本文選題：隨機(jī)有限集　切入點(diǎn)：擴(kuò)展目標(biāo)　出處：《西安電子科技大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：由于雷達(dá)和傳感器的分辨率隨科技進(jìn)步而不斷提高,得到的同一個(gè)目標(biāo)的量測不止一個(gè),此時(shí),目標(biāo)需看作是擴(kuò)展目標(biāo),如果仍然使用傳統(tǒng)的方法將量測和目標(biāo)相關(guān)聯(lián)進(jìn)行跟蹤已無法滿足現(xiàn)狀。近些年,基于隨機(jī)有限集的多目標(biāo)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(jì)算復(fù)雜度方面明顯優(yōu)于傳統(tǒng)算法,所以受到了廣泛學(xué)者的認(rèn)同。本文主要針對擴(kuò)展目標(biāo)中基于形狀估計(jì)的隨機(jī)集多目標(biāo)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(diǎn)以及效率低的問題,提出了一種基于均值漂移聚類的改進(jìn)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(jìn)行合并,最后將噪聲孤點(diǎn)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(diǎn),可得到良好的劃分效果。2.針對距離劃分方法在目標(biāo)相鄰時(shí)無法進(jìn)行準(zhǔn)確劃分的問題,提出了一種基于距離劃分的改進(jìn)方法。該方法在距離劃分的基礎(chǔ)上進(jìn)行二次劃分,使用極大似然估計(jì)來估計(jì)每個(gè)劃分集合中的目標(biāo)數(shù),對目標(biāo)數(shù)大于1的劃分集合進(jìn)行再劃分,從而將每個(gè)擴(kuò)展目標(biāo)的量測劃分開。該方法可以有效地處理目標(biāo)相鄰或交叉時(shí)距離劃分無法區(qū)分的情況。3.針對現(xiàn)有擴(kuò)展目標(biāo)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(jī)超曲面的伽瑪高斯混合勢概率假設(shè)密度擴(kuò)展目標(biāo)跟蹤算法(SRHM-GGM-CPHD)。該算法將擴(kuò)展目標(biāo)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(gè)擴(kuò)展目標(biāo)的跟蹤。該算法在質(zhì)心位置和擴(kuò)展形狀的估計(jì)精度方面要優(yōu)于傳統(tǒng)的基于隨機(jī)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(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TN713

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本文編號：1638105