Increase Tracking Performance Research Articles

An Improved Hybrid Beamforming Algorithm for Fast Target Tracking in Satellite and V2X Communication

Autonomous remote sensing systems establish communication links between nodes. Ensuring coverage and seamless communication in highly dense environments is not a trivial task as localization, separation, and tracking of targets, as well as interference suppression, are challenging. Therefore, smart antenna systems fulfill these requirements by employing beamforming algorithms and are considered a key technology for autonomous remote sensing applications. Among many beamforming algorithms, the recursive least square (RLS) algorithm has proven superior convergence and convergence rate performances. However, the tracking performance of RLS degrades in the case of dynamic targets. The forgetting factor in RLS needs to be updated constantly for fast target tracking. Additionally, multiple beamforming algorithms can be combined to increase tracking performance. An improved hybrid constant modulus RLS beamforming algorithm with an adaptive forgetting factor and a variable regularization factor is proposed. The forgetting factor is updated using the low-complexity yet robust adaptive moment estimation method (ADAM). The sliding-window technique is applied to the proposed algorithm to mitigate the steady-state noise. The proposed algorithm is compared with existing RLS-based algorithms in terms of convergence, convergence rate, and computational complexity. Based on the results, the proposed algorithm has at least 10 times better convergence (accuracy) and a convergence rate two times faster than the compared RLS-based algorithms.

1D Convolutional Neural Network-based Signal Tracking Algorithm with Feature Extraction and System Diagnosis

Diverse adaptive algorithms have been investigated and developed in an effort to mitigate the vibration and noise produced by mechanical systems. A reference signal plays a crucial role in adaptive algorithms for monitoring the target signal, however it is difficult to identify the reference signal when its spectrum is relatively complex. In addition, if the state of a mechanical system changes due to a disturbance or malfunction, signal tracking may not perform as well if the reference signal for the changing condition has not been redefined. This work focuses on constructing a signal tracking mechanism based on deep learning to increase tracking performance. The suggested algorithm consists of the following two parts: 1) automatically defining the reference signal segment and 2) system state feedback segment. These portions are managed by convolutional neural networks in one dimension. The Case Western Reserve University (CWRU) bearing dataset was used for training and testing to validate the proposed technique. In addition, a neural network-based approach for signal tracking is applied to validate the signal tracking performance. The suggested approach demonstrates that the signal tracking performance is great regardless of the system's state.

Maximum Power Point Tracking Method Application to Increase the Efficiency of Photovoltaic Installations

Photovoltaic systems using boost converters based on the traditional incremental conduction method have delayed convergence dynamics to the maximum power point (MPP). This article presents a simulation and hardware implementation of a variable step size MPPT with automatic scaling at low cost and low power consumption. The production of photovoltaic energy is highly dependent on weather and environmental conditions such as temperature and solar radiation. Due to any change in the external environment and the only control condition, the response of the first step of the working cycle of the converter of the traditional incremental conduction MPPT algorithm is not accurate, resulting in an incorrect estimate. Furthermore, this study clarifies the usual technique and presents a modified incremental conductance approach that appropriately responds as solar radiation levels rise. In order to improve the economy and efficiency of photo-voltaic systems, we proposed an improved incremental conduction algorithm for the MPP control strategy. This tech-nique is simpler in structure than the classic incremental conduction algorithm, and it can assign immediate incre-ments of power, voltage, and current as the environment changes, as well as increase tracking performance. The MATLAB/Simulink program runs under conditions of rapidly changing solar radiation levels, and the results of the enhanced and conventional incremental conductance algorithm are compared. The results show that the pro-posed algorithm can effectively detect erroneous judgments and prevent their occurrence. This not only optimizes the system, but also improves the efficiency, response speed, and tracking efficiency of the PV system, thus ensuring the stable operation of the power system.

Cell region fingerprints enable highly precise single-cell tracking and lineage reconstruction.

