發(fā)布時(shí)間：2018-11-10 21:32

【摘要】：近年來(lái),微弱信號(hào)的檢測(cè)和跟蹤技術(shù)被廣泛的應(yīng)用在工業(yè)、交通和國(guó)防等領(lǐng)域,但隨著對(duì)檢測(cè)和跟蹤的精度要求越來(lái)越高,微弱信號(hào)分離的難題也日益突顯�；诖�,本文提出基于改進(jìn)粒子濾波的檢測(cè)前跟蹤方法,在低信噪比下實(shí)現(xiàn)對(duì)微弱目標(biāo)的精確跟蹤。首先,本論文對(duì)檢測(cè)前和檢測(cè)后兩種跟蹤方法優(yōu)勢(shì)和不足進(jìn)行論述,對(duì)無(wú)源傳感器的觀測(cè)模型和目標(biāo)運(yùn)動(dòng)模型進(jìn)行建模,并且詳細(xì)的介紹了貝葉斯估計(jì)和在其框架下的粒子濾波理論,引出粒子濾波檢測(cè)前跟蹤方法的優(yōu)越性,為后續(xù)研究提供了理論依據(jù)。其次,介紹了傳統(tǒng)的粒子濾波檢測(cè)前跟蹤算法,并對(duì)模型進(jìn)行驗(yàn)證。但是由于傳統(tǒng)算法自身的缺陷導(dǎo)致粒子分布不均及多樣性不足,介紹了幾種常用的改進(jìn)算法。并在改進(jìn)算法的基礎(chǔ)上,將進(jìn)化計(jì)算中的交叉與變異操作引入到蒙特卡羅算法中,在重采樣過(guò)程中引入Metropolis-Hastings (MH)重采樣方法,該算法在一定程度上改善了粒子多樣性匱乏的問(wèn)題并降低了算法的運(yùn)行時(shí)間。仿真結(jié)果證明改進(jìn)后的擬蒙特卡羅智能粒子濾波算法的效率和追蹤精確度都大幅度提高。最后,針對(duì)微弱目標(biāo)做勻加速和轉(zhuǎn)彎運(yùn)動(dòng)的檢測(cè)與跟蹤問(wèn)題,在改進(jìn)后的擬蒙特卡羅智能粒子濾波算法的基礎(chǔ)上提出多模型結(jié)合的建模方法,并在此基礎(chǔ)上提出基于改進(jìn)的擬蒙特卡羅智能粒子濾波交互式多模型檢測(cè)前跟蹤算法對(duì)模型進(jìn)行優(yōu)化。仿真結(jié)果表明,該改進(jìn)算法在保證跟蹤精度的前提下能在一定程度上降低粒子數(shù),能精確的對(duì)勻加速和轉(zhuǎn)彎運(yùn)動(dòng)的微弱目標(biāo)進(jìn)行跟蹤。證明了改進(jìn)后的擬蒙特卡羅智能粒子濾波算法對(duì)微弱目標(biāo)檢測(cè)和跟蹤的有效性和可靠性。
[Abstract]:In recent years, weak signal detection and tracking technology has been widely used in industry, traffic, national defense and other fields, but with the higher accuracy of detection and tracking, the difficult problem of weak signal separation has become increasingly prominent. Based on this, a pre-detection tracking method based on improved particle filter is proposed to accurately track weak targets at low signal-to-noise ratio (SNR). Firstly, this paper discusses the advantages and disadvantages of the two tracking methods before and after detection, and models the observation model and the target motion model of the passive sensor. The Bayesian estimation and particle filter theory under the framework of Bayesian estimation are introduced in detail, and the superiority of particle filter detection before tracking method is introduced, which provides a theoretical basis for further research. Secondly, the traditional particle filter detection before tracking algorithm is introduced, and the model is verified. However, due to the defects of the traditional algorithm itself, the particle distribution is uneven and the diversity is insufficient, so several commonly used improved algorithms are introduced. On the basis of the improved algorithm, the crossover and mutation operations in evolutionary computation are introduced into Monte Carlo algorithm, and the Metropolis-Hastings (MH) resampling method is introduced in the process of resampling. To some extent, the algorithm improves the lack of particle diversity and reduces the running time of the algorithm. The simulation results show that the efficiency and tracking accuracy of the improved quasi-Monte Carlo intelligent particle filter algorithm are greatly improved. Finally, aiming at the problem of detecting and tracking weak targets with uniform acceleration and turning motion, a multi-model combined modeling method is proposed based on the improved quasi-Monte Carlo intelligent particle filter algorithm. On this basis, an improved Quasi-Monte Carlo intelligent particle filter interactive multi-model detection pre-tracking algorithm is proposed to optimize the model. The simulation results show that the improved algorithm can reduce the number of particles to a certain extent and accurately track the weak targets with uniform acceleration and turn motion under the premise of ensuring the tracking accuracy. It is proved that the improved quasi-Monte Carlo intelligent particle filter algorithm is effective and reliable for weak target detection and tracking.
【學(xué)位授予單位】：山東科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP212.9;TN713

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