本文選題：概率假設(shè)密度 + 檢測(cè)前跟蹤��； 參考：《浙江大學(xué)》2017年碩士論文

【摘要】：隨著當(dāng)代信息技術(shù)的飛速發(fā)展,無論是在軍工層面還是在民用層面,多目標(biāo)跟蹤技術(shù)都擁有著廣泛的應(yīng)用前景。在目前復(fù)雜的電磁環(huán)境下,現(xiàn)有的傳統(tǒng)跟蹤算法對(duì)“低小慢”的微弱目標(biāo)跟蹤還存在一定的局限性,研究性能更好的多目標(biāo)跟蹤算法勢(shì)在必行。本文在貝葉斯濾波的估計(jì)框架下,研究了當(dāng)前較為熱門的多目標(biāo)跟蹤算法——概率假設(shè)密度(Probability Hypothesis Density,PHD)濾波,并結(jié)合檢測(cè)前跟蹤(Track-Before-Detect,TBD)思想對(duì)微弱目標(biāo)的處理優(yōu)勢(shì),重點(diǎn)研究了基于PHD濾波的TBD算法理論及其具體實(shí)現(xiàn)。在傳統(tǒng)的PHD-TBD濾波算法中,由于沒有合理的新生粒子生成方案,以及沒有滿足使用PHD濾波的基本假設(shè),嚴(yán)重限制了算法的濾波性能和實(shí)用價(jià)值。針對(duì)這些問題,本文研究一種改進(jìn)的基于PHD濾波的檢測(cè)前多目標(biāo)跟蹤算法,提出一種基于差分定位的自適應(yīng)粒子生成方法,將新生粒子大量聚集在真實(shí)目標(biāo)的位置周圍,大大提高了新生粒子的有效性;同時(shí)建立新的觀測(cè)模型,并通過引入相關(guān)閾值對(duì)觀測(cè)數(shù)據(jù)進(jìn)行預(yù)處理使得觀測(cè)數(shù)據(jù)中的雜波數(shù)目近似服從泊松分布,令PHD濾波在TBD技術(shù)中可以更好的發(fā)揮其優(yōu)勢(shì)。仿真結(jié)果表明,所提出的算法可以提高目標(biāo)數(shù)目估計(jì)的準(zhǔn)確率,增強(qiáng)檢測(cè)與跟蹤性能,達(dá)到降低計(jì)算量的效果。在TBD算法中,每幀所要處理的數(shù)據(jù)量非常龐大,為了進(jìn)一步提升算法的實(shí)時(shí)性能,重點(diǎn)研究了算法實(shí)現(xiàn)中的重采樣過程,并提出了一種帶有緩存機(jī)制的Metropolis Hasting(Buffered Metropolis Hasting,BMH)利于并行流水線操作的重采樣方法,該方法在得到每個(gè)更新階段的粒子和其對(duì)應(yīng)權(quán)值時(shí),就可以開始處理重采樣步驟,不需要等待所有粒子的權(quán)值生成,并且能夠保證權(quán)值較大的粒子被大量保存下來,維持算法的濾波跟蹤性能。仿真結(jié)果表明,在相同的多目標(biāo)仿真條件下,相比之前的方法,BMH采樣可以獲得較好的跟蹤性能,并進(jìn)一步提升了算法的實(shí)時(shí)性能。
[Abstract]:With the rapid development of modern information technology, multi-target tracking technology has a wide application prospect both in military industry and civilian level. In the current complex electromagnetic environment, the existing traditional tracking algorithms have some limitations on the weak target tracking of "low, small and slow", so it is imperative to study the multi-target tracking algorithm with better performance. In this paper, under the framework of Bayesian filtering, a popular multi-target tracking algorithm, probabilistic Hypothesis density (PHD) filtering, is studied, and the advantage of Track-Before-DetectTBD-based approach to weak targets is presented. The theory and implementation of TBD algorithm based on PHD filter are studied in detail. In the traditional PHD-TBD filtering algorithm, the filtering performance and practical value of the algorithm are seriously limited due to the lack of a reasonable new particle generation scheme and the failure to satisfy the basic assumptions of using PHD filter. In order to solve these problems, an improved pre-detection multi-target tracking algorithm based on PHD filter is studied, and an adaptive particle generation method based on differential localization is proposed. At the same time, a new observation model is established, and the number of clutter in the observed data is approximated by Poisson distribution by introducing the correlation threshold to preprocess the observed data. So that PHD filter in TBD technology can better play its advantages. The simulation results show that the proposed algorithm can improve the accuracy of target number estimation, enhance the detection and tracking performance, and achieve the effect of reducing the amount of computation. In the TBD algorithm, the amount of data to be processed in each frame is very large. In order to further improve the real-time performance of the algorithm, the resampling process in the implementation of the algorithm is studied. A resampling method with buffer mechanism for parallel pipeline operation is proposed. The method can process the resampling step when the particle in each update stage and its corresponding weight are obtained. It does not need to wait for the weight of all particles to be generated, and can ensure that the particles with large weights are saved in large numbers, and maintain the filtering and tracking performance of the algorithm. The simulation results show that under the same multi-objective simulation conditions, the BMH sampling method can obtain better tracking performance than the previous method, and further improve the real-time performance of the algorithm.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN713

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本文編號(hào)：1971163