【摘要】：目標(biāo)跟蹤由于應(yīng)用廣泛,受到了專(zhuān)家學(xué)者的普遍關(guān)注。在實(shí)際的跟蹤場(chǎng)景中,感興趣的目標(biāo)往往不止一個(gè),隨著運(yùn)動(dòng)目標(biāo)的出現(xiàn)和消失,目標(biāo)的數(shù)目也是實(shí)時(shí)變化的,相應(yīng)的多目標(biāo)跟蹤技術(shù)取得了巨大的發(fā)展。箱粒子濾波是近年來(lái)新提出的一種廣義的粒子濾波方法,具有所需粒子數(shù)目少,計(jì)算復(fù)雜度低,計(jì)算效率高等優(yōu)點(diǎn)。本文在箱粒子濾波基礎(chǔ)上,對(duì)多目標(biāo)跟蹤方法進(jìn)行了深入研究。介紹了箱粒子濾波的理論基礎(chǔ)。箱粒子濾波本質(zhì)上是廣義的粒子濾波算法,它將區(qū)間分析這一數(shù)學(xué)工具與傳統(tǒng)的蒙特卡洛算法相結(jié)合,用箱粒子代替了最大誤差已知的點(diǎn)粒子,是一種處理非精確量測(cè)的方法。跟傳統(tǒng)的粒子濾波算法相比箱粒子濾波算法體現(xiàn)出了良好的性能,在保持跟蹤精度的前提下,所用粒子數(shù)目少,減少了算法的計(jì)算量,節(jié)省了運(yùn)算時(shí)間,極大的提高了運(yùn)算效率。本文在箱粒子和隨機(jī)集的基礎(chǔ)上,提出了一種新的多目標(biāo)跟蹤方法,箱粒子勢(shì)概率假設(shè)密度濾波方法(BP-CPHD)。該算法保持了箱粒子濾波算法優(yōu)點(diǎn),又結(jié)合了CPHD濾波的優(yōu)勢(shì)。與傳統(tǒng)的粒子CPHD算法相比,它的計(jì)算復(fù)雜度低,運(yùn)算效率高。與基于箱粒子的概率假設(shè)密度(BP-PHD)算法相比,不需要對(duì)目標(biāo)數(shù)目的分布做出符合泊松分布的假設(shè),較好的解決了濾波器對(duì)雜波和漏檢的敏感問(wèn)題。通過(guò)遞推目標(biāo)數(shù)目的勢(shì)分布,對(duì)目標(biāo)數(shù)目做出了偏差更小的估計(jì),從而提高了跟蹤效果。在機(jī)動(dòng)目標(biāo)跟蹤問(wèn)題中,結(jié)合提出的基于箱粒子的勢(shì)概率假設(shè)密度濾波(BPCPHD)算法和交互多模型算法,提出了交互多模型的箱粒子勢(shì)概率假設(shè)密度濾波(IMM-BP-CPHD),該算法繼承了箱粒子勢(shì)概率假設(shè)密度濾波算法的優(yōu)點(diǎn),同時(shí)又能對(duì)多機(jī)動(dòng)目標(biāo)進(jìn)行有效的跟蹤,通過(guò)仿真實(shí)驗(yàn),將該算法與區(qū)間量測(cè)下的交互多模型粒子勢(shì)概率假設(shè)密度算法進(jìn)行對(duì)比,體現(xiàn)了所提算法運(yùn)行速度快等優(yōu)點(diǎn)。
[Abstract]:Because of its wide application, target tracking has been paid more and more attention by experts and scholars. In the actual tracking scene, there is always more than one object of interest. With the appearance and disappearance of moving targets, the number of targets also changes in real time, and the corresponding multi-target tracking technology has made great progress. Box particle filter is a new generalized particle filter method proposed in recent years. It has the advantages of small number of particles, low computational complexity and high computational efficiency. On the basis of box particle filter, the multi-target tracking method is studied in this paper. The theoretical basis of box particle filter is introduced. Box particle filter is a generalized particle filter algorithm, which combines interval analysis, a mathematical tool, with the traditional Monte Carlo algorithm, and uses box particles instead of point particles with known maximum error. It is a method of dealing with imprecise measurement. Compared with the traditional particle filter algorithm, the box particle filter algorithm has a good performance. On the premise of keeping tracking accuracy, the number of particles used is less, the calculation amount of the algorithm is reduced, and the computation time is saved. The operation efficiency is greatly improved. On the basis of box particle and random set, a new multi-target tracking method, BP-CPHD (Particle potential probability assumption density filter), is proposed in this paper. The algorithm preserves the advantages of box particle filter and combines the advantages of CPHD filter. Compared with the traditional particle CPHD algorithm, it has low computational complexity and high computational efficiency. Compared with the probability assumption density (BP-PHD) algorithm based on box particle, it is not necessary to make the assumption that the distribution of target number accords with Poisson distribution, and the sensitivity of filter to clutter and miss detection is well solved. By recursive potential distribution of the number of targets, the deviation of the number of targets is estimated to be smaller, thus the tracking effect is improved. In the maneuvering target tracking problem, combining the (BPCPHD) algorithm based on the potential probability assumption density filter proposed by the box particle and the interactive multiple model algorithm, the paper proposes the box particle potential probability assumption density filter (IMM-BP-CPHD) based on the interactive multiple model. The algorithm not only inherits the advantages of the probability assumption density filter algorithm of box particle potential, but also can effectively track multiple maneuvering targets. The algorithm is compared with the interactive multi-model particle potential probability assumption density algorithm under interval measurement, which shows the advantages of the proposed algorithm, such as fast running speed and so on.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：TN713

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