發(fā)布時(shí)間：2018-05-18 04:39

  本文選題：擴(kuò)展目標(biāo)跟蹤 + 概率假設(shè)密度濾波��； 參考：《系統(tǒng)仿真學(xué)報(bào)》2017年01期

【摘要】：擴(kuò)展目標(biāo)跟蹤過(guò)程中,若出現(xiàn)目標(biāo)交叉,直接采用擴(kuò)展目標(biāo)高斯混合概率假設(shè)密度濾波算法會(huì)出現(xiàn)目標(biāo)漏估計(jì)。針對(duì)該問(wèn)題,提出了一種改進(jìn)算法。計(jì)算每一時(shí)刻跟蹤到的目標(biāo)間的歐式距離,以此判定目標(biāo)是否處于臨近區(qū)域。在下一時(shí)刻,若臨近區(qū)域內(nèi)跟蹤到的目標(biāo)數(shù)目突然變少,則對(duì)臨近區(qū)域內(nèi)目標(biāo)對(duì)應(yīng)的高斯分量權(quán)值進(jìn)行補(bǔ)償;否則看作是正常的目標(biāo)消亡現(xiàn)象,不作處理。使用處理后的高斯分量進(jìn)行目標(biāo)估計(jì)和跟蹤。改進(jìn)算法解決了因量測(cè)集分布緊密而被劃分到同一個(gè)子集帶來(lái)的目標(biāo)數(shù)目漏估計(jì)的問(wèn)題。仿真實(shí)驗(yàn)結(jié)果表明了改進(jìn)算法的精確性與有效性。
[Abstract]:In the process of extended target tracking, if the target cross occurs, the extended target Gao Si hybrid probability assumption density filtering algorithm will be used to estimate the target missing. To solve this problem, an improved algorithm is proposed. The Euclidean distance between the targets tracked at each time is calculated to determine whether the target is in an adjacent area. At the next moment, if the number of targets tracked in the adjacent region suddenly becomes smaller, the weight of the Gao Si component corresponding to the target in the adjacent region is compensated; otherwise, it is regarded as a normal phenomenon of target extinction and is not dealt with. The processed Gao Si component is used for target estimation and tracking. The improved algorithm solves the problem of missing estimation of the number of targets which is divided into the same subset due to the tight distribution of the measurement set. Simulation results show the accuracy and effectiveness of the improved algorithm.
【作者單位】： 西安工程大學(xué)計(jì)算機(jī)科學(xué)學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(61201118) 中國(guó)博士后科學(xué)基金(2103M532020) 陜西省自然科學(xué)基礎(chǔ)研究計(jì)劃項(xiàng)目(2016JM6030)
【分類號(hào)】：TN713
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本文編號(hào)：1904430