Nested smoothing algorithms for inference and tracking of heterogeneous multi-scale state-space systems

Deep generative modeling for mechanistic-based learning and design of metamaterial systems

ABSTRACTIn this paper, we consider bipartite tracking of linear multi-agent systems with a leader. Both homogeneous and heterogeneous systems are investigated. The communication between agents is modelled by a directed signed graph, where the negative (positive) edges represent the antagonistic (cooperative) interactions among agents. Linear Quadratic Regulator (LQR)-based approach is used to derive the distributed protocol for the follower agent to achieve bipartite tracking of the leader. It is shown that solving the bipartite tracking problem over the structurally balanced signed graph is equivalent to solving the cooperative tracking problem over a corresponding graph with nonnegative edge weights. This bridges the gap between the newly raised bipartite tracking problem and the well-studied cooperative tracking problem. Three novel control protocols are proposed for both cooperative and bipartite output tracking of heterogeneous linear multi-agent systems. Numerical examples are given to show the effectiveness of our control protocols.

A horizontal single-axis tracking bracket with an adjustable tilt angle and its adaptive real-time tracking system for bifacial PV modules

This paper addresses the leader-follower consensus tracking problem for heterogeneous systems with general Lipschitz nonidentical nonlinear dynamics under a direct communication topology. Unlike existing works that achieved the cooperative behavior for heterogeneous dynamics networks with a nonzero error bound after the evolution of the entire systems, this paper develops an online leader-follower consensus tracking algorithm in which the design of the error condensation controller is proposed to achieve the completely cooperative behavior. In the sense of Lyapunov stability, it is proved that the leader-follower consensus tracking for the closed-loop heterogeneous systems with nonidentical nonlinear dynamics can be achieved completely. Two simulation examples are presented to verify the effectiveness of the proposed approach.

Localization and tracking (LT) algorithms for low data rate (LDR) ultra wideband (UWB) systems developed within the Integrated Project PULSERS Phase II are reviewed and compared. In particular, two localization algorithms, designed for static networks with mesh topologies, and one Tracking Algorithm, designed for dynamic network with star topologies are described and/or compared. Each of the localization algorithms adopts a different approach, namely, a centralized non-parametric weighted least squares approach (WLS), and a distributed Bayesian approach that relies on the cooperative maximization of the log-likelihood of range measurements (DMLL). The performance of these two alternatives are compared in a 3D indoor scenario under realistic ranging errors. The tracking algorithm is a fast non-parametric technique based on multidimensional scaling (MDS) and its performance is tested in a dynamic scenario. The proposed algorithms are practical and robust solutions addressing distinct network topologies and/or service requirements related to LDR-LT applications.

A novel real-time multiple objects detection and tracking framework for different challenges

In the World, perhaps the most significant issue as far as people understood, non-renewable sources would be extinguished. Apart from that, non-renewable energy sources are one of the critical factors for pollution, global warming. To address such issues, it is vital to shift to sustainable power sources, for example - sunlight, wind, etc. are essential in the present century. To examine the available solar tracking methods and algorithm, which have better accuracy and high output power efficiency. Multiple databases were searched for English literature and limiting to last ten years. The keywords selected for the search were a combination of the solar tracking algorithm, PLC, maximum power point tracking system, and solar tracking. The search results suggested that research on solar tracking is required to increase the generation of electricity. Many International research institutions conducted research related to solar tracking systems and tracking algorithms. The solar tracking is done, by utilizing mechanical sensors to maintain the PV module perpendicular to the sun’s irradiation. Proficient solar tracking methods are investigated by directing various analyses. The usage of renewable energy sources is lacking. Also, by designing an optimized solar tracking system for the generation of better output power is recommended.

The total amount of the electrical energy yielded by in-field photovoltaic (PV) modules can be increased by tracking, usually aiming at maximizing the incident direct solar radiation as input of the PV systems. The photovoltaic conversion efficiency is influenced by the PV cell temperature, directly correlated with the ambient temperature and the solar radiation (intensity and spectral distribution, particularly the % of IR); as results, more complex approaches of solar PV tracking can be considered, aiming at maximizing the PV output (electric energy) by optimizing the conversion efficiency based on collected global solar radiation and PV cell temperature. This paper presents a new approach for increasing the PV tracking efficiency; starting with the analysis of the tracking effect on the temperature of silicon photovoltaic modules and based on the specifics of each type of tracking, the paper comparatively discusses different tracking algorithms considering their effect on the input solar radiation. During the past 10 years, the R&D Center of Renewable Energy Systems and Recycling (RES-REC) in the Transilvania University has investigated different tracking systems and algorithms, focusing on optimized solutions tailored to the specific features of the implementation location (e.g. temperate, mountain areas like Brasov, Romania). The research infrastructure includes indoor high quality testing facilities (controlled solar radiation intensity and spectrum, and temperature as variable inputs) and in-field testing rigs (fixed tilted platforms, single-axis and dual-axis tracking systems), on-grid and off-grid connected PV systems. Based on the experimental data collected in the RES-REC Centre, recommendations are formulated on the need for accurate tracked PV system design, avoiding over- or under-estimating the output, thus allowing the implementation of feasible and efficient solutions.

With the rapid development of sensor information, robot control and target tracking technology, the target tracking of single target and single agent cannot meet the needs of some occasion. In the past decades, multi-agent control and tracking algorithms have attracted more and more attention. Among them, the control problem of multi-agent has become the research focus of scholars. As an important aspect of multi-agent control problem, formation control has also made considerable development. It has played an important role in the fields of industry, civil affairs and military affairs [1]. Multi-agent formation has obvious advantages compared with the task of single agent, it can accomplish more and more complex tasks, and the efficiency of the task is greatly improved. The failure of a single agent in the system will not significantly affect the completion of the overall task. In order to improve the efficiency of multi-agent system, it is very necessary to study the task of multi-agent coordination. At present, the widely used intelligent formation tracking system includes the target detection and tracking system of unmanned aerial vehicle formation, the ground vehicle object tracking and detection system and the detection and tracking system of the sea surface ship area target.

