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High manoeuvre target tracking in coordinated turns

IET RADAR SONAR AND NAVIGATION 2015年第8期9卷 1078-1087页

作者： Zhu, Lihua Cheng, Xianghong Southeast Univ Key Lab Micro Inertial Instrument & Adv Nav Nanjing 210096 Jiangsu Peoples R China

In target tracking, most tracking algorithms are model based, and serious errors can arise when the models fail to fit the physical target motions, especially during coordinated turns. To address this problem, a two-step tracking algorithm is proposed. First, considering the diversity of the target manoeuvres, five elemental Singer models with carefully designed parameter alpha interact with each other in an interacting multiple model (IMM) algorithm to accomplish the tracking and estimate the target's kinematic parameters. Second, if a turn motion occurs, then it discriminates the turn motion into a horizontal turn or a three-dimensional (3D) turn;and the real-time turn rate is calculated to refine the corresponding model for tracking. Since the filtering stage is important in estimating the kinematic parameters, sparse-grid quadrature Kalman filter is employed to improve the filtering capability. The proposed algorithm is exemplified by simulation tests and data tests in 3D, and is compared with that of an IMM algorithm utilising constant velocity, constant acceleration and 3D coordinated turn models. All of the test results demonstrate that the proposed algorithm is effective and has higher accuracy.

关键词： target tracking Kalman filters high manoeuvre target tracking coordinated turns tracking algorithms physical target motions target manoeuvres Singer models interacting multiple model IMM algorithm target kinematic parameters kinematic parameters sparse-grid quadrature Kalman filter filtering capability

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Optimal tracking of Distributed Heavy Hitters and Quantiles

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ALGORITHMICA 2013年第1期65卷 206-223页

作者： Yi, Ke Zhang, Qin Hong Kong Univ Sci & Technol Kowloon Hong Kong Peoples R China Aarhus Univ MADALGO Ctr Mass Data Algorithm Aarhus Denmark

We consider the problem of tracking heavy hitters and quantiles in the distributed streaming model. The heavy hitters and quantiles are two important statistics for characterizing a data distribution. Let A be a multiset of elements, drawn from the universe U={1,aEuro broken vertical bar,u}. For a given 0a parts per thousand currency signI center dot a parts per thousand currency sign1, the I center dot-heavy hitters are those elements of A whose frequency in A is at least I center dot|A|;the I center dot-quantile of A is an element x of U such that at most I center dot|A| elements of A are smaller than A and at most (1-I center dot)|A| elements of A are greater than x. Suppose the elements of A are received at k remote sites over time, and each of the sites has a two-way communication channel to a designated coordinator, whose goal is to track the set of I center dot-heavy hitters and the I center dot-quantile of A approximately at all times with minimum communication. We give tracking algorithms with worst-case communication cost O(k/I mu a <...logn) for both problems, where n is the total number of items in A, and I mu is the approximation error. This substantially improves upon the previous known algorithms. We also give matching lower bounds on the communication costs for both problems, showing that our algorithms are optimal. We also consider a more general version of the problem where we simultaneously track the I center dot-quantiles for all 0a parts per thousand currency signI center dot a parts per thousand currency sign1.

关键词： QUANTILES tracking algorithms DATA distribution DATABASES COMMUNICATION models MATHEMATICAL models MATHEMATICAL optimization

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A dynamic lookahead tree based tracking algorithm for wireless sensor networks using particle filtering technique

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COMPUTERS & ELECTRICAL ENGINEERING 2014年第2期40卷 374-383页

作者： Alaybeyoglu, Aysegul Kantarci, Aylin Erciyes, Kayhan Celal Bayar Univ Dept Comp Engn Manisa Turkey Ege Univ Dept Comp Engn Izmir Turkey Izmir Univ Dept Comp Engn Izmir Turkey

In this study, five different algorithms are provided for tracking targets that move very fast in wireless sensor networks. The first algorithm is static and clusters are formed initially at the time of network deployment. In the second algorithm, clusters that have members at one hop distance from the cluster head are provided dynamically. In the third algorithm, clustered trees where members of a cluster may be more than one hop distance from the cluster head are provided dynamically. In the fourth, algorithm lookahead trees are formed along the predicted trajectory of the target dynamically. Linear, Kalman and particle filtering techniques are used to predict the target's next state. The algorithms are compared for linear and nonlinear motions of the target against tracking accuracy, energy consumption and missing ratio parameters. Simulation results show that, for all cases, better performance results are obtained in the dynamic lookahead tree based tracking approach. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： DYNAMICAL systems tracking algorithms WIRELESS sensor networks MONTE Carlo method NONLINEAR systems ENERGY consumption

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Parallel algorithm implementation for multi-object tracking and surveillance

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IET COMPUTER VISION 2016年第3期10卷 202-211页

作者： Elbahri, Mohamed Taleb, Nasreddine Kpalma, Kidiyo Ronsin, Joseph Univ Djillali Liabes Sidi Bel Abbes Dept Comp Sci Sidi Bel Abbes Algeria Univ Djillali Liabes Sidi Bel Abbes Dept Elect RCAM Lab Sidi Bel Abbes Algeria UEB INSA IETR Dept Image & Automat F-35708 Rennes France

A recently developed sparse representation algorithm, has been proved to be useful for multi-object tracking and this study is a proposal for developing its parallelisation. An online dictionary learning is used for object recognition. After detection, each moving object is represented by a descriptor containing its appearance features and its position feature. Any detected object is classified and indexed according to the sparse solution obtained by an orthogonal matching pursuit (OMP) algorithm. For a real-time tracking, the visual information needs to be processed very fast without reducing the results accuracy. However, both the large size of the descriptor and the growth of the dictionary after each detection, slow down the system process. In this work, a novel accelerating OMP algorithm implementation on a graphics processing unit is proposed. Experimental results demonstrate the efficiency of the parallel implementation of the used algorithm by significantly reducing the computation time.

关键词： Parallel algorithms tracking algorithms Orthogonal matching pursuit Feature extraction (Data processing) Computer software

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A range-dependent echo-association algorithm and its application in split-beam sonar tracking of migratory salmon in the Fraser River watershed

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IEEE JOURNAL OF OCEANIC ENGINEERING 2000年第3期25卷 387-398页

作者： Xie, YB Pacific Salmon Commiss Vancouver BC V6E 1B5 Canada

Fish sonars have long been used to survey and monitor migratory salmon in rivers and at sea. However, research has been lacking in the development of algorithms to extract fish tracks from data collected in a riverine or oceanic environment. Current fish trackers, based on a pulse-to-pulse tracking method, only work well under ideal conditions when targets are well separated and the signal-to-noise ratio is high. Fisheries biologists often have to identify fish traces visually from raw echograms, This approach is both labor-intensive and time-consuming, limiting the usefulness of hydroacoustic techniques for fisheries management. This paper presents a fish-tracking algorithm which sorts randomly distributed echoes into coherent fish traces. Fish counts obtained with the algorithm compare well with visual counts at two quite different sites in the Fraser River watershed. The key features of the algorithm are: 1) the linking mechanisms among sequential fish echoes are range-dependent;2) the growth echo for a developing track depends not only on its space-time relation with the previous track echo (the pulse-to-pulse statistics) but also on its relation to the entire track being constructed;3) there are a total of only five echo-association criteria in the algorithm;4) the simplicity of the algorithm structure provides a convenient platform for implementing specific and sophisticated tracking criteria to meet specific needs;and 5) the user can fully control the performance of the algorithm by choosing values for the 11 well-defined tracking parameters.

关键词： fisheries acoustics Pacific salmon remote sensing split-beam sonar tracking algorithms

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Bayesian information fusion and multitarget tracking for maritime situational awareness

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IET RADAR SONAR AND NAVIGATION 2020年第12期14卷 1845-1857页

作者： Gaglione, Domenico Soldi, Giovanni Meyer, Florian Hlawatsch, Franz Braca, Paolo Farina, Alfonso Win, Moe Z. NATO Ctr Maritime Res & Expt CMRE La Spezia Italy Univ Calif San Diego Scripps Inst Oceanog La Jolla CA 92093 USA Univ Calif San Diego Elect & Comp Engn Dept La Jolla CA 92093 USA TU Wien Inst Telecommun Vienna Austria MIT Lab Informat & Decis Syst LIDS 77 Massachusetts Ave Cambridge MA 02139 USA

The goal of maritime situational awareness (MSA) is to provide a seamless wide-area operational picture of ship traffic in coastal areas and the oceans in real time. Radar is a central sensing modality for MSA. In particular, oceanographic high-frequency surface-wave (HFSW) radars are attractive for surveying large sea areas at over-the-horizon distances, due to their low environmental footprint and low power requirements. However, their design is not optimal for the challenging conditions prevalent in MSA applications, thus calling for the development of dedicated information fusion and multisensor-multitarget tracking algorithms. In this study, the authors show how the multisensor-multitarget tracking problem can be formulated in a Bayesian framework and efficiently solved by running the loopy sum-product algorithm on a suitably devised factor graph. Compared to previously proposed methods, this approach is advantageous in terms of estimation accuracy, computational complexity, implementation flexibility, and scalability. Moreover, its performance can be further enhanced by estimating unknown model parameters in an online fashion and by fusing automatic identification system (AIS) data and context-based information. The effectiveness of the proposed Bayesian multisensor-multitarget tracking and information fusion algorithms is demonstrated through experimental results based on simulated data as well as real HFSW radar data and real AIS data.

关键词： target tracking ships oceanographic techniques sensor fusion radar detection Bayes methods radar tracking geophysical signal processing marine radar Bayesian information fusion maritime situational awareness wide-area operational picture ship traffic coastal areas central sensing modality high-frequency surface-wave radars sea areas over-the-horizon distances low environmental footprint low power requirements MSA applications dedicated information fusion tracking algorithms multisensor-multitarget tracking problem Bayesian framework loopy sum-product algorithm suitably devised factor graph authors context-based information information fusion algorithms HFSW radar data

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Problem of tracking in linear systems with parametric uncertainties under unstable zero dynamics

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AUTOMATION AND REMOTE CONTROL 2014年第9期75卷 1577-1592页

作者： Utkin, V. A. Utkin, A. V. Russian Acad Sci Trapeznikov Inst Control Sci Moscow Russia

Proposed was a solution to the problem of tracking in linear SISO systems with unstable zero dynamics in the conditions of parametric uncertainty of the models of plant and reference generator under incomplete information about the state vectors. The approach developed employs the methods of the sliding mode theory in the problems of real-time feedback design, observation, and parameter identification.

关键词： tracking algorithms FEEDBACK control systems CLOSED loop systems CANONICAL correlation (Statistics) LINEAR dynamical systems

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An improved Morris Water Maze tracking algorithm for psychophysical studies

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ANNALS OF BIOMEDICAL ENGINEERING 2004年第8期32卷 1141-1152页

作者： Hval, ES Barrett, SF Wilcox, M Bailey, TA Univ Wyoming Coll Engn Dept Elect & Comp Engn Laramie WY 82071 USA USAF Acad Dept Biol Colorado Springs CO USA Univ Wyoming Coll Engn Dept Comp Sci Laramie WY 82071 USA

An algorithm to track a rat swimming in a Morris Water Maze has been developed. The system is automatically configured to any pool and relative suitable light conditions. It tracks the rat's position and head pose 10 times per second. The output data is displayed in a bitmap and also in a text file. The system was tested with an X - Y plotter using a simulated rat swimming in the maze. Known signals were provided to a model rat and compared to the position and pose information provided by the tracking algorithm. The algorithm was able to track rat velocities up to 2.32 m/s, localize rat position to 4 mm within the maze. and provide head pose information. Early prototypes of the algorithm were also used to track actual rats in a water maze.

关键词： Morris Water Maze tracking algorithms template matching

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tracking Virus Particles in Fluorescence Microscopy Images Using Multi-Scale Detection and Multi-Frame Association

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2015年第11期24卷 4122-4136页

作者： Jaiswal, Astha Godinez, William J. Eils, Roland Lehmann, Maik Joerg Rohr, Karl Heidelberg Univ BIOQUANT Ctr Biomed Comp Vis Grp Dept Bioinformat & Funct GenomInst Pharm & Mol B D-69117 Heidelberg Germany German Canc Res Ctr Heidelberg 69120 Germany Novartis Inst Biomed Res Basel Switzerland Humboldt Univ Dept Mol Parasitol D-10115 Berlin Germany

Automatic fluorescent particle tracking is an essential task to study the dynamics of a large number of biological structures at a sub-cellular level. We have developed a probabilistic particle tracking approach based on multi-scale detection and two-step multi-frame association. The multi-scale detection scheme allows coping with particles in close proximity. For finding associations, we have developed a two-step multi-frame algorithm, which is based on a temporally semiglobal formulation as well as spatially local and global optimization. In the first step, reliable associations are determined for each particle individually in local neighborhoods. In the second step, the global spatial information over multiple frames is exploited jointly to determine optimal associations. The multi-scale detection scheme and the multi-frame association finding algorithm have been combined with a probabilistic tracking approach based on the Kalman filter. We have successfully applied our probabilistic tracking approach to synthetic as well as real microscopy image sequences of virus particles and quantified the performance. We found that the proposed approach outperforms previous approaches.

关键词： Virus particle tracking multi-frame association multi-scale particle detection Kalman filter tracking algorithms

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Salient feature selection for visual tracking

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ELECTRONICS LETTERS 2012年第18期48卷 1123-U150页

作者： Kang, W. -S. Na, J. H. Choi, J. Y. Samsung Elect Suwon 443742 Gyeonggi Do South Korea Seoul Natl Univ Dept Elect Engn & Comp Sci Seoul 151600 South Korea

Proposed is a novel method that can adaptively extract discriminative features and learn the target region accurately for object tracking. Only the region selected as salient pixels by the proposed weighted log likelihood ratio is employed, instead of using all data in the tracker window, for learning the object appearance accurately. The selected pixels are used to train a new weighted likelihood ratio which is employed to select new salient pixels. The proposed method has a recursive structure between selecting salient pixels and learning the weighted likelihood ratio. Experimental results show that the approach by the proposed adaptive feature selection is effective to adapt to object appearance change and alleviate tracking drift or the occlusion problem.

关键词： tracking algorithms tracking control systems (Engineering) AUTOMATIC tracking PIXELS COMPUTER vision

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