Experimental studies of cell growth, inheritance and their associated processes by microscopy require accurate single-cell observations of sufficient duration to reconstruct the genealogy. However, cell tracking-assigning identical cells on consecutive images to a track-is often challenging, resulting in laborious manual verification. Here, we propose fingerprints to identify problematic assignments rapidly. A fingerprint distance compares the structural information contained in the low frequencies of a Fourier transform to measure the similarity between cells in two consecutive images. We show that fingerprints are broadly applicable across cell types and image modalities, provided the image has sufficient structural information. Our tracker (TracX) uses fingerprints to reject unlikely assignments, thereby increasing tracking performance on published and newly generated long-term data sets. For Saccharomyces cerevisiae, we propose a comprehensive model for cell size control at the single-cell and population level centered on the Whi5 regulator, demonstrating how precise tracking can help uncover previously undescribed single-cell biology.

Exploring the effectiveness of auditory, visual, and audio-visual sensory cues in a multiple object tracking environment

Maintaining object correspondence among multiple moving objects is an essential task of the perceptual system in many everyday life activities. A substantial body of research has confirmed that observers are able to track multiple target objects amongst identical distractors based only on their spatiotemporal information. However, naturalistic tasks typically involve the integration of information from more than one modality, and there is limited research investigating whether auditory and audio-visual cues improve tracking. In two experiments, we asked participants to track either five target objects or three versus five target objects amongst similarly indistinguishable distractor objects for 14 s. During the tracking interval, the target objects bounced occasionally against the boundary of a centralised orange circle. A visual cue, an auditory cue, neither or both coincided with these collisions. Following the motion interval, the participants were asked to indicate all target objects. Across both experiments and both set sizes, our results indicated that visual and auditory cues increased tracking accuracy although visual cues were more effective than auditory cues. Audio-visual cues, however, did not increase tracking performance beyond the level of purely visual cues for both high and low load conditions. We discuss the theoretical implications of our findings for multiple object tracking as well as for the principles of multisensory integration.

State Estimation With a Destination Constraint Imposed by Proportional Navigation Guidance Law

Proportional navigation (PN) guidance laws have been commonly applied to homing missile systems guiding the missile to its target. When the target is stationary and its location is known a priori, the state of the missile is subjected to a destination constraint, which contains extra information on missile state that can help increase tracking performance. This article concerns with the constraint modeling and the state estimation with a destination constraint imposed by PN guidance law in 3-D space. Constraint models corresponding to three representative PN guidance laws: pure PN, true PN, and generalized PN, are presented using the state augmentation method, which stacks the states at the previous and current time steps in one state vector to express the constraint relationships. For cases where the destination information is noisy and the parameters of the guidance law, i.e., the navigation constant and the bias angle, are not known a priori, the unknown parameters are estimated along with the missile state in the state vector in an augmentation way. Based on the formulations of constraint models, the construction of pseudomeasurements is carried out. A constrained state estimation method is proposed by incorporating the pseudomeasurements into the nonlinear filtering process. In this method, a sequential process framework is employed and the cubature Kalman filter is used to handle the high-dimensional filtering problem and the strong nonlinearity involved in pseudomeasurements. Numerical experiments in three scenarios are performed to demonstrate the validity of the proposed models along with the estimation method.

Active Disturbance Rejection Control Based Sinusoidal Trajectory Tracking for an Upper Limb Robotic Rehabilitation Exoskeleton

In this paper, a combined control strategy with extended state observer (ESO) and finite time stable tracking differentiator (FTSTD) has been proposed to perform flexion and extension motion repetitively and accurately in the sagittal plane for shoulder and elbow joints. The proposed controller improves the tracking accuracy, performs state estimation, and actively rejects disturbance. A sinusoidal trajectory as an input has been given to a two-link multiple-input multiple-output (MIMO) upper limb robotic rehabilitation exoskeleton (ULRRE) for a passive rehabilitation purpose. The efficacy of the controller has been tested with the help of performance indices such as integral time square error (ITSE), integral square error (ISE), integral time absolute error (ITAE), and integral of the absolute magnitude of error (IAE). The system model is obtained through the Euler–Lagrangian method, and the controller’s stability is also given. The proposed controller has been simulated for ±20% parameter variation with constant external disturbances to test the disturbance rejection ability and robustness against parametric uncertainties. The proposed controller has been compared with already developed ESO-based methods such as active disturbance rejection control (ADRC), nonlinear active disturbance rejection control (NLADRC), and improved active disturbance rejection control (I-ADRC). It has been found that the proposed method increases tracking performance, as evidenced by the above performance indices.

