the two joint bars, connected to the rail ends by a Winkler layer. The concept of weakness of the rail joint (rail joint deflection/continuous rail deflection) is introduced and used to analyse the static behaviour of three types of rail joints used at CFR (Romanian Railways). The influence of the joint bars length and bending stiffness, and the influence of the joint gap length upon the rail joint weakness is pointed out.

Joint target detection and tracking with heavy-tailed noises

This paper investigates the problem of simultaneous target detection and tracking with heavy-tailed noises under Bernoulli filtering framework. By exploiting the Student-t distributions to formulate the outlier-corrupted process noise and measurement noise, a novel target tracking algorithm named the Student-t mixture Bernoulli filter is proposed. Under linear assumptions on target dynamic model, sensor measurement model and birth process, closed-form recursions that propagate the parameters of Bernoulli random finite set are derived. The validity of the proposed algorithm is verified by numerical simulations.

Composite direct adaptive Taylor series – fuzzy controller for the robust asymptotic tracking control of flexible-joint robots

Recent developments in the use of Taylor series-based controllers have increased the need for utilizing Taylor series system for complex nonlinear systems. This research aims at developing a direct adaptive controller for the tracking control of electrically-driven flexible-joint robotic manipulators. In our direct adaptive method, the decentralized controller benefits by two control terms, namely a Taylor series estimator to approximate an ideal controller for the diminution of joint tracking error in the presence of lumped uncertainty and also a fuzzy estimator to compensate an approximation error. The joint tracking error and its time derivatives up to the second order are guaranteed to converge asymptotically to zero by means of the stability analysis. To obtain better control performance, the particle swarm optimization is used for selecting the control parameters. Simple and accurate design, small joint tracking error and no need of bounding functions are the main advantages of proposed control method. Finally, numerous simulation studies are included to verify the efficiency of the controller.

Joint Localization and Tracking Design for AUV With Asynchronous Clocks and State Disturbances

Localization and tracking are basic services for the applications of autonomous underwater vehicles (AUVs). Although they are closely related, the localization or tracking is usually studied independently. This paper addresses a joint localization and tracking issue for an AUV, subject to asynchronous clocks in physical channel and state disturbances in control channel. We first develop an asynchronous localization algorithm to estimate the position of an AUV, wherein the clocks on AUV and buoys are not required to be synchronized. With the estimated position, a model-free tracking controller is designed for an AUV to track the reference trajectory. Particularly, the time-delay estimation and dynamic gain strategies are adopted to reduce the influence of state disturbances. Moreover, the convergence conditions and Cramer–Rao lower bounds for the localization algorithm are derived. For the tracking controller, Lyapunov functions are constructed to analyze the stability, through which the sufficient conditions for asymptotic stability are presented. Finally, simulation and experiment results are given to show the effectiveness of the proposed method. It is demonstrated that the asynchronous localization algorithm in this paper can effectively eliminate the impact of the clock skew and offset. Meanwhile, the tracking accuracy can be significantly improved.

Research on Trajectory Tracking and Vibration Suppression of a Smart Flexible-joint-and-link Space Manipulator

The under actuated model of a smart space manipulator with flexible links and joints is established based on the Lagrange equation and assumed model method. Afterwards, using singular perturbation method, the system is decomposed into a slow subsystem and a fast subsystem, which describe the rigid-body motion and flexible vibration, respectively. Then, a modified computed torque controller based on the under actuated model is put forward to realize trajectory tracking of joints. In the meantime, the joint flexibility is compensated real-timely by the motors while the real-time suppression of the link vibration is realized by the embedded piezoelectric actuators. Finally, the simulation results indicate that the proposed composite control strategy can realize the trajectory tracking rapidly and in the same time suppress the vibration caused by flexible joints and links efficiently.

VirtFogSim: A Parallel Toolbox for Dynamic Energy-Delay Performance Testing and Optimization of 5G Mobile-Fog-Cloud Virtualized Platforms

It is expected that the pervasive deployment of multi-tier 5G-supported Mobile-Fog-Cloudtechnological computing platforms will constitute an effective means to support the real-time execution of future Internet applications by resource- and energy-limited mobile devices. Increasing interest in this emerging networking-computing technology demands the optimization and performance evaluation of several parts of the underlying infrastructures. However, field trials are challenging due to their operational costs, and in every case, the obtained results could be difficult to repeat and customize. These emerging Mobile-Fog-Cloud ecosystems still lack, indeed, customizable software tools for the performance simulation of their computing-networking building blocks. Motivated by these considerations, in this contribution, we present VirtFogSim. It is a MATLAB-supported software toolbox that allows the dynamic joint optimization and tracking of the energy and delay performance of Mobile-Fog-Cloud systems for the execution of applications described by general Directed Application Graphs (DAGs). In a nutshell, the main peculiar features of the proposed VirtFogSim toolbox are that: (i) it allows the joint dynamic energy-aware optimization of the placement of the application tasks and the allocation of the needed computing-networking resources under hard constraints on acceptable overall execution times; (ii) it allows the repeatable and customizable simulation of the resulting energy-delay performance of the overall system; (iii) it allows the dynamic tracking of the performed resource allocation under time-varying operational environments, as those typically featuring mobile applications; (iv) it is equipped with a user-friendly Graphic User Interface (GUI) that supports a number of graphic formats for data rendering; and (v) its MATLAB code is optimized for running atop multi-core parallel execution platforms. To check both the actual optimization and scalability capabilities of the VirtFogSim toolbox, a number of experimental setups featuring different use cases and operational environments are simulated, and their performances are compared.

