本文選題：目標(biāo)跟蹤　切入點(diǎn)：IMM-TBD　出處：《西安電子科技大學(xué)》2014年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：雷達(dá)數(shù)據(jù)處理的主要功能是完成對(duì)目標(biāo)的跟蹤。經(jīng)典的目標(biāo)跟蹤算法以點(diǎn)目標(biāo)假設(shè)為前提,通過(guò)航跡起始、點(diǎn)航關(guān)聯(lián)及狀態(tài)濾波等操作來(lái)實(shí)現(xiàn)航跡處理,但是隨著雷達(dá)分辨率的不斷提高,目標(biāo)量測(cè)將會(huì)分布在多個(gè)分辨單元內(nèi),采用傳統(tǒng)的跟蹤算法對(duì)擴(kuò)展目標(biāo)進(jìn)行跟蹤時(shí)會(huì)面臨數(shù)據(jù)關(guān)聯(lián)復(fù)雜度高及跟蹤發(fā)散等難題,有必要研究適用于擴(kuò)展目標(biāo)的跟蹤方法。本文首先介紹了點(diǎn)目標(biāo)跟蹤基礎(chǔ)理論,包括經(jīng)典的航跡起始算法、數(shù)據(jù)互聯(lián)算法及濾波算法。其中航跡起始算法主要有邏輯法和Hough變換法;數(shù)據(jù)關(guān)聯(lián)算法主要有最近鄰域互聯(lián)算法、強(qiáng)近鄰域互聯(lián)算法、概率數(shù)據(jù)互聯(lián)算法以及聯(lián)合概率數(shù)據(jù)互聯(lián)算法;濾波算法主要有卡爾曼濾波、擴(kuò)展卡爾曼濾波、不敏卡爾曼濾波以及交互式多模型濾波算法。結(jié)合仿真實(shí)驗(yàn),對(duì)比分析了上述典型算法的性能。然后對(duì)比研究了噪聲背景下的兩種點(diǎn)目標(biāo)跟蹤算法,其中一種是線(xiàn)性非高斯系統(tǒng)中的序貫貝葉斯估計(jì)方法,該方法具有高斯和濾波算法的優(yōu)點(diǎn),并通過(guò)引入模型階數(shù)降低步驟解決了高斯和濾波算法模型階數(shù)呈指數(shù)型增長(zhǎng)的問(wèn)題。接著重點(diǎn)提出了一種基于軌跡增強(qiáng)的IMM-TBD算法,采用一組增強(qiáng)算子對(duì)目標(biāo)軌跡進(jìn)行增強(qiáng)檢測(cè),并將該算子與交互多模型算法有效結(jié)合,從而解決了低信噪比情況下高機(jī)動(dòng)目標(biāo)的跟蹤問(wèn)題。最后對(duì)比研究了兩種基于概率假設(shè)密度(PHD)的擴(kuò)展目標(biāo)跟蹤算法,其中一種算法基于隨機(jī)有限集理論,該算法將擴(kuò)展目標(biāo)的量測(cè)集合建模為隨機(jī)有限集,適用于雜波背景下非鄰近多目標(biāo)的跟蹤。另一種算法為基于隨機(jī)矩陣的PHD濾波方法,該算法將目標(biāo)的擴(kuò)展情況建模為隨機(jī)矩陣,適用于雜波背景下鄰近多目標(biāo)的跟蹤。
[Abstract]:The main function of radar data processing is performed on the target tracking. Target tracking algorithm with the classic point target assumption for the track initiation point, association and filter operations to achieve track processing, but with the improvement of the resolution of radar target, the measurement will be distributed in more than one resolution cell, by tracking the traditional algorithm of extended target tracking data association will face high complexity and tracking divergence problem, it is necessary to study the method of tracking for extended targets. This paper first introduces the basic theory of point target tracking, including track initiation algorithm, data association algorithm and filtering algorithm. The algorithm of track initiation are logical method and Hough transform method; nearest neighbor association algorithm main data association algorithm, strong neighborhood association algorithm, probabilistic data association algorithm and joint probability Data association algorithm; Calman filter is the main filter algorithm, extended Calman filter, unscented Calman filter and interacting multiple model algorithm. With the simulation results, comparative analysis of the performance of the typical algorithms. And comparative study of the two kinds of point target noise background tracking algorithm, one of which is a sequential Bayesian linear non Gauss system the estimation method, this method has the advantages of Gauss and filtering algorithm, and by introducing the model order reduction steps to solve Gauss and filtering algorithm for model order exponential growth. Then we propose a IMM-TBD algorithm based on enhanced trajectory, using a set of enhanced operator to track the target to enhance detection and, the effective combination of the operator and the interactive multi model algorithm to solve the tracking problem of low SNR and high maneuvering target. Finally, comparative study Two hypotheses based on probability density (PHD) of the extended target tracking algorithm, an algorithm based on the theory of random finite set, the algorithm will be extended target measurement set is modeled as random finite sets, suitable for clutter non adjacent targets tracking. Another algorithm for PHD filtering method of random matrix based on the algorithm will be extended to the modeling target for random matrix, suitable for clutter adjacent targets tracking.

【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TN953

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 周紹光,熊仁生,吳圣雄,汪金祥;多目標(biāo)跟蹤[J];光子學(xué)報(bào);1997年02期

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本文編號(hào)：1607944