本文選題：目標(biāo)跟蹤 + PHD濾波��； 參考：《哈爾濱工業(yè)大學(xué)》2015年碩士論文

【摘要】：隨著科學(xué)研究和技術(shù)的發(fā)展,目標(biāo)跟蹤方法在軍事和民用領(lǐng)域均得到了普遍的應(yīng)用,而目標(biāo)跟蹤領(lǐng)域中近幾年的研究熱點(diǎn)之一就是概率假設(shè)密度(Probability Hypothesis Density,PHD)濾波。PHD濾波是基于有限集統(tǒng)計(jì)(Finite Set Statistics,FISST)的濾波方法。本文主要研究隨機(jī)有限集理論框架下glint噪聲環(huán)境中目標(biāo)跟蹤的PHD濾波方法。在本文研究中,第二章首先研究了glint噪聲的特征。glint噪聲是在雷達(dá)跟蹤環(huán)境下出現(xiàn)的測(cè)量噪聲,根據(jù)其顯著的非高斯分布及長(zhǎng)拖尾特性考慮采用t分布的建模方法。接著基于RFS理論,研究了PHD濾波算法,并考慮到仿真實(shí)驗(yàn)中的非線性觀測(cè)方程,采用了基于擴(kuò)展卡爾曼濾波(Extended Kalman Filter,EKF)的高斯混合PHD(GM-PHD)濾波算法。第三章通過(guò)增廣目標(biāo)狀態(tài)和噪聲參數(shù)來(lái)擴(kuò)展PHD濾波,為了得到擴(kuò)展PHD的封閉解,對(duì)噪聲參數(shù)應(yīng)用先驗(yàn)伽馬分布使得預(yù)測(cè)和更新強(qiáng)度能由高斯-伽馬項(xiàng)混合表示。因?yàn)槟繕?biāo)狀態(tài)和噪聲參數(shù)在似然函數(shù)中是耦合的,所以應(yīng)用變分貝葉斯方法得到近似分布使得更新強(qiáng)度的表達(dá)形式和預(yù)測(cè)強(qiáng)度一樣,并且生成的變分貝葉斯PHD(VB-PHD)濾波算法是遞歸的。最后一章研究了擴(kuò)展目標(biāo)的跟蹤情況。擴(kuò)展目標(biāo)跟蹤的重點(diǎn)在于測(cè)量集的分割,原則上應(yīng)該是將來(lái)源于同一個(gè)目標(biāo)的測(cè)量都分到一起,但是本文為了研究方便,采用了比較簡(jiǎn)單的馬氏距離分割法。仿真實(shí)驗(yàn)表明了所提出的VB-PHD濾波的跟蹤效果要優(yōu)于GM-PHD濾波。
[Abstract]:With the development of scientific research and technology, target tracking method has been widely used in both military and civil fields. In recent years, one of the research hotspots in the field of target tracking is the probability hypothesis density hypothesis probability density filter. PhD filter is a filtering method based on finite set Statistics set (FISST). In this paper, the PhD filtering method for target tracking in glint noise environment is studied in the framework of stochastic finite set theory. In the second chapter, we first study the feature of glint noise. Glint noise is the measurement noise in radar tracking environment. According to its significant non-Gao Si distribution and long tail characteristics, we consider a t distribution modeling method. Then, based on the RFS theory, the PhD filtering algorithm is studied, and considering the nonlinear observation equation in the simulation experiment, the Gao Si hybrid PHD GM-PHD filter algorithm based on extended Kalman filter (EKF) is adopted. In chapter 3, the extended PhD filter is extended by extending the target state and noise parameters. In order to obtain the closed solution of extended PhD, a prior gamma distribution is applied to the noise parameters so that the prediction and update intensity can be represented by the Gauss-Gamma term mixture. Because the target state and the noise parameters are coupled in the likelihood function, the variational Bayesian method is used to obtain the approximate distribution so that the expression of the updated strength is the same as the predicted intensity. And the generated variational Bayesian PHD VB-PHD filtering algorithm is recursive. In the last chapter, the tracking of extended targets is studied. The emphasis of extended target tracking is on the segmentation of measurement sets. In principle, all measurements from the same target should be divided together. However, in order to facilitate the research, a simple Markov distance segmentation method is used in this paper. Simulation results show that the proposed VB-PHD filter has better tracking performance than GM-PHD filter.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TN713

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相關(guān)期刊論文 前2條

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2 周衛(wèi)東;張鶴冰;喬相偉;;基于核密度估計(jì)高斯混合PHD濾波的多目標(biāo)跟蹤算法[J];系統(tǒng)工程與電子技術(shù);2011年09期

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本文編號(hào)：2027831