本文選題：非線性濾波 + 目標(biāo)跟蹤�。� 參考：《電子科技大學(xué)》2017年碩士論文

【摘要】：目標(biāo)跟蹤已經(jīng)被廣泛的運(yùn)用到軍用和民用領(lǐng)域,濾波算法在各種目標(biāo)跟蹤系統(tǒng)里面都扮演著非常重要的角色。但是在大多數(shù)建立在極坐標(biāo)或球坐標(biāo)下的雷達(dá)實(shí)時(shí)跟蹤系統(tǒng)中,系統(tǒng)的量測(cè)方程是非線性的。這超出了標(biāo)準(zhǔn)卡爾曼濾波算法的處理范圍,在這種情況下,非線性濾波算法就被派上了用場(chǎng)。針對(duì)這個(gè)既實(shí)際又有必要深入研究的問(wèn)題,本文對(duì)雷達(dá)目標(biāo)跟蹤中使用的多種基于非線性量測(cè)信息的濾波算法展開(kāi)了研究。首先,本文研究了擴(kuò)展卡爾曼濾波算法、無(wú)跡卡爾曼濾波算法以及粒子濾波算法這幾種典型的非線性濾波算法。并且使用仿真實(shí)驗(yàn)從目標(biāo)跟蹤精度以及算法運(yùn)算復(fù)雜度方面來(lái)比較了這三種算法。其次,在僅利用雷達(dá)系統(tǒng)獲得的目標(biāo)位置量測(cè)信息的條件下,本文研究了基于量測(cè)轉(zhuǎn)換的卡爾曼濾波算法,包括無(wú)偏量測(cè)轉(zhuǎn)換卡爾曼濾波算法、修改的無(wú)偏量測(cè)轉(zhuǎn)換卡爾曼濾波算法以及去相關(guān)無(wú)偏量測(cè)轉(zhuǎn)換卡爾曼濾波算法。本章同樣也利用仿真實(shí)驗(yàn)從目標(biāo)跟蹤精度以及算法運(yùn)算復(fù)雜度方面來(lái)比較了這三種算法。然后考慮系統(tǒng)能夠同時(shí)獲得目標(biāo)的多普勒信息的目標(biāo)跟蹤場(chǎng)景。本章首先介紹了兩種能利用多普勒信息的目標(biāo)跟蹤算法,包括基于量測(cè)值的序貫量測(cè)轉(zhuǎn)換卡爾曼濾波算法,和近期提出的靜態(tài)融合多普勒量測(cè)轉(zhuǎn)換卡爾曼濾波算法。本文將DUCMKF更進(jìn)一步,提出了基于預(yù)測(cè)值的序貫量測(cè)轉(zhuǎn)換卡爾曼濾波算法。同樣,本章也通過(guò)利用仿真實(shí)驗(yàn)來(lái)從目標(biāo)跟蹤精度以及算法運(yùn)算復(fù)雜度方面來(lái)比較了這三種算法,表明新算法能獲得更高的目標(biāo)跟蹤精度,其復(fù)雜度僅略微增加。最后研究了基于BLUE的雷達(dá)目標(biāo)跟蹤算法。首先簡(jiǎn)要介紹了BLUE,然后通過(guò)對(duì)BLUE算法步驟的一系列等價(jià)變換,推導(dǎo)出了卡爾曼框架結(jié)構(gòu)形式下的BLUE算法。接著根據(jù)BLUE算法的卡爾曼濾波形式和卡爾曼算法的相似性,將BLUE算法和SQ-DUCMKF結(jié)合起來(lái),提出了序貫的多普勒量測(cè)BLUE算法,將BLUE算法擴(kuò)展到了能利用多普勒信息的場(chǎng)景中。仿真結(jié)果表明,BLUE和DUCMKF的性能相當(dāng),SQ-BLUE和SQ-DUCMKF的性能相當(dāng),能實(shí)現(xiàn)在位置量測(cè)信息下以及同時(shí)具有多普勒量測(cè)信息時(shí)的高精度目標(biāo)跟蹤。
[Abstract]:Target tracking has been widely used in military and civil fields. Filtering algorithm plays a very important role in various target tracking systems. However, in most radar real-time tracking systems based on polar or spherical coordinates, the measurement equations are nonlinear. This is beyond the scope of the standard Kalman filtering algorithm, in which case the nonlinear filtering algorithm is used. Aiming at this problem, which is both practical and necessary to be studied deeply, this paper studies many filtering algorithms based on nonlinear measurement information used in radar target tracking. Firstly, several typical nonlinear filtering algorithms, such as extended Kalman filter, unscented Kalman filter and particle filter, are studied. Simulation experiments are used to compare these three algorithms in terms of target tracking accuracy and computational complexity. Secondly, under the condition of only using the target position measurement information obtained by radar system, this paper studies the Kalman filtering algorithm based on measurement conversion, including unbiased measurement conversion Kalman filter algorithm. Modified unbiased measurement conversion Kalman filter algorithm and uncorrelated unbiased measurement conversion Kalman filter algorithm. In this chapter, simulation experiments are also used to compare these three algorithms in terms of target tracking accuracy and computational complexity. Then consider the target tracking scene where the system can simultaneously obtain the Doppler information of the target. In this chapter, we first introduce two kinds of target tracking algorithms which can use Doppler information, including sequential measurement conversion Kalman filtering algorithm based on measurement value, and static fusion Doppler measurement conversion Kalman filter algorithm proposed recently. In this paper, DUCMKF is further studied, and a sequential measurement conversion Kalman filter algorithm based on predictive values is proposed. In the same way, the simulation experiments are used to compare these three algorithms in terms of target tracking accuracy and computational complexity. It shows that the new algorithm can achieve higher target tracking accuracy and its complexity is only slightly increased. Finally, the radar target tracking algorithm based on blue is studied. First of all, this paper briefly introduces Blue, and then deduces the blue algorithm in the form of Kalman frame structure by a series of equivalent transformations of blue algorithm steps. Then according to the similarity between the Kalman filter form of blue algorithm and the Kalman algorithm, combining blue algorithm with SQ-DUCMKF algorithm, a sequential Doppler measurement blue algorithm is proposed, and the blue algorithm is extended to the scene where Doppler information can be used. The simulation results show that the performance of blue and DUCMKF is comparable to that of SQ-BLUE and SQ-DUCMKF, and can achieve high precision target tracking under position measurement information and with Doppler measurement information at the same time.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN953;TN713

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本文編號(hào)：2082134