Track-to-track association for intelligent vehicles by preserving local track geometry

Track-to-track association (T2TA) is a challenging task in situational awareness in intelligent vehicles and surveillance systems. In this paper, the problem of track-to-track association with sensor bias (T2TASB) is considered. Traditional T2TASB algorithms only consider a statistical distance cost between local tracks from different sensors, without exploiting the geometric relationship between one track and its neighboring ones from each sensor. However, the relative geometry among neighboring local tracks is usually stable, at least for a while, and thus helpful in improving the T2TASB. In this paper, we propose a probabilistic method, called the local track geometry preservation (LTGP) algorithm, which takes advantage of the geometry of tracks. Assuming that the local tracks of one sensor are represented by Gaussian mixture model (GMM) centroids, the corresponding local tracks of the other sensor are fitted to those of the first sensor. In this regard, a geometrical descriptor connectivity matrix is constructed to exploit the relative geometry of these tracks. The track association problem is formulated as a maximum likelihood estimation problem with a local track geometry constraint, and an expectation–maximization (EM) algorithm is developed to find the solution. Simulation results demonstrate that the proposed methods offer better performance than the state-of-the-art methods.The authors gratefully acknowledge the Autonomous Vision Group for providing the KITTI dataset. The authors also would like to thank the editors and referees for the valuable comments and suggestions.The Research Funds of Chongqing Science and Technology Commission, the National Natural Science Foundation of China, the Key Project of Crossing and Emerging Area of CQUPT, the Research Fund of young-backbone university teacher in Chongqing province, Chongqing Overseas Scholars Innovation Program, Wenfeng Talents of Chongqing University of Posts and Telecommunications, Innovation Team Project of Chongqing Education Committee, the National Key Research and Development Program, the Research and Innovation of Chongqing Postgraduate Project, the Lilong Innovation and Entrepreneurship Fund of Chongqing University of Posts and Telecommunications.http://www.mdpi.com/journal/sensorsam2021Electrical, Electronic and Computer Engineerin

Sustainable supply chain management towards disruption and organizational ambidexterity:A data driven analysis

Balancing sustainability and disruption of supply chains requires organizational ambidexterity. Sustainable supply chains prioritize efficiency and economies of scale and may not have sufficient redundancy to withstand disruptive events. There is a developing body of literature that attempts to reconcile these two aspects. This study gives a data-driven literature review of sustainable supply chain management trends toward ambidexterity and disruption. The critical review reveals temporal trends and geographic distribution of literature. A hybrid of data-driven analysis approach based on content and bibliometric analyses, fuzzy Delphi method, entropy weight method, and fuzzy decision-making trial and evaluation laboratory is used on 273 keywords and 22 indicators obtained based on the experts’ evaluation. The most important indicators are identified as supply chain agility, supply chain coordination, supply chain finance, supply chain flexibility, supply chain resilience, and sustainability. The regions show different tendencies compared with others. Asia and Oceania, Latin America and the Caribbean, and Africa are the regions needs improvement, while Europe and North America show distinct apprehensions on supply chain network design. The main contribution of this review is the identification of the knowledge frontier, which then leads to a discussion of prospects for future studies and practical industry implementation

Underwater source localization using time difference of arrival and frequency difference of arrival measurements based on an improved invasive weed optimization algorithm

Abstract In this study, we propose an underwater localization method based on an improved invasive weed optimization algorithm to accurately locate moving sources in underwater sensor networks. First, the Lévy flight model is introduced into the invasive weed optimization algorithm to enhance its global search ability and avoid falling into local optima. At the same time, under the condition that the observed noise of each observation is Gaussian noise and does not consider the influence of other error factors, the localization error is adopted as the objective function to obtain an initial estimate for the unknown source parameter. Then, the obtained initial estimates of the target position and velocity as well as the target parameter error are utilized to construct a new localization model. Finally, the precise position of the source and its velocity are obtained according to the weighted least square method. The performance of the algorithm is verified by comparing it with the Cramér–Rao Lower Bound (CRLB). Results from simulations indicate that the algorithm proposed in this paper has excellent localization accuracy compared to existing methods and achieves results close to the CRLB

Finite Element Steering Wheel for Heavy Vehicles, Testing and Modeling

The main aim of this study was to validate an already existing Finite Element (FE) model of a truck steering wheel, using experimental testing and computer modeling. Statistics show a high risk of sustaining severe injuries, in a frontal crash of heavy vehicles, due to steering wheel rim to thorax contact. The Hybrid III crash test dummy is now also used for analyzing heavy vehicle frontal crashes. It was originally developed for passenger cars and load cases common to them. To use in heavy vehicles, loading pattern to Hybrid III torso is now changed from the central hub to the rim of steering wheel. The Hybrid III crash test dummy is also available in FE codes. In order to investigate the heavy vehicle crashes in FE, a validated FE steering wheel model is also required. An impact test setup was designed and replicated both in physical testing and computer modeling. Since the steering wheel can be adjusted at different tilt angles of the rim during driving, a set of tilt angles 0, 10, 20 and 30 degree was selected to see the behavior of steering wheel on impact with the rigid steel plate. The contact force, impact plate displacement and component deformations were the key parameters to be observed and compared, in order to validate the FE steering wheel model. During physical tests for all the tilt angles, the steering wheel showed a stiff behavior regarding force level and horizontal displacement of the impact plate. Variation in peak force level during simulation and physical tests is less than 5% for 0 and 10 degree. The simulation results were considered validated for 78 mm and 66 mm of impact plate displacement for 0 and 10 degree respectively. Similarly, peak force level was found 26% and 45% higher in physical tests for 20 and 30 degree respectively. The simulation results were found out of the corridor limits, and FE model was not considered as validated for 20 and 30 degree tilt angles. Presence of foam is found one of the major differences between the steering wheel and its FE model, as this foam absorbs some energy from the impact plate. This foam is absent in FE model. For a better agreement of FE model with the steering wheel, FE model may need to be stiffer. Modeling of foam around the steering wheel can be the other alternative

Shared Aperture Multibeam Forming of Time-Modulated Linear Array

A novel technique is proposed in this paper for shared aperture multibeam forming in a complex time-modulated linear array. First, a uniform line array is interleaved randomly to form two sparse array subarrays. Subsequently, the theory of time modulation for linear arrays is applied in the constructed subarrays. In the meantime, the switch-on time sequences for each element of the two subarrays are optimized by an optimized differential evolution (DE) algorithm, i.e., the scaling factor of the sinusoidal iterative chaotic system and the adaptive crossover probability factor are used to enhance the diversity of the population. Lastly, the feasibility of the new technique is verified by the comparison between this technique and the basic multibeam algorithm in a shared aperture and the algorithm of iterative FFT. The results of simulations confirm that the proposed algorithm can form more desired beams, and it is superior to other similar approaches.Peer Reviewe