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Semidefinite programming-based localisation and tracking algorithm using Gaussian mixture modelling

IET COMMUNICATIONS 2017年第16期11卷 2514-2523页

作者： Zhang, Yueyue Zhu, Yaping Xia, Weiwei Yan, Feng Shen, Lianfeng Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Jiangsu Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

In this study, the authors propose a semidefinite programming (SDP)-based localisation and tracking algorithm, which mitigates the non-line-of-sight (NLOS) error of range measurement and calibrates the accumulative error within the inertial sensing data. Both the range measurement in a mixed line-of-sight/NLOS environment and the step length estimated from inertial sensing information are approximated parametrically using Gaussian mixture modelling, and a maximum-likelihood estimator (MLE) is formulated to obtain the optimal position estimation. Since the Gaussian mixture models are non-linear functions of positions, the MLE is a non-convex problem, which global optimum is difficult to attain. Then, the non-convex MLE is transformed into an SDP-based localisation and tracking problem, relying on Jensen's inequality and semidefinite relaxation. Thus, a sub-optimal solution to the original MLE can be achieved. Moreover, the Cramer-Rao lower bound is also derived to serve as a performance indicator for localisation errors. The simulation and experimental results demonstrate the performance of the proposed algorithm. Compared with the existing algorithms, the proposed algorithm owns the best localisation accuracy, and can achieve a sub-metre level accuracy to a root mean square error of 0.46m in the real deployments.

关键词： mathematical programming Gaussian processes mixture models maximum likelihood estimation mean square error methods semidefinite programming tracking algorithm localisation algorithm Gaussian mixture modelling SDP NLOS error inertial sensing data maximum-likelihood estimator MLE optimal position estimation nonlinear functions Jensen's inequality semidefinite relaxation root mean square error

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Improving the nowcasting of precipitation in an Alpine region with an enhanced radar echo tracking algorithm

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JOURNAL OF HYDROLOGY 2000年第1-4期239卷 46-68页

作者： Mecklenburg, S Joss, J Schmid, W Swiss Fed Inst Technol Zurich Switzerland Swiss Meteorol Inst Osservatorio Ticinese Locarno Monti Switzerland

Nowcasting for hydrological applications is discussed. The tracking algorithm extrapolates radar images in space and time. It originates from the pattern recognition techniques TREC (tracking Radar Echoes by Correlation, Rinehart and Garvey, J. Appl. Meteor.. 34 ( 1995) 1286) and COTREC (Continuity of TREC vectors, Li et al., Nature, 273 (1978) 287). To evaluate the quality of the extrapolation. a parameter scheme is introduced, able to distinguish between errors in the position and the intensity of the predicted precipitation. The parameters for the position are the absolute error, the relative error and the error of the forecasted direction. The parameters for the intensity are the ratio of the medians and the variations of the rain rate (ratio of two quantiles) between the actual and the forecasted image. To judge the overall quality of the forecast, the correlation coefficient between the forecasted and the actual radar image has been used. To improve the forecast, three aspects have been investigated: (a) Common meteorological attributes of convective cells, derived from a hail statistics, have been determined to optimize the parameters of the tracking algorithm. Using (a), the forecast procedure modifications (b) and (c) have been applied. (b) Small-scale features have been removed by using larger tracking areas and by applying a spatial and temporal smoothing, since problems with the tracking algorithm are mainly caused by small-scale/short-term variations of the echo pattern or because of limitations caused by the radar technique itself (erroneous vectors caused by clutter or shielding). (c) The searching area and the number of searched boxes have been restricted. This limits false detections, which is especially useful in stratiform precipitation and for stationary echoes. Whereas a larger scale and the removal of small-scale features improve the forecasted position for the convective precipitation, the forecast of the stratiform event is not influenced, bu

关键词： nowcasting quantitative precipitation forecasting (QPF) tracking algorithm cross-correlation method

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A robust occupancy detection and tracking algorithm for the automatic monitoring and commissioning of a building

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ENERGY AND BUILDINGS 2014年 77卷 270-280页

作者： Shih, Huang-Chia Yuan Ze Univ Dept Elect Engn Human Comp Interact Multimedia Lab Taoyuan Taiwan

The focus of this study is the 24 h a day monitoring of buildings for commissioning purposes. Based on an image-based depth sensor and a programmable pan-tilt-zoom (PTZ) camera, the proposed monitoring system enables the continuous detection and tracking of the occupants, even under dim-lighting conditions. The proposed SVM-based observation measurement provides a more reliable tracking performance. This paper presents a robust day-and-night people tracking and counting algorithm. The function of large-scale field monitoring is realized using a PTZ camera network instead of a conventional fixed camera. Furthermore, based on the depth image sensor, the contour information of the occupant can be applied for more accurate activity recognition. In our experiments we demonstrated the positive result of the occupancy detection and tracking algorithms applied to count people and monitor a building. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Building control Commissioning Building management system (BMS) Building monitoring Intelligent-controlled environment Occupancy detection tracking algorithm