Bearing-Only Active Sensing Under Merged Measurements

In this letter we propose an algorithm to actively track multiple moving targets using a bearing-only sensor in the presence of merged measurements. Merged measurements arise from sensor resolution constraints and therefore targets that are close in relative bearing to the sensor get reported as a single group measurement. We employ a merged measurement model in a nonlinear joint probabilistic data association filter for tracking multiple targets through merging events. We also propose an online adaptive planning algorithm that maneuvers the sensor in order to increase tracking performance. We introduce a novel method based on forward value iteration that incorporates the merged measurement information into the planning strategy. The resulting trajectory is biased away from situations where merged measurements occur, as this leads to more uncertainty in the target state estimates. We demonstrate our algorithm both in simulation as well as onboard real unmanned ground vehicles. This is the first time bearing-only tracking with merged measurements has been accomplished with a mobile sensor in practice. Furthermore, to the best of our knowledge, this is the first time merged measurement data association information has been utilized to effectively plan for such situations.

Triple-Mode Model Predictive Control Using Future Target Information

In this paper, we propose a triple-mode model predictive control (MPC) algorithm that uses future target information to improve tracking performance. To explicitly take into account the future target information in the MPC optimization, the proposed triple-mode control law encompasses three parts: (i) the future target information feedforward, (ii) the output feedback, and (iii) the extra degrees of freedom for constraint satisfaction. The first two parts of the control law are off-line designed through unconstrained MPC, and the optimal future trajectory horizon is obtained by golden section search based on the integral of squared error (ISE) criterion. The final part is calculated by the on-line MPC algorithm aiming to satisfy constraints. Furthermore, we analyze the feasibility and convergence properties of the proposed algorithm. The method is demonstrated by the simulation of the shell fundamental control problem and also tested on the coordinated control problem in the power plant. The test results show that the proposed algorithm can increase tracking performance dramatically due to the proper selection of future trajectory horizon.

Fractional High-Order Differential Estimator and Feedback Controller Design for a Single-Input–Single-Output Affine Chaotic System

AbstractControl of chaos generally refers to realize a desired behavior of chaotic system output and its states. In this manner, we design a fractional high-order differential feedback controller (FHODFC) to increase tracking performance of a nonlinear system output and its differentials for a desired trajectory signal. The proposed controller is based on fractional calculus and high-order extracted differentials of error signal. The suggested fractional approach is applied to a single-input–single-output affine Duffing-Holmes dynamical system in matlab/simulink environment. Duffing-Holmes system is analyzed for two different problems: estimation and control problems. The simulation results clearly demonstrate superior dynamic behavior of the FHODFC compared to the classical high-order differential feedback controller (HODFC) version for both estimation and control problems.

Stem cell motion-tracking by using deep neural networks with multi-output

The aim of automated stem cell motility analysis is reliable processing and evaluation of cell behaviors such as translocation, mitosis, death, and so on. Cell tracking plays an important role in this research. In practice, tracking stem cells is difficult because they have frequent motion, deformation activities, and small resolution sizes in microscopy images. Previous tracking approaches designed to address this problem have been unable to generalize the rapid morphological deformation of cells in a complex living environment, especially for real-time tracking tasks. Herein, a deep learning framework with convolutional structure and multi-output layers is proposed for overcoming stem cell tracking problems. A convolutional structure is used to learn robust cell features through deep features learned on massive visual data by a transfer learning strategy. With multi-output layers, this framework tracks the cell’s motion and simultaneously detects its mitosis as an assistant task. This improves the generalization ability of the model and facilitates practical applications for stem cell research. The proposed framework, tracking and detection neural networks, also contains a particle filter-based motion model, a specialized cell sampling strategy, and corresponding model update strategy. Its current application to a microscopy image dataset of human stem cells demonstrates increased tracking performance and robustness compared with other frequently used methods. Moreover, mitosis detection performance was verified against manually labeled mitotic events of the tracked cell. Experimental results demonstrate good performance of the proposed framework for addressing problems associated with stem cell tracking.

