發(fā)布時間：2018-07-04 09:24

  本文選題：可分辨群目標 + 圖理論　； 參考：《杭州電子科技大學》2017年碩士論文

【摘要】：群目標由多個相互協(xié)作的目標組成,并且保持著一定結(jié)構(gòu)進行運動。隨著傳感器技術的不斷發(fā)展,原來不可分辨的群目標呈現(xiàn)出可分辨的屬性,群目標跟蹤算法的研究也越來越重要。跟蹤估計主要包括獲得目標的量測,并對量測進行濾波和估計。因此,需要對群目標進行動態(tài)建模,并采用合適的算法對群目標進行跟蹤估計�，F(xiàn)在對群目標的動態(tài)建模主要是將群目標中的各成員的狀態(tài)進行簡單的結(jié)合,但是該方法不能充分體現(xiàn)出群目標各成員之間的依賴關系。目前的群目標跟蹤研究主要集中在不可分辨群目標或者部分可分辨群目標上,如果將不可分辨群目標的跟蹤方法應用于可分辨群目標,會導致估計過于粗糙從而丟失許多結(jié)構(gòu)信息。為了解決上述問題,本文使用圖理論結(jié)合標簽隨機有限集(L-RFS)理論,并且引進目標的可分辨系數(shù),給出在可分辨情況下群目標新的動態(tài)建模方法和跟蹤估計方法,主要工作包括:(1)可分辨群目標的動態(tài)建模。根據(jù)群結(jié)構(gòu)與圖結(jié)構(gòu)的相似性,利用描述有向圖的方法對群進行描述,即使用鄰接矩陣來描述群。再通過鄰接矩陣判斷群中各成員的依賴關系。由于頭結(jié)點起到領導作用,因此可以對頭結(jié)點進行獨自建模。再建立其它依賴于頭結(jié)點的子結(jié)點的動態(tài)模型,并依次建立其它結(jié)點的動態(tài)模型。(2)群目標可分辨系數(shù)。由于雷達的分辨率會隨著探測距離的增大而變低,使獲得的量測誤差變大。假設該誤差服從正態(tài)分布,而正態(tài)分布的均值到邊緣的距離大概為標準差?的3倍,因此,本文令群目標的可分辨距離i,jr為3 3i j?(10)?,其中i和j表示目標i和目標j。當群目標中各對成員的距離大于i,jr時,認為群目標是可分辨的。(3)可分辨群目標的跟蹤算法的研究。第一步,由于起始階段群目標之間的協(xié)作關系未知,因此假設群目標之間是獨立的并采用廣義標簽多伯努利(GLMB)濾波算法獲得各目標的狀態(tài)估計和軌跡估計以及目標的個數(shù)估計。第二步,在獲得群目標中各成員的狀態(tài)估計基礎上,通過計算每時刻的偏差矩陣估計獲得鄰接矩陣估計,并通過鄰接矩陣得到群的結(jié)構(gòu)關系。再利用圖論中連通圖的概念獲得各時刻子群的個數(shù)估計。
[Abstract]:Group targets are composed of multiple cooperative targets and move in a certain structure. With the continuous development of sensor technology, the original indistinguishable group targets show discernible attributes, and the research of swarm target tracking algorithm is becoming more and more important. Tracking estimation mainly includes obtaining the measurement of the target and filtering and estimating the measurement. Therefore, it is necessary to dynamically model the group target and use the appropriate algorithm to track and estimate the group target. At present, the dynamic modeling of group objects is mainly based on the simple combination of the states of each member of the group object, but this method can not fully reflect the dependency relationship among the members of the group object. The current research on group target tracking is mainly focused on indiscernible group targets or partially discernible group targets. If the tracking method of indiscernible group targets is applied to discernible group targets, This can result in rough estimates and the loss of a lot of structural information. In order to solve the above problems, this paper uses graph theory combined with label random finite set (L-RFS) theory, and introduces the discernibility coefficient of targets, and gives a new dynamic modeling method and tracking estimation method for group targets under discernible conditions. The main work includes: (1) dynamic modeling of discernible group targets. According to the similarity between group structure and graph structure, the method of describing directed graph is used to describe the group, that is, the adjacent matrix is used to describe the group. Then the dependency of each member in the group is judged by the adjacency matrix. Because the head node plays a leading role, we can model the head node alone. Then the dynamic models of other child nodes dependent on the head node are established, and the dynamic models of the other nodes are established in turn. (2) the discernibility coefficient of the group targets. Because the resolution of radar decreases with the increase of detection range, the measurement error becomes larger. Suppose the error is from normal distribution, and the distance from the mean to the edge of the normal distribution is approximately standard deviation? Therefore, in this paper, the discernible distance ig jr of the group target is 3 3i jr (10), where I and j denote the target I and the target j. When the distance of each pair of members in a group target is greater than that of ijr, it is considered that the group target is distinguishable. (3) the research of tracking algorithm for discernible group target In the first step, because of the unknown cooperative relationship between group targets in the initial stage, it is assumed that the group targets are independent and the generalized label multi-Bernoulli (GLMB) filtering algorithm is used to obtain the state estimation and trajectory estimation of each target and the estimation of the number of targets. In the second step, on the basis of the state estimation of each member of the group target, the adjacent matrix estimation is obtained by calculating the deviation matrix estimation at every moment, and the structure relation of the group is obtained by the adjacent matrix. Then, by using the concept of connected graph in graph theory, the number of subgroups at each time is estimated.
【學位授予單位】：杭州電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN713

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