【摘要】：多目標(biāo)跟蹤技術(shù)作為多傳感器信息融合領(lǐng)域的一個(gè)研究熱點(diǎn),在軍事與民用領(lǐng)域有著廣泛的應(yīng)用。傳統(tǒng)多目標(biāo)跟蹤方法以經(jīng)典概率論為基礎(chǔ),其核心為解決多目標(biāo)數(shù)據(jù)關(guān)聯(lián)問(wèn)題,跟蹤過(guò)程中易受目標(biāo)個(gè)數(shù)未知、雜波密集、檢測(cè)率低等復(fù)雜環(huán)境影響,導(dǎo)致數(shù)據(jù)關(guān)聯(lián)問(wèn)題復(fù)雜度增加和跟蹤精度下降。近年來(lái),基于隨機(jī)有限集(Random Finite Set,RFS)的概率假設(shè)密度(Probability Hypothesis Density,PHD)濾波方法頗受關(guān)注。該方法利用RFS理論,能夠?qū)⒛繕?biāo)狀態(tài)集合和傳感器測(cè)量集合統(tǒng)一描述于一個(gè)概率假設(shè)密度空間中,有效的避免了傳統(tǒng)跟蹤算法中的數(shù)據(jù)關(guān)聯(lián)問(wèn)題。盡管如此,目前大多數(shù)基于隨機(jī)有限集的多目標(biāo)跟蹤方法還是針對(duì)單傳感器提出的。在復(fù)雜環(huán)境下,很難做到僅依靠單個(gè)傳感器獲取的信息進(jìn)行穩(wěn)定且準(zhǔn)確的濾波估計(jì),通常需要融合多個(gè)傳感器的信息來(lái)達(dá)到跟蹤要求。為此,本文針對(duì)高雜波率和低檢測(cè)率下的多傳感器多目標(biāo)跟蹤問(wèn)題進(jìn)行研究,主要工作和研究成果如下:1)針對(duì)高雜波環(huán)境下,單傳感器應(yīng)用PHD濾波器出現(xiàn)跟蹤效果退化的問(wèn)題,通過(guò)構(gòu)建了分布式多傳感器數(shù)據(jù)融合結(jié)構(gòu)模型,提出了一種基于高斯混合PHD濾波的自適應(yīng)多傳感器數(shù)據(jù)融合算法。仿真結(jié)果表明,與單傳感器相比,所提算法有效的提高了跟蹤精度。2)針對(duì)在不同的雜波環(huán)境以及檢測(cè)率下,常規(guī)航跡融合算法具有的局限性,限制了跟蹤效果的提高。為此,構(gòu)建帶反饋的分布式多傳感器數(shù)據(jù)融合結(jié)構(gòu)模型,并提出了兩種不同的多傳感器PHD融合算法:極值融合算法和乘積融合算法。通過(guò)不同場(chǎng)景的仿真實(shí)驗(yàn),驗(yàn)證了所提算法性能優(yōu)于傳統(tǒng)算法。3)將常規(guī)多目標(biāo)跟蹤拓展到多機(jī)動(dòng)目標(biāo)跟蹤,引入交互多模型(Interacting Multiple Model Algorithm,IMM)算法,構(gòu)建了一種用于多機(jī)動(dòng)目標(biāo)跟蹤的多傳感器IMM-GMPHD濾波算法,使其能夠有效處理雜波環(huán)境中的多機(jī)動(dòng)目標(biāo)跟蹤問(wèn)題。仿真實(shí)驗(yàn)證明:當(dāng)目標(biāo)發(fā)生機(jī)動(dòng)時(shí),所提算法能得到更高的目標(biāo)狀態(tài)估計(jì)精度。
[Abstract]:As a research focus in the field of multi-sensor information fusion, multi-target tracking technology has a wide range of applications in military and civil fields. The traditional multi-target tracking method is based on the classical probability theory. The core of the traditional multi-target tracking method is to solve the problem of multi-target data association. The tracking process is easily affected by the complex environment such as unknown number of targets, dense clutters, low detection rate and so on, which leads to the increase of the complexity of the data association problem and the decrease of tracking accuracy. In recent years, probabilistic hypothetical density (Probability Hypothesis Density,PHD) filtering methods based on stochastic finite set (Random Finite Set,RFS) have attracted much attention. By using RFS theory, the target state set and the sensor measurement set can be described in a probability hypothetical density space, which effectively avoids the problem of data association in the traditional tracking algorithm. However, most of the multi-target tracking methods based on random finite sets are proposed for single sensor. In complex environment, it is difficult to rely only on the information obtained by a single sensor for stable and accurate filtering estimation. It is usually necessary to fuse the information of multiple sensors to meet the tracking requirements. In this paper, the problem of multi-sensor multi-target tracking with high hash rate and low detection rate is studied. The main work and research results are as follows: 1) aiming at the degradation of tracking effect of single-sensor application PHD filter in high clutter environment, an adaptive multi-sensor data fusion algorithm based on Gao Si hybrid PHD filter is proposed by constructing the distributed multi-sensor data fusion structure model. The simulation results show that compared with the single sensor, the proposed algorithm effectively improves the tracking accuracy. 2) aiming at the limitations of the conventional track fusion algorithm in different clutter environment and detection rate, the tracking effect is limited. In this paper, a distributed multi-sensor data fusion structure model with feedback is constructed, and two different multi-sensor PHD fusion algorithms, extreme value fusion algorithm and product fusion algorithm, are proposed. The simulation results of different scenarios show that the proposed algorithm is superior to the traditional algorithm. 3) the conventional multi-target tracking is extended to multi-maneuvering target tracking, and the interactive multi-model (Interacting Multiple Model Algorithm,IMM algorithm is introduced to construct a multi-sensor IMM-GMPHD filtering algorithm for multi-maneuvering target tracking, which can effectively deal with the multi-maneuvering target tracking problem in cluttered environment. The simulation results show that the proposed algorithm can obtain higher accuracy of target state estimation when the target maneuvers.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP212