저가 하드웨어 기반 멀티로터 비행제어 컴퓨터 설계 및 검증

This paper describes development and validation processes of a low-cost hardware based flight control computer designed for multi-rotor UAVs. The developed flight control computer controls multi-rotors stable and can handle complex flight missions using an integrated high-performance Linux computer. A complementary filter generates a navigation solution with 500 Hz, and a proposed observer significantly reduces measurement noise. A control algorithm utilizes a feed-forward term computed by a three-dimensional curve fitting method, and it increases tracking performance. The developed flight control system has been fully tested through several test flights, and it can apply to real flight environments.

Optimal Appearance Model for Visual Tracking

Many studies argue that integrating multiple cues in an adaptive way increases tracking performance. However, what is the definition of adaptiveness and how to realize it remains an open issue. On the premise that the model with optimal discriminative ability is also optimal for tracking the target, this work realizes adaptiveness and robustness through the optimization of multi-cue integration models. Specifically, based on prior knowledge and current observation, a set of discrete samples are generated to approximate the foreground and background distribution. With the goal of optimizing the classification margin, an objective function is defined, and the appearance model is optimized by introducing optimization algorithms. The proposed optimized appearance model framework is embedded into a particle filter for a field test, and it is demonstrated to be robust against various kinds of complex tracking conditions. This model is general and can be easily extended to other parameterized multi-cue models.

Discriminative descriptors for object tracking

Object tracking is one of the most challenging problems in computer vision. Only Fast trackers can satisfy the real-time requirements and can be used in many artificial intelligence applications. Due to the impressive high-speed, correlation filters have received much attention within the field of object tracking. Recently, trackers using luminance information or color names for image description have been performed in a correlation filters framework. In this paper, we propose the usage of discriminative color descriptors to improve the tracking performance of the traditional correlation filters tracker. Discriminative color descriptors are compact and efficient. Moreover, our tracker incorporates scale estimation into the traditional correlation filters, which results in increased tracking performance. Extensive experiments demonstrate that the proposed tracker can obtain superior results compared to existing trackers using correlation filters and it is also able to outperform state-of-the-art trackers on the CVPR2013 object tracking benchmark.

Struck: Structured Output Tracking with Kernels.

Adaptive tracking-by-detection methods are widely used in computer vision for tracking arbitrary objects. Current approaches treat the tracking problem as a classification task and use online learning techniques to update the object model. However, for these updates to happen one needs to convert the estimated object position into a set of labelled training examples, and it is not clear how best to perform this intermediate step. Furthermore, the objective for the classifier (label prediction) is not explicitly coupled to the objective for the tracker (estimation of object position). In this paper, we present a framework for adaptive visual object tracking based on structured output prediction. By explicitly allowing the output space to express the needs of the tracker, we avoid the need for an intermediate classification step. Our method uses a kernelised structured output support vector machine (SVM), which is learned online to provide adaptive tracking. To allow our tracker to run at high frame rates, we (a) introduce a budgeting mechanism that prevents the unbounded growth in the number of support vectors that would otherwise occur during tracking, and (b) show how to implement tracking on the GPU. Experimentally, we show that our algorithm is able to outperform state-of-the-art trackers on various benchmark videos. Additionally, we show that we can easily incorporate additional features and kernels into our framework, which results in increased tracking performance.