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Accuracy Improvement of Indoor Real-Time Location tracking algorithm for Smart Supermarket Based on Ultra-Wideband

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2019年第12期33卷

作者： Hu, Guohua Feldhaus, Pascal Feng, Yuwu Wang, Shengjie Zheng, Juan Duan, Huimin Gu, Juanjuan Hefei Univ Dept Elect Informat & Elect Engn Hefei 230601 Anhui Peoples R China Stralsund Univ Appl Sci Dept Elect Engn & Comp Sci D-18435 Stralsund Germany

Collecting data like location information is an essential part of concepts like the "IoT" or "Industry 4.0". In the case of the development of a precise localization system and an integrated navigation system, indoor location technology receives more and more attention and has become a hot research topic. Common indoor location techniques are mainly based on wireless local area network, radio frequency tag, ZigBee technology, Bluetooth technology, infrared technology and ultra-wideband (UWB). However, these techniques are vulnerable to various noise signals and indoor environments, and also the positioning accuracy is easily affected by the complicated indoor environment. We studied the problem of real-time location tracking based on UWB in an indoor environment in this paper. We have proposed a combinational filtering algorithm and an improved Two-Way Ranging (ITWR) method for indoor real-time location tracking. The simulation results prove that the real-time performance and high accuracy of the presented algorithm can improve location accuracy. The experiment shows that the combinational algorithm and ITWR method which are applied to the positioning and navigation of the smart supermarket, have achieved quiet good results in positioning accuracy. The average positioning error is less than 10 cm, some of the improvements can elevate the positioning accuracy by 17.5%. UWB is a suitable method for indoor real-time location tracking and has important theoretic value and practical significance.

关键词： tracking algorithm indoor real-time locating system ultra-wideband improved Two-Way Ranging

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Fuzzy train tracking algorithm for the energy efficient operation of CBTC equipped metro lines

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2016年 53卷 19-31页

作者： Carvajal-Carreno, William Cucala, Asuncion P. Fernandez-Cardador, Antonio Pontifical Comillas Univ Inst Res Technol 23 Alberto Aguilera St Madrid 28015 Spain

In CBTC (Communications-Based Train Control) operated metro lines, headway can be reduced by taking advantage of the system's train track continuous communication, thus increasing transport capacity. When a train runs close enough to the preceding one, a tracking algorithm is triggered to control the distance between the trains. The following train receives the LMA (limit of movement authority) via radio, which is updated periodically as the preceding train runs. Besides transport capacity, one of railway operators' main goals is the reduction of energy consumption, for environmental and economic reasons. This paper firstly shows that the existing basic CBTC tracking algorithm is not energy efficient, due to the application of short braking traction cycles. It then proposes a new efficient algorithm that makes use of coast (null traction) command, and models the uncertainty associated with the speed of the preceding train. This fuzzy algorithm is compared to the basic one in terms of energy consumption, running time and steady state interval (resulting tracking interval). Simulation results show that the proposed, fuzzy tracking algorithm provides important energy savings with minor influence on running time and steady state interval. This algorithm is suitable for implementation in on-board CBTC equipment to reduce the energy consumption of traffic operation. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Energy-efficiency tracking algorithm Fuzzy parameters CBTC signaling system Moving block Train simulation Ecodriving ATO

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An eddy tracking algorithm based on dynamical systems theory

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OCEAN DYNAMICS 2016年第11期66卷 1415-1427页

作者： Conti, Daniel Orfila, Alejandro Mason, Evan Sayol, Juan Manuel Simarro, Gonzalo Balle, Salvador UIB CSIC IMEDEA Miquel Marques 21 Esporles 07190 Spain CSIC ICM Passeig Maritim Barceloneta 37-49 E-08003 Barcelona Spain

This work introduces a new method for ocean eddy detection that applies concepts from stationary dynamical systems theory. The method is composed of three steps: first, the centers of eddies are obtained from fixed points and their linear stability analysis;second, the size of the eddies is estimated from the vorticity between the eddy center and its neighboring fixed points, and, third, a tracking algorithm connects the different time frames. The tracking algorithm has been designed to avoid mismatching connections between eddies at different frames. Eddies are detected for the period between 1992 and 2012 using geostrophic velocities derived from AVISO altimetry and a new database is provided for the global ocean.