Multi-vision tracking and collaboration based on spatial particle filter

In existing multi-vision tracking methods, a distributed collaborative tracking mode based on homography constraints is often adopted, yet there are significant shortcomings to this approach. For example, visual information complementation is not used to improve the robustness of tracking, and collaborative tracking is limited by homography constraints. In this study, a three-dimensional spatial particle filter tracking method was proposed, and multi-vision joint tracking and collaboration were effectively achieved. This method was based on the existing particle filter framework. A two-dimensional plane particle was taken as the projection of a three-dimensional spatial particle on the imaging plane, and the formula for calculating a spatial particle’s weight was derived based on Bayesian posterior probability recursion. In addition, an approximation method to determine spatial particle weight was given. The resampling of spatial particles was performed by using an epipolar line resampling method, and a collaborative tracking mechanism was established based on the concept of resolution. The results showed that the proposed method had higher tracking precision and anti-occlusion performance than other existing methods. In this method, the robustness of tracking was effectively improved, and unlimited optimization cooperation between visual sensing was achieved.

Joint Tracking and Classification of Extended Targets Using Random Matrix and Bernoulli Filter for Time-Varying Scenarios

In surveillance applications, the extent states and measurements of extended targets received by sensors are time-varying. In this paper, we propose a joint tracking and classification (JTC) method for single extended target under the presence of clutter and detection uncertainty. The extent state is modeled as elliptic shape via random matrix model (RMM), and is used as the feature for target classification. To adapt to the time-varying conditions of an extended target, the RMM proposed by Lan et al. is used. Besides, the RMM is integrated into Bernoulli filter to detect an extended target with clutter and detection uncertainty. The resulting method is called joint tracking and classification Gaussian inverse Wishart Bernoulli (JTC-GIW-Ber) filter, and the closed expressions for JTC-GIW-Ber filter recursions are derived under the necessary assumptions and approximations. Comprehensive simulations are carried out to test the performance, and the results demonstrate that the proposed JTC-GIW-Ber filter not only outperforms the JTC-GIW probability hypothesis density (JTC-GIW-PHD) filter and the GIW-Ber filter in extent state estimation, but also outperforms the JTC-GIW-PHD filter in target classification.

Seam tracking during electron beam welding in the air

We study methods of beam positioning at the joint during Electron Beam Welding in the air. Traditional methods of automatic beam positioning at the joint are unacceptable during the Electron Beam Welding in the air because of the significant dispersion of electrons and the impossibility of the beam deflection inside the electron beam gun due to the presence of an airlock. For joint tracking, we propose using magnetic fields of the current in the welded parts created by the beam current. It is established that the vertical component of the magnetic field of the current in the welded parts is proportional to the beam deflection at the joint. A differential flux gate meter is used as a tracking device. We outline the functional diagram of the joint tracking device and address issues with error prevention methods.

Jointly Optimized Target Detection and Tracking Using Compressive Samples

In this paper, we consider the problem of joint target detection and tracking in compressive sampling and processing (CSP-JDT). CSP can process the compressive samples of sparse signals directly without signal reconstruction, which is suitable for handling high-resolution radar signals. However, in CSP, the radar target detection and tracking problems are usually solved separately or by a two-stage strategy, which cannot obtain a globally optimal solution. To jointly optimize the target detection and tracking performance and inspired by the optimal Bayes joint decision and estimation (JDE) framework, a jointly optimized target detection and tracking algorithm in CSP is proposed. Since detection and tracking are highly correlated, we first develop a measurement matrix construction method to acquire the compressive samples, and then a joint CSP Bayesian approach is developed for target detection and tracking. The experimental results demonstrate that the proposed method outperforms the two-stage algorithms in terms of the joint performance metric.

A PHD-Based Particle Filter for Detecting and Tracking Multiple Weak Targets

Joint detection and tracking weak target is a challenging problem whose complexity is intensified when there are multiple targets present at the same time. Some Probability Hypothesis Density (PHD) based track-before-detect (TBD) particle filters (PHD-TBD) are proposed to solve this issue; however, the performance is unsatisfactory especially when the number of targets is large because some assumptions in PHD are violated. We propose to modify the general PHD-TBD filter in two aspects to make the PHD processing available for TBD scenarios. First, the distribution of false alarms is approximated as the Poisson distribution through a threshold method, and then a clustering technique is proposed to solve the overestimation of the target number. A typical TBD scenario is used to test the effectiveness of the proposed method. Simulation results indicate that the proposed method outperforms the general method in terms of estimation accuracy and computational complexity.

Search-Detect-Track Sensor Allocation for Geosynchronous Space Objects

The initiation of tracks for newly discovered objects presents unique challenges in space situational awareness. Recent work explores the use of admissible regions to initialize filters for space object tracking; however, these methods require follow-on measurements to refine the initial admissible region solution. This paper presents an approach to the joint search and track problem, designed to allow a single sensor to build and maintain a catalog of objects without requiring an a priori estimate.