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 蒼巖;馬瑩;喬玉龍;;高斯混合概率假設(shè)密度濾波的改進(jìn)與應(yīng)用研究[J];系統(tǒng)工程與電子技術(shù);2016年11期

2 秦嶺;黃心漢;;自適應(yīng)目標(biāo)新生強(qiáng)度的SMC-PHD/CPHD濾波[J];控制與決策;2016年08期

3 申屠晗;薛安克;駱吉安;;多步歷史估計(jì)信息反饋多模型融合方法[J];控制理論與應(yīng)用;2015年01期

4 楊峰;王永齊;梁彥;潘泉;;基于概率假設(shè)密度濾波方法的多目標(biāo)跟蹤技術(shù)綜述[J];自動(dòng)化學(xué)報(bào);2013年11期

5 徐洋;徐暉;羅少華;安瑋;;基于隨機(jī)有限集理論的多傳感器目標(biāo)聯(lián)合檢測(cè)跟蹤算法[J];國(guó)防科技大學(xué)學(xué)報(bào);2013年01期

6 王曉;韓崇昭;連峰;;基于隨機(jī)有限集的目標(biāo)跟蹤方法研究及最新進(jìn)展[J];工程數(shù)學(xué)學(xué)報(bào);2012年04期

7 楊威;付耀文;龍建乾;黎湘;;基于有限集統(tǒng)計(jì)學(xué)理論的目標(biāo)跟蹤技術(shù)研究綜述[J];電子學(xué)報(bào);2012年07期

8 呂學(xué)斌;周群彪;陳正茂;熊運(yùn)余;蔡葵;;高斯混合概率假設(shè)密度濾波器在多目標(biāo)跟蹤中的應(yīng)用[J];計(jì)算機(jī)學(xué)報(bào);2012年02期

9 劉貴喜;周承興;王澤毅;廖興海;;用于多個(gè)機(jī)動(dòng)目標(biāo)的混合高斯概率假設(shè)密度跟蹤器[J];控制理論與應(yīng)用;2011年08期

10 李偉;何鵬舉;高社生;;多傳感器加權(quán)信息融合算法研究[J];西北工業(yè)大學(xué)學(xué)報(bào);2010年05期

相關(guān)會(huì)議論文 前1條

1 張英杰;古強(qiáng);余誠(chéng)剛;王偉;李秉國(guó);;基于PHD濾波的多傳感器多目標(biāo)跟蹤融合算法[A];2015航空試驗(yàn)測(cè)試技術(shù)學(xué)術(shù)交流會(huì)論文集[C];2015年

相關(guān)博士學(xué)位論文 前3條

1 申屠晗;面向目標(biāo)跟蹤的信息反饋融合方法研究[D];浙江大學(xué);2014年

2 張鶴冰;概率假設(shè)密度濾波算法及其在多目標(biāo)跟蹤中的應(yīng)用[D];哈爾濱工程大學(xué);2012年

3 徐洋;基于隨機(jī)有限集理論的多目標(biāo)跟蹤技術(shù)研究[D];國(guó)防科學(xué)技術(shù)大學(xué);2012年

相關(guān)碩士學(xué)位論文 前3條

1 趙慧;多傳感器信息融合目標(biāo)跟蹤算法研究[D];華南理工大學(xué);2014年

2 司冠楠;面向多目標(biāo)跟蹤的多傳感器數(shù)據(jù)融合方法研究[D];沈陽(yáng)理工大學(xué);2014年

3 魯振偉;基于PHD濾波的多傳感器融合方法研究[D];哈爾濱工業(yè)大學(xué);2012年

，

本文編號(hào)：2501212