本文選題：隨機有限集　切入點：擴展目標　出處：《西安電子科技大學》2015年碩士論文　論文類型：學位論文

【摘要】：由于雷達和傳感器的分辨率隨科技進步而不斷提高,得到的同一個目標的量測不止一個,此時,目標需看作是擴展目標,如果仍然使用傳統(tǒng)的方法將量測和目標相關(guān)聯(lián)進行跟蹤已無法滿足現(xiàn)狀。近些年,基于隨機有限集的多目標跟蹤方法由于能夠有效地解決傳統(tǒng)跟蹤方法中出現(xiàn)的一些難題,并且在計算復雜度方面明顯優(yōu)于傳統(tǒng)算法,所以受到了廣泛學者的認同。本文主要針對擴展目標中基于形狀估計的隨機集多目標跟蹤方法展開研究。主要研究內(nèi)容如下:1.針對K-means聚類劃分方法過分依賴初始中心點以及效率低的問題,提出了一種基于均值漂移聚類的改進劃分方法。該方法將高斯函數(shù)作為內(nèi)核函數(shù),采用均值漂移方法求出吸引盆,并將吸引盆進行合并,最后將噪聲孤點去除,得到最終的量測劃分集合。該方法不需要給出具體的聚類數(shù)目和中心點,可得到良好的劃分效果。2.針對距離劃分方法在目標相鄰時無法進行準確劃分的問題,提出了一種基于距離劃分的改進方法。該方法在距離劃分的基礎上進行二次劃分,使用極大似然估計來估計每個劃分集合中的目標數(shù),對目標數(shù)大于1的劃分集合進行再劃分,從而將每個擴展目標的量測劃分開。該方法可以有效地處理目標相鄰或交叉時距離劃分無法區(qū)分的情況。3.針對現(xiàn)有擴展目標的建模使用橢圓建模而無法分辨星形形狀的問題,提出了一種基于星凸型隨機超曲面的伽瑪高斯混合勢概率假設密度擴展目標跟蹤算法(SRHM-GGM-CPHD)。該算法將擴展目標的形狀建模為星凸形,并將其嵌入到伽瑪高斯混合CPHD濾波器框架中,完成對多個擴展目標的跟蹤。該算法在質(zhì)心位置和擴展形狀的估計精度方面要優(yōu)于傳統(tǒng)的基于隨機矩陣的伽瑪高斯逆威舍特CPHD濾波器。
[Abstract]:Since the resolution of radar and sensors has improved with technological advances, more than one measurement of the same target has been obtained, where the target needs to be viewed as an extended target. In recent years, the multi-target tracking method based on stochastic finite set can effectively solve some difficult problems in traditional tracking methods. And the computational complexity is obviously better than the traditional algorithm, This paper mainly focuses on the shape estimation based random set multi-target tracking method in extended targets. The main contents of the research are as follows: 1. K-means clustering method is too dependent on the initial. The center point and the problem of inefficiency, An improved partition method based on mean shift clustering is proposed, in which Gao Si function is taken as kernel function, mean shift method is used to calculate the suction basin, and the suction basin is merged, finally the noise is removed from the isolated point. The final measurement partition set is obtained. This method does not need to give specific clustering number and center point, and can get good partition effect. 2. Aiming at the problem that the distance partition method can not be accurately partitioned when the target is adjacent, An improved method based on distance partition is proposed, in which the quadratic partition is based on the distance partition. The maximum likelihood estimation is used to estimate the number of objects in each partition set, and the partition set with more than one target number is repartitioned. Thus, the measurement of each extended target can be divided. The method can effectively deal with the problem that the distance partition between adjacent or crossing targets can not be distinguished. 3. For the existing extended target modeling, the elliptical model is used but the star shape can not be distinguished. In this paper, a hybrid probability assumption density extended target tracking algorithm for gamma Gao Si based on star convex random hypersurface is proposed. The extended target shape is modeled as a star convex shape by this algorithm and embedded in the framework of Gamma Gao Si hybrid CPHD filter. The algorithm is superior to the conventional Gamma Gao Si inverse Weschet CPHD filter based on random matrix in the estimation accuracy of centroid position and extended shape.
【學位授予單位】：西安電子科技大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TN713

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本文編號：1638105