本文選題：隨機有限集　切入點：擴展目標(biāo)　出處：《西安電子科技大學(xué)》2014年碩士論文

【摘要】：對于擴展目標(biāo)來說,由于每個目標(biāo)在每一個采樣周期會產(chǎn)生多個量測,如若將量測和目標(biāo)相關(guān)聯(lián),勢必會存在巨大的困難,因此,研究一種更為實時有效的跟蹤方法具有極其重要的現(xiàn)實意義和應(yīng)用價值。近年來,由于計算復(fù)雜度比傳統(tǒng)方法要小而且可以有效地處理傳統(tǒng)跟蹤算法中出現(xiàn)的某些問題,基于隨機有限集的多目標(biāo)跟蹤方法受到了廣泛的認(rèn)同。本文針對基于隨機有限集的跟蹤方法展開重點研究,具體內(nèi)容如下:1.基于高斯逆威舍特概率假設(shè)密度(GIW-PHD)的擴展目標(biāo)跟蹤算法。該算法在已知量測噪聲協(xié)方差情況下,不僅考慮了目標(biāo)的運動狀態(tài),而且考慮了目標(biāo)的擴展?fàn)顟B(tài)。它將目標(biāo)的運動狀態(tài)建模為高斯分布,擴展?fàn)顟B(tài)建模為逆威舍特分布,通過量測數(shù)據(jù)來更新高斯分布以及逆威舍特分布中的參數(shù),如自由度、逆尺度矩陣等,以此來達(dá)到跟蹤目標(biāo)的位置、大小、方向等信息的目的。2.基于隨機超曲面概率假設(shè)密度(RHM-PHD)的擴展目標(biāo)跟蹤算法。該算法同GIW-PHD算法相似,在已知量測噪聲協(xié)方差情況下并且考慮了目標(biāo)的擴展?fàn)顟B(tài)。不過,RHM-PHD算法的量測建模方式與GIW-PHD算法有很大不同。RHM-PHD算法認(rèn)為量測是由在目標(biāo)表面隨機分布的量測源再加上噪聲所產(chǎn)生,GIW-PHD中的量測是由目標(biāo)的運動狀態(tài)再加上噪聲所產(chǎn)生。此外,RHM-PHD算法是將表示目標(biāo)擴展?fàn)顟B(tài)的參數(shù)嵌入到了運動狀態(tài)矢量中,通過對運動狀態(tài)矢量的更新來估計目標(biāo)的形狀、大小及方向。3.基于變分貝葉斯勢均衡多目標(biāo)多伯努利的擴展目標(biāo)跟蹤算法。該算法的優(yōu)勢在于它適用于量測噪聲協(xié)方差未知的場景并且還提出了一種新的擴展目標(biāo)的量測建模方式。核心思想在于對量測產(chǎn)生點狀態(tài)和量測噪聲協(xié)方差的聯(lián)合概率密度用變分貝葉斯近似,之后再嵌入到勢均衡多目標(biāo)多伯努利框架中,在濾波更新得到量測產(chǎn)生點狀態(tài)后,對其進(jìn)行聚類從而得到擴展目標(biāo)的估計狀態(tài)。4.基于變分貝葉斯概率假設(shè)密度(VB-PHD)的擴展目標(biāo)跟蹤算法,同VB-CBMe MBer相同的是,該算法依然適用于量測噪聲協(xié)方差未知的場景并且應(yīng)用了VB-CBMe MBer中所提到的新的量測建模方式。然而,該算法是對量測產(chǎn)生點狀態(tài)和量測噪聲協(xié)方差的聯(lián)合后驗強度用變分貝葉斯近似,得到近似分布之后,對近似分布中的參數(shù)進(jìn)行濾波更新后得到量測產(chǎn)生點狀態(tài),對其聚類從而得到擴展目標(biāo)的狀態(tài)估計。仿真結(jié)果表明,該算法與已知量測噪聲協(xié)方差,且協(xié)方差為真實值時的CBMeMBer估計結(jié)果相當(dāng)。
[Abstract]:For extended targets, since each target produces multiple measurements in each sampling period, if the measurement is associated with the target, there will be great difficulties. It is of great practical significance and practical value to study a more real-time and effective tracking method. In recent years, the computational complexity is smaller than the traditional method and it can effectively deal with some problems in the traditional tracking algorithm. The multi-target tracking method based on random finite set is widely accepted. In this paper, we focus on the tracking method based on random finite set. The specific contents are as follows: 1. An extended target tracking algorithm based on Gao Si inverse Weichet probability assumption density (GIW-PHD). The algorithm not only considers the moving state of the target, but also considers the moving state of the target when the noise covariance is known. Moreover, the extended state of the target is considered. It models the moving state of the target as Gao Si's distribution, the extended state as the inverse Weschet distribution, and the throughput data to update the Gao Si distribution and the parameters in the inverse Weschet distribution, such as degrees of freedom. The inverse scale matrix is used to track the position, size and direction of the target. 2. An extended target tracking algorithm based on the probability assumption density of random hypersurface (RHM-PHD) is proposed. The algorithm is similar to the GIW-PHD algorithm. When the measurement noise covariance is known and the extended state of the target is considered, the measurement modeling method of RHM-PHD algorithm is quite different from that of GIW-PHD algorithm. RHM-PHD algorithm thinks that measurement is recomposed by the random distribution of measurement sources on the surface of the target. The measurement in GIW-PHD caused by noise is caused by the moving state of the target plus noise. In addition, the RHM-PHD algorithm embeds the parameters representing the extended state of the target into the motion state vector. The shape of the target is estimated by updating the motion state vector, Size and direction. 3. An extended target tracking algorithm based on variational Bayesian potential equalization and multi-target multi-Bernoulli. The advantage of this algorithm is that it is suitable for measuring noise covariance unknown scenarios and proposes a new extended object. The key idea is to approximate the joint probability density of the measurement point state and the measurement noise covariance with variational Bayes. Then it is embedded into the multi-target multi-Bernoulli framework of potential equalization, and the state of the measurement generating point is obtained by the filter update. The estimated state of the extended target is obtained by clustering it. 4. The extended target tracking algorithm based on variational Bayesian probability assumption density (VB-PHD) is the same as VB-CBMe MBer. The algorithm is still applicable to the scene where the covariance of measurement noise is unknown and applies the new measurement modeling method mentioned in VB-CBMe MBer. However, This algorithm uses variational Bayes approximation for the joint posteriori strength of the measurement generating point state and the measurement noise covariance. After the approximate distribution is obtained, the parameters in the approximate distribution are filtered and updated to obtain the measurement generation point state. The simulation results show that the proposed algorithm is equivalent to the CBMeMBer estimation results when the measured noise covariance is known and the covariance is true.
【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】：2014
【分類號】：TN911.4

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