【摘要】：多輸入多輸出(MIMO)雷達(dá)作為一種新型雷達(dá)體制,已受到國(guó)內(nèi)外學(xué)者的廣泛關(guān)注。MIMO雷達(dá)由于能夠有效地克服傳統(tǒng)雷達(dá)體制存在的弊端,可以顯著地提高目標(biāo)的檢測(cè)性能,具有巨大應(yīng)用前景。目前,針對(duì)MIMO雷達(dá)目標(biāo)檢測(cè)已提出一系列方法,如Neyman-Pearson檢測(cè)、廣義似然比檢測(cè)等,它們雖不同程度上提高了檢測(cè)性能,但需要事先已知或預(yù)先估計(jì)噪聲方差、目標(biāo)散射矩陣等信息,屬于非盲檢測(cè)方法。而且,這些方法通常假定快拍數(shù)遠(yuǎn)遠(yuǎn)大于陣元數(shù),此時(shí),接收信號(hào)的樣本協(xié)方差矩陣可以作為統(tǒng)計(jì)協(xié)方差矩陣的極大似然估計(jì)。隨著MIMO雷達(dá)技術(shù)日益走向應(yīng)用,大陣列系統(tǒng)已成為一個(gè)必然發(fā)展趨勢(shì)。在大陣列系統(tǒng)中,陣元數(shù)可以與快拍數(shù)相比擬甚至大于快拍數(shù),此時(shí)樣本協(xié)方差矩陣的特征值分布區(qū)間發(fā)生改變,傳統(tǒng)的目標(biāo)檢測(cè)方法不再適用。針對(duì)上述問(wèn)題,本文以高維協(xié)方差矩陣的收縮估計(jì)技術(shù)和大維隨機(jī)矩陣?yán)碚摓楣ぞ?對(duì)MIMO雷達(dá)的目標(biāo)盲檢測(cè)方法進(jìn)行了深入研究。本文的研究工作得到國(guó)家自然科學(xué)基金“基于大維隨機(jī)矩陣?yán)碚摰腗IMO雷達(dá)穩(wěn)健目標(biāo)檢測(cè)與估計(jì)”(項(xiàng)目編號(hào):61371158)的資助。本文的創(chuàng)新性研究工作如下:針對(duì)陣元數(shù)與快拍數(shù)可以相比擬的大陣列MIMO雷達(dá)系統(tǒng),將高維協(xié)方差矩陣估計(jì)的收縮算法與大維隨機(jī)矩陣?yán)碚撓嘟Y(jié)合,提出一種基于線性收縮-標(biāo)準(zhǔn)條件數(shù)(LS-SCN)的目標(biāo)盲檢測(cè)新方法。通過(guò)求解大維系統(tǒng)樣本協(xié)方差矩陣的優(yōu)化矩陣,并利用M-P律,推導(dǎo)出檢測(cè)閾值與收縮系數(shù)之間的關(guān)系,分別給出了基于LS-SCN的單目標(biāo)和多目標(biāo)檢測(cè)算法。該算法無(wú)需已知噪聲方差、目標(biāo)散射矩陣和目標(biāo)方位等先驗(yàn)信息,對(duì)噪聲變化不敏感,且適用于大陣列系統(tǒng)。針對(duì)快拍數(shù)相對(duì)于陣元數(shù)匱乏的情況,通過(guò)分析回波信號(hào)協(xié)方差矩陣的線性收縮系數(shù)的統(tǒng)計(jì)分布特性,提出一種基于收縮系數(shù)檢測(cè)(SCD)的MIMO雷達(dá)多目標(biāo)盲檢測(cè)算法。進(jìn)而,為了降低其計(jì)算復(fù)雜度,將收縮系數(shù)進(jìn)行化簡(jiǎn),選取特征值-矩之比(EMR)作為檢測(cè)統(tǒng)計(jì)量,提出一種基于EMR的MIMO雷達(dá)多目標(biāo)盲檢測(cè)算法。仿真結(jié)果表明,兩種算法顯著地提高了MIMO雷達(dá)在快拍數(shù)匱乏環(huán)境下多目標(biāo)盲檢測(cè)的性能。傳統(tǒng)目標(biāo)檢測(cè)方法通常僅考慮理想白噪聲的情況,而實(shí)際中由于陣元間耦合等因素會(huì)產(chǎn)生相關(guān)噪聲。針對(duì)此問(wèn)題,本文建立了相關(guān)噪聲模型,提出一種相關(guān)噪聲背景下基于隨機(jī)矩陣?yán)碚摰腗IMO雷達(dá)目標(biāo)盲檢測(cè)算法。該算法利用乘性自由卷積S-變換、加性自由卷積R-變換以及Stieltjes變換等數(shù)學(xué)工具,推導(dǎo)出其樣本協(xié)方差矩陣特征值的漸近分布,結(jié)合標(biāo)準(zhǔn)條件數(shù)的檢測(cè)思想,計(jì)算出其判決閾值,從而實(shí)現(xiàn)相關(guān)噪聲背景下MIMO雷達(dá)的目標(biāo)盲檢測(cè)。
[Abstract]:As a new type of radar system, multi-input multi-output (MIMO) radar has attracted wide attention from scholars at home and abroad. MIMO radar can effectively overcome the disadvantages of traditional radar system and improve the performance of target detection. It has great application prospect. At present, a series of methods have been proposed for MIMO radar target detection, such as Neyman-Pearson detection, generalized likelihood ratio detection and so on. Although they improve the detection performance in varying degrees, they need to know or estimate the noise variance in advance. The target scattering matrix is a non-blind detection method. Moreover, these methods usually assume that the beat number is much larger than the number of matrix elements. In this case, the sample covariance matrix of the received signal can be used as the maximum likelihood estimation of the statistical covariance matrix. With the increasing application of MIMO radar technology, large array system has become an inevitable development trend. In large array systems, the number of array elements can be comparable to the number of beats or even larger than the number of beats. In this case, the distribution interval of eigenvalues of the sample covariance matrix is changed, and the traditional method of target detection is no longer applicable. In order to solve the above problems, the shrinkage estimation technique of high dimensional covariance matrix and the theory of large dimensional random matrix are used to study the blind target detection method of MIMO radar. The research work of this paper is supported by the National Natural Science Foundation of China "MIMO Radar robust Target Detection and estimation based on the large Dimension Random Matrix Theory" (item number: 61371158). The innovative research work of this paper is as follows: for large array MIMO radar systems where the number of array elements and rapid-beat numbers can be comparable, the contraction algorithm of high-dimensional covariance matrix estimation is combined with