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Study On Multiple Moving Targets Passive Tracking And Data Association Algorithms

Posted on:2004-07-10Degree:DoctorType:Dissertation
Country:ChinaCandidate:Y S LinFull Text:PDF
GTID:1118360122971277Subject:Control Science and Engineering
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Multisensor-multitarget tracking and data associationarethe kernel content of multisensor-multitarget data fusion, which includes two parts: target tracking and data association. This technology is widely applied in the field of military and civil, many people are interested in it. It is a difficulty that continuous tracks multiple targets with dense ghost in admissible time.Passive detect sensor has the advantage which can't easy to be detected by enemy, but single sensor can not get all parameters of moving targets. Multisensor's information is needed to obtain all parameters of move targets. If the information has the time-delay characteristic such as sound, it is needed to adjust all the data from sensors. These factors make the problem of multiple moving targets passive tracking and data association become especially difficulty, many algorithms become helpless in this problem which are available in active multisensor-multitarget tracking and data association.This paper studies on algorithms of multiple moving targets passive tracking and data association in the field of passive acoustic detective network forwarning system, the major contributions and innovations are as following:1. In this paper, We describe the study background, meaning and methods of passive acoustic detective network, summarize the basic theories and methods of target tracking and data association, analyze some tipical data association algorithms include the Nearest Neighbor algorithm(NN), Probabilistic Data Association Filtering(PDAF), Joint Probabilistic Data Association Filtering(JPDAF), Multiple Hypothesis Tracking(MHT), and Multidimensional S-D Assignment algorithm.2. In detective network, sometimes a surveillance region have only single sensor. In this paper, there are presented that a single stationary station single target passive bearings-only tracking and data association algorithm and a simplified single stationary station multiple target passive bearing-only tracking and data association algorithm, then a single stationary station multiple target tracking problem can be regarded as asingle stationary station single target tracking problem.3. Based on least square method, this paper presents the line-of-sight location method of multiple stationary station single target. Considering the time delay of sound transfer, a least square line-of-sight location method is presented in this paper.4. Filtration matrix is developed to make the problem of multiple target tracking in net can be regarded as a problem of multisensor-multitarget tracking in a same region. A time and space joint probabilistic data association algorithm is developed to solve the difficult problem of passive multisensor-multitarget tracking.5. In many data fusion systems, process noise and/or measurement noise may have unknown statistics characteristics but limited power. In this paper weobtain a characterization of all H∞ fusion filters based on DAREs and LMIs, respectively.6. This paper is concerned with the problem of H∞ -norm and thevariance-cost-guaranteed filter for uncertainty systems, in which 1) the process noise and measurement noise have unknown characteristics but limited power; 2) the state and measurement matrices have time-varyingnorm-bounded parameter uncertainty. A new robust H∞ filter based onLMI approach has been developed to solve the above problem.7. Finally, a brief review of this paper is given, and the future research directions are proposed.
Keywords/Search Tags:Data fusion, passive tracking, data association, bearings-only, time and space joint probabilistic data association, linear matrix inequality(LMI), robust H∞ filter
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