关键词： Coherent structures Global ocean Mesoscale eddies Dynamical systems theory Stability analysis of fixed points tracking algorithm

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Clarifying warhead separation from the reentry vehicle using a novel tracking algorithm

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2006年第5期4卷 529-538页

作者： Liu, Cheng-Yu Sung, Yu-Ming Lee Ming Inst Technol Dept Elect Engn Taipei 24305 Taiwan

Separating a reentry vehicle into warhead and body is a conventional and efficient means of producing a huge decoy and increasing the kinetic energy of the warhead. This procedure causes the radar to track the body, whose radar cross section is larger, and ignore the warhead, which is the most important part of the reentry vehicle. However, the procedure is difficult to perforin using standard tracking criteria. This study presents a novel tracking algorithm by integrating input estimation and modified probabilistic data association filter to solve this difficulty in a clear environment. The proposed algorithm with a new defined association probability in this filter provides a good tracking capability for the warhead ignoring the radar cross section. The simulation results indicate that the errors between the estimated and the warhead trajectories are reduced to a small interval in a short time. Therefore, the radar can produce a beam to illuminate to the right area and keep tracking the warhead all the way. In conclusion, this algorithm is worthy of further study and application.

关键词： input estimation probabilistic data association filter reentry vehicle tracking algorithm trajectory estimation

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Robust video tracking algorithm: a multi-feature fusion approach

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IET COMPUTER VISION 2018年第5期12卷 640-650页

作者： Wang, Howard Nguang, Sing Kiong Wen, Jiwei Univ Auckland Dept Elect & Comp Engn Auckland New Zealand Jiangnan Univ Dept Automat Wuxi Peoples R China

This study proposes a novel robust video tracking algorithm consists of target detection, multi-feature fusion, and extended Camshift. Firstly, a novel target detection method that integrates Canny edge operator, three-frame difference, and improved Gaussian mixture model (IGMM)-based background modelling is provided to detect targets. The IGMM-based background modelling divides video frames into meshes to avoid pixel-wise processing. In addition, the output of the target detection is utilised to initialise the IGMM and to accelerate the convergence of iterations. Secondly, low-dimensional regional covariance matrices are introduced to describe video targets by fusing multiple features like pixel location, colour index, rotation and scale invariant features as well as uniform local binary patterns, and directional derivatives. Thirdly, an extended Camshift based on adaptive kernel bandwidth and robust H state estimation is proposed to predict the states of fast moving targets and to reduce the mean shift iterations. Finally, the effectiveness of the proposed tracking algorithm is demonstrated via experiments.

关键词： image fusion feature extraction video signal processing edge detection Gaussian processes mixture models covariance matrices robust video tracking algorithm multifeature fusion approach target detection extended Camshift target detection method Canny edge operator three-frame difference improved Gaussian mixture model IGMM-based background modelling video frames low-dimensional regional covariance matrices video targets multiple feature fusion pixel location colour index scale invariant features local binary patterns directional derivatives adaptive kernel bandwidth robust H state estimation mean shift iteration reduction tracking algorithm

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The Improvement and Application of Intelligence tracking algorithm for Moving Logistics Objects Based on Machine Vision Sensor

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SENSOR LETTERS 2013年第5期11卷 862-869页

作者： Zhang, Shu Shan Wachs, Juan P. NE Normal Univ Ser Ind Operat & Control Res Ctr Changchun 130117 Peoples R China Purdue Univ Sch Ind Engn W Lafayette IN 47906 USA

Logistics object tracking is one of the key technologies of intelligence logistics management system (ILMS). Using machine vision sensor technology, the paper introduced the application method of machine vision sensor technology in intelligent logistics system, and proposed an improved and simple covariance matrix algorithm to detect and track moving logistics objects. Against the features and technical difficulties of moving logistics objects detection, the covariance matrix algorithm was applied to detecting and tracking of logistics objects, and against the shortcomings of covariance matrix algorithm in the process of detecting and tracking of moving logistics objects, the paper presented a method of logistics objects path prediction, and template image dynamic selection and adjustment. Experiments show that the method can effectively apply the improved covariance matrix algorithm to the detecting and tracking of moving logistics objects, the method can not only adapt quickly to pose and scale variations of logistics objects, but also track accurately and continuously those temporarily occluded logistics objects, the improved method has good robustness. The method provides a new solution of the detecting and tracking of moving logistics objects.

关键词： Machine Vision Sensor Covariance Matrix tracking algorithm Logistics Objects

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PARALLEL IMPLEMENTATION OF IMM tracking algorithm USING TRANSPUTERS

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IEE PROCEEDINGS-RADAR SONAR AND NAVIGATION 1994年第6期141卷 325-332页

作者： ATHERTON, DP LIN, HJ CHUNG SHAN INST SCI & TECHNOL TAOYUNGTAIWAN

The paper discusses the implementation of the interacting multiple model (IMM) target tracking algorithm on a transputer network. Particular interest is focused on the efficient implementation of the algorithm in the parallel processing design. The experimental results show that for a parallel implementation with four transputers, a speedup of around three can be obtained.

关键词： INTERACTIVE MULTIPLE MODEL tracking algorithm TRANSPUTERS

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