This paper proposes a novel tracking algorithm considering radiation to improve the power of a photovoltaic (PV) tracking system. The sensor method used in a conventional PV plant is unable to track the sun’s exact position when the intensity of solar radiation is low. It also has the problem of malfunctions in the tracking system due to rapid changes in the climate. The program method generates power loss due to unnecessary operation of the tracking system because it is not adapted to various weather conditions. This tracking system does not increase the power above that of a power of tracking system fixed at a specific position due to these problems. To reduce the power loss, this paper proposes a novel control algorithm for a tracking system and proves the validity of the proposed control algorithm through a comparison with the conventional PV tracking method.

Mainly studies multisensor data fusion algorithm for target tracking in nonlinear systems. Proposes the distributed fusion algorithm based on Converted Measurement Kalman Filter (CMKF). From theory, it is derived that distributed converted CMKF algorithm (DCMKFA) can nearly reconstruct centralized fusion estimate based on CMKF. Simulations proved this conclusion. So DCMKFA is a better distributed fusion algorithm for target tracking in nonlinear systems.

The Air Force Institute of Technology's Center for Directed Energy's (AFIT/CDE), under sponsorship of the HEL Joint Technology Office, and as part of a multidisciplinary research initiative on aero optics effects, has designed and fabricated a laser pointing/tracking system. This system will serve as the laser source for a series of in-flight data collection campaigns involving two aircraft. Real-Time tracking systems have a distinct difference from automatic image analysis. Both activities often involve the segmentation of an image and the automatic location of an item of interest. A number of advanced tracking algorithms have been developed for applications involving processing previously captured data. Medical imaging applications frequently use post processing algorithms to segment anomalies in medical imaging. In this paper we discuss an airborne laser pointing and tracking system and its requirements, designed and implemented at AFIT. This application is different because the image processing must be completed during the inter-frame period. AFIT analyzed available tracking algorithms including: centroid tracking, Fitts correlator, Posterior Track, and Active Contour. These algorithms were evaluated on their ability to both accurately track and to be computed in real time using existing hardware. The analysis shows that some of the more accurate tracking algorithms are not easily implementable in real time. Often there are large numbers of correlations that must be computed for each frame. Higher resolution images quickly escalate this problem. Algorithm selection for tracking applications must balance the need for accuracy and computational simplicity. Real time tracking algorithms are limited by the amount of time between frames with which to processes the data. Specialized hardware can improve this situation. We selected centroid tracking for the airborne application and evaluate its performance to show that it meets design requirements.

Target tracking in public traffic calls for a tracking system with automated track initiation and termination facilities in a randomly evolving driving environment. In addition, the key problem of data association needs to be handled effectively considering the limitations in the computational resources onboard an autonomous car. In this paper, we discuss a multi-target tracking system that addresses target birth/initiation and death/termination processes with automatic track management feature. The tracking system is based on Linear Multi-target Integrated Probabilistic Data Association Filter (LMIPDAF), which is adapted to specifically include algorithms that handle track initiation and termination, clutter density estimation and track management. The performance of the proposed tracking algorithm is compared to other single and multi-target tracking schemes and is shown to have acceptable tracking error. It is further illustrated through multiple traffic simulations that the computational requirement of the tracking algorithm is less than that of optimal multi-target tracking algorithms that explicitly address data association uncertainties.

We introduce a novel long-range traffic monitoring system for vehicle detection, tracking, and classification based on fiber-optic distributed acoustic sensing (DAS). High resolution and long range are provided by the use of an optimized setup incorporating pulse compression, which, to our knowledge, is the first time that is applied to a traffic-monitoring DAS system. The raw data acquired with this sensor feeds an automatic vehicle detection and tracking algorithm based on a novel transformed domain that can be regarded as an evolution of the Hough Transform operating with non-binary valued signals. The detection of vehicles is performed by calculating the local maxima in the transformed domain for a given time-distance processing block of the detected signal. Then, an automatic tracking algorithm, which relies on a moving window paradigm, identifies the trajectory of the vehicle. Hence, the output of the tracking stage is a set of trajectories, each of which can be regarded as a vehicle passing event from which a vehicle signature can be extracted. This signature is unique for each vehicle, allowing us to implement a machine-learning algorithm for vehicle classification purposes. The system has been experimentally tested by performing measurements using dark fiber in a telecommunication fiber cable running in a buried conduit along 40 km of a road open to traffic. Excellent results were obtained, with a general classification rate of 97.7% for detecting vehicle passing events and 99.6% and 85.7% for specific car and truck passing events, respectively.

Ultra-Wideband (UWB) stands out as one of the most promising technologies for the development of indoor location & tracking systems, providing very precise time of arrival measurement and consequently centimeter-level resolution in distance estimation. Once the distances to multiple reference nodes are estimated, several location & tracking algorithms can be used to compute user's position. Position calculation is a widely studied topic, and many different methods have been proposed in the literature and evaluated under different conditions. Nevertheless, evaluation scenarios are usually generic and too simplistic. The rigorous evaluation of location & tracking algorithms should take into account the specific error distribution of UWB-based distance estimation and the implications of indoor tracking scenarios, such as user's mobility, the number of reference nodes and the distance between them. The objective of this paper is to provide a realistic evaluation of the performance of different location & tracking algorithms (trilateration, least square — multidimensional scaling, extended Kalman filter and particle filter) in indoor environments using a specific UWB-based ranging model to characterize the distribution of distance estimation error.