惯性稳定平台自适应前馈控制

An adaptive feed-forward control method combining with feedback control was proposed to improve the command tracking performance of control circuit in an inertial stabilized platform. On the basis of subspace system identification, the input-output data were used to identify a state space of the stabilized platform model and the frequency-domain loop shaping technique was used to design the feedback loop controller to reject the external disturbance. Furthermore, a Recursive Least Square (RLS) adaptive filter was taken to build the inverse model of the feedback control loop and to construct the all-pass transfer function for increasing tracking performance. Several tracking experiments were conducted on different command inputs to verify the validity of the adaptive feed-forward controller. Experimental results show that this method responses quickly to step commands and the overshoot has decreased from 30% to 4.5% as compared with that of the feedback control. For a sinusoidal signal at the frequency of 30 Hz, the adaptive feed-forward controller can obtain an amplitude response without attenuation and the phase lag is reduced to 54 from 90 as compared with that of the feedback control. This method significantly improves the transient performance and is superior to the feedback control alone. , 2015, Chinese Academy of Sciences. All right reserved.

Acoustic source tracking in long baseline microphone arrays

Long baseline microphone arrays, where the distance between single microphones is large, is a cost-effective method to monitor activity in a large region of interest. The high range between source and sensors in a long baseline setup, generally leads to low signal-to-noise ratios (SNR). This affect the performance of localization. This paper investigates how energy based localization (EBL) can be used for localization and tracking of both single and multiple acoustic sources, within a long baseline array. Least-Squares (LS) optimization have been used for EBL-based localization, however the localization performance is sensitive to low SNR. We propose a tracking scheme based on a cost reference particle filter (CRPF) to increase performance during low SNR. The CRPF is a new class of particle filters which is able to estimate the system state from the available observations without a priori knowledge of any probability density function. We present a modified cost function, for EBL based localization, which is incorporated in the CRPF framework to increase tracking performance during simulated wind gusts and background noise. The proposed method outperforms LS based localization in the case of low SNR.

The MPI CyberMotion Simulator: A Novel Research Platform to Investigate Human Control Behavior

The MPI CyberMotion Simulator provides a unique motion platform, as it features an anthropomorphic robot with a large workspace, combined with an actuated cabin and a linear track for lateral movement. This paper introduces the simulator as a tool for studying human perception, and compares its characteristics to conventional Stewart platforms. Furthermore, an experimental evaluation is presented in which multimodal human control behavior is studied by identifying the visual and vestibular responses of participants in a roll-lateral helicopter hover task. The results show that the simulator motion allows participants to increase tracking performance by changing their control strategy, shifting from reliance on visual error perception to reliance on simulator motion cues. The MPI CyberMotion Simulator has proven to be a state-of-the-art motion simulator for psychophysical research to study humans with various experimental paradigms, ranging from passive perception experiments to active control tasks, such as driving a car or flying a helicopter.

Multitarget Sensor Resolution Model and Joint Probabilistic Data Association

In the design of target tracking algorithms, the aspect of sensor resolution is rarely considered. Instead, it is usually assumed that all targets are always resolved, and that the only uncertainties in the data association are which targets that are detected, and which measurement each detected target gave rise to. However, in situations where the targets are closely spaced in relation to the sensor resolution, this assumption is not valid, and may lead to degraded tracking performance due to an incorrect description of the data. We present a framework for handling sensor resolution effects for an arbitrary, but known, number of targets. We propose a complete multitarget sensor resolution model that can be incorporated into traditional Bayesian tracking filters. Further, the exact form of the posterior probability density function (pdf) is derived, and two alternative ways of approximating that exact posterior density with a joint probabilistic data association (JPDA) filter are proposed. Evaluations of the resulting filters on simulated radar data show significantly increased tracking performance compared with the JPDA filter without a resolution model.

Underdamped Second-Order Systems Overshoot Control

The paper addresses the problem of decreasing the overshoot for underdamped second-order systems. A new technique to control the overshoot is proposed, which is based on Posicast control and proportional integral and derivative (PID) control, which performs switching between two controllers. The aim is to use open-loop feedforward control to increase tracking performance and PID control to deal with disturbance rejection. It has been shown that the proposed control scheme can have some advantages over the classical approaches without switching capabilities.