本文選題：連續(xù)波雷達(dá) + 目標(biāo)檢測　； 參考：《西安電子科技大學(xué)》2014年碩士論文

【摘要】：連續(xù)波體制雷達(dá)由于很好地解決了脈沖體制的距離盲區(qū)問題,并具有良好的反隱身、抗背景雜波和抗干擾能力,已廣泛應(yīng)用于地面監(jiān)視雷達(dá)。而在地面目標(biāo)監(jiān)視中,行人和車輛等慢速目標(biāo)通常是監(jiān)視的重要對象,它們的運(yùn)動和趨勢關(guān)系著態(tài)勢的走向,相關(guān)檢測技術(shù)已經(jīng)得到研究人員的廣泛關(guān)注。同時,隨著現(xiàn)代科技快速發(fā)展和各國軍事戰(zhàn)略的技術(shù)要求,雷達(dá)的功能不僅僅局限于探測和測距方面。目標(biāo)的分類能給態(tài)勢分析帶來更多的信息,已經(jīng)成為雷達(dá),特別是地面監(jiān)視雷達(dá)另一個重要的實際應(yīng)用方向。因此,連續(xù)波雷達(dá)慢速運(yùn)動目標(biāo)檢測與分類方法是目前雷達(dá)信號處理的研究熱點(diǎn)。本文結(jié)合實際應(yīng)用需求,對連續(xù)波體制雷達(dá)的目標(biāo)檢測與分類進(jìn)行了系統(tǒng)的研究,所取得的研究成果為:(1)對線性調(diào)頻連續(xù)波雷達(dá)的工作原理和信號預(yù)處理方法進(jìn)行了研究。推導(dǎo)了多周期的信號模型以及差拍信號的頻域響應(yīng)特性,為目標(biāo)的檢測分析奠定了基礎(chǔ)。根據(jù)目標(biāo)的特性和需求的區(qū)別,詳細(xì)論述了動目標(biāo)顯示、CLEAN和雜波圖等雜波抑制方法,實現(xiàn)了雜波和目標(biāo)的區(qū)分,為后續(xù)的慢速目標(biāo)檢測奠定了基礎(chǔ)。(2)深入研究了目標(biāo)檢測過程的距離速度耦合問題及其解決方法。針對鋸齒波調(diào)頻,利用線性調(diào)頻信號的參數(shù)估計方法和模糊速度估計的方法,實現(xiàn)了目標(biāo)距離和速度的準(zhǔn)確反演;針對三角波調(diào)頻,利用目標(biāo)頻譜對稱性,在傳統(tǒng)的頻譜配對方法的基礎(chǔ)上,介紹了基于動目標(biāo)檢測(Moving Target Detction,簡稱MTD)的頻域配對和提出了時頻配對方法,從而實現(xiàn)了運(yùn)動目標(biāo)的準(zhǔn)確配對,進(jìn)而精確地獲得了目標(biāo)的真實距離和速度。(3)深入研究了人與車輛等慢速目標(biāo)的微多普勒譜特征。建立了行人和車輛等慢速目標(biāo)的數(shù)學(xué)模型,并利用實測數(shù)據(jù)驗證了建模的有效性。然后,結(jié)合仿真和實測數(shù)據(jù),分析了行人和車輛等慢速目標(biāo)微多普勒特性的差異。(4)針對目標(biāo)的微多普勒特征的差異性,提出了一種人與車輛連續(xù)波雷達(dá)分類新方法。該方法以目標(biāo)時頻譜圖的灰度共生矩陣的能量、熵值、對比度和自相關(guān)的均值和方差等統(tǒng)計量為特征,利用主分量分析進(jìn)行特征降維。在得到的本征特征量中選取部分樣本作為分類器的訓(xùn)練樣本,輸入支持向量機(jī)分類器進(jìn)行分類。仿真和實測數(shù)據(jù)處理結(jié)果表明該方法可以穩(wěn)健地實現(xiàn)人、車譜圖數(shù)據(jù)的監(jiān)督分類。
[Abstract]:Continuous wave radar (CWR) has been widely used in ground surveillance radar because of its good anti-stealth, anti-background clutter and anti-jamming ability, because it can solve the problem of blind area of pulse system. In ground target surveillance, slow targets such as pedestrians and vehicles are usually the important objects of surveillance, their movement and trend are related to the trend of the situation, the related detection technology has been widely concerned by researchers. At the same time, with the rapid development of modern science and technology and the technical requirements of the military strategy of various countries, the functions of radar are not limited to detection and ranging. Target classification can bring more information to situation analysis and has become another important practical application direction of radar, especially ground surveillance radar. Therefore, the detection and classification of slow-moving targets in continuous wave radar is a hot topic in radar signal processing. In this paper, the target detection and classification of continuous-wave radar is systematically studied according to the practical application requirements. The research result is: 1) the principle and signal preprocessing method of LFM CWR are studied. The multi-period signal model and the response characteristics of beat signal in frequency domain are derived, which lays a foundation for target detection and analysis. According to the difference of target characteristics and requirements, the clutter suppression methods such as moving target display clear and clutter graph are discussed in detail, and the distinction between clutter and target is realized. It lays a foundation for the following slow target detection. (2) the distance and velocity coupling problem in the process of target detection and its solution are studied in depth. For zigzag wave frequency modulation, the parameter estimation method of linear frequency modulation signal and the method of fuzzy velocity estimation are used to realize the accurate inversion of target distance and velocity, and for triangular wave frequency modulation, the symmetry of target frequency spectrum is used. Based on the traditional spectrum pairing method, this paper introduces the frequency domain pairing based on moving target detection (MTD) and proposes a time-frequency pairing method to realize the accurate pairing of moving targets. Finally, the real distance and velocity of the target are obtained. (3) the characteristics of micro-Doppler spectrum of the slow target, such as human and vehicle, are studied in depth. The mathematical model of slow target such as pedestrian and vehicle is established, and the validity of the model is verified by the measured data. Then, based on the simulated and measured data, the difference of micro-Doppler characteristics between pedestrian and vehicle is analyzed. A new method of continuous wave radar classification is proposed for the difference of micro-Doppler characteristics. This method is characterized by the energy, entropy, contrast and autocorrelation mean and variance statistics of the gray level co-occurrence matrix of the target spectrum. The principal component analysis is used to reduce the dimension of the feature. Some samples are selected as the training samples of the classifier and the support vector machine classifier is input to classify the eigenvalues. The simulation and measured data processing results show that the proposed method can be used to monitor and classify the data of human and vehicle spectra.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN957.51

【共引文獻(xiàn)】

相關(guān)期刊論文 前2條

1 司偉建;蔣鵬;劉旭波;;改進(jìn)的三次相位函數(shù)法LFM雷達(dá)信號參數(shù)估計[J];哈爾濱工程大學(xué)學(xué)報;2012年06期

2 唐堯;王偉;張艷;;LFMCW雷達(dá)MTD處理的分析與研究[J];火力與指揮控制;2014年11期

相關(guān)碩士學(xué)位論文 前1條

1 張聲杰;分布式SAR動目標(biāo)參數(shù)估計技術(shù)研究[D];哈爾濱工業(yè)大學(xué);2011年

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本文編號：2015524