本文選題：無(wú)源機(jī)動(dòng)弱目標(biāo)　切入點(diǎn)：粒子濾波　出處：《電子科技大學(xué)》2015年碩士論文

【摘要】：微弱信號(hào)的定位與跟蹤在軍用和民用領(lǐng)域都有著廣泛的應(yīng)用。由于傳感器截獲的目標(biāo)信號(hào)強(qiáng)度較小,難以將其從噪聲中分離,采用傳統(tǒng)目標(biāo)檢測(cè)與跟蹤方法性能較差。檢測(cè)前跟蹤(Track Before Detect,TBD)算法基于原始觀測(cè)數(shù)據(jù),在檢測(cè)之前建立跟蹤模型,在進(jìn)行一定時(shí)間的信號(hào)能量積累之后對(duì)目標(biāo)進(jìn)行檢測(cè)判決,同時(shí)輸出跟蹤結(jié)果,可有效地解決微弱信號(hào)的檢測(cè)與跟蹤問題。粒子濾波(Particle Filter,PF)算法是實(shí)現(xiàn)TBD算法的一種有效手段。本文主要從非合作方的角度,利用PF技術(shù),開展基于角度測(cè)量信息的無(wú)源傳感器機(jī)動(dòng)弱目標(biāo)檢測(cè)和跟蹤算法的研究工作,主要研究成果如下:首先,介紹了貝葉斯估計(jì)和粒子濾波的理論基礎(chǔ),提出了基于角度測(cè)量信息的無(wú)源傳感器TBD處理模型,并在該模型基礎(chǔ)上研究了基于粒子濾波檢測(cè)前跟蹤(PF-TBD)的原理和統(tǒng)一理論框架,根據(jù)該原理實(shí)現(xiàn)了基于未歸一化權(quán)值的優(yōu)效PF-TBD算法。通過(guò)仿真實(shí)驗(yàn)驗(yàn)證了基于TBD處理實(shí)現(xiàn)無(wú)源傳感器對(duì)微弱目標(biāo)定位與跟蹤的可行性,并分析了算法性能的影響因素。其次,針對(duì)機(jī)動(dòng)弱目標(biāo)的檢測(cè)和跟蹤問題,研究了多模型粒子濾波(MM-PF)算法,并將其應(yīng)用到TBD算法中。根據(jù)一定準(zhǔn)則隨機(jī)選擇各粒子的運(yùn)動(dòng)模型,通過(guò)重采樣技術(shù)對(duì)符合目標(biāo)運(yùn)動(dòng)特性的粒子進(jìn)行自適應(yīng)篩選,并將其融合得到目標(biāo)的運(yùn)動(dòng)狀態(tài),可解決無(wú)源機(jī)動(dòng)弱目標(biāo)的定位與跟蹤問題,并將UKF引入到算法中,提高了算法的性能。最后,針對(duì)MM-PF算法,研究了模型集的設(shè)計(jì)準(zhǔn)則,針對(duì)多模型PF-TBD算法處理強(qiáng)機(jī)動(dòng)性弱目標(biāo)時(shí)具有跟蹤性能較差的缺陷,提出了一種基于角速度估計(jì)的改進(jìn)方法。通過(guò)對(duì)目標(biāo)角速度進(jìn)行實(shí)時(shí)濾波估計(jì),并將其作為模型參數(shù)來(lái)設(shè)計(jì)各時(shí)刻的模型集,用相對(duì)較少的模型個(gè)數(shù)來(lái)自適應(yīng)地精確匹配目標(biāo)的實(shí)時(shí)運(yùn)動(dòng)狀態(tài),大大提高了多模型PF-TBD算法的實(shí)用性和性能。
[Abstract]:The positioning and tracking of weak signal in military and civilian fields have a wide range of applications. Since the target signal intensity of the sensor is small intercepted, it is difficult to be separated from the noise, the traditional methods of target detecting and tracking performance is poor. The track before detect (Track Before Detect TBD) algorithm based on the original observed data, establish the tracking model before the test, to detect the target in the decision after the accumulation of signal energy for a specified period of time, while the output tracking results, which can effectively solve the problem of detection and tracking of weak signals. The particle filter (Particle Filter PF) algorithm is an effective method to achieve the TBD algorithm. In this paper, mainly from the perspective of non cooperation, the use of PF technology to carry out passive sensor angle measurement based on the information of the research work for maneuvering weak target detection and tracking algorithms, the main research results are as follows: firstly, introduced the Pattra leaf Theoretical basis of Bayesian estimation and particle filter, we propose passive sensor TBD angle measurement based on the information processing model, and study the particle filter track before detection based on the basis of the model (PF-TBD) and the principle of unified theoretical framework, according to the principle of effective PF-TBD algorithm based on non normalization weights. Through the simulation experiment to verify the feasibility of TBD processing to achieve passive sensor positioning of weak targets and tracking based on, and analyzes the factors influencing the performance of the algorithm. Secondly, the problem of detection and tracking for maneuvering target, study the multiple model particle filter (MM-PF) algorithm and its application to the TBD algorithm. According to certain criteria random motion model the choice of particles, by re sampling technology to meet the target motion characteristics of particle adaptive screening, and the target state fusion, which can solve the passive The problem of positioning and tracking maneuvering target, and the UKF is introduced into the algorithm, improve the performance of the algorithm. Finally, according to the MM-PF algorithm, study the design criterion of the model set, the multiple model PF-TBD algorithm has strong maneuverability and weak target defect tracking performance, an improved method is proposed for angular velocity estimation based on the real-time filtering. To estimate the target angular velocity, and the model parameters to design each time the model set, the real-time state of motion to accurately match the target number of models with relatively small, greatly improves the practicability and performance of multi model PF-TBD algorithm.

【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】：2015
【分類號(hào)】：TN713

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本文編號(hào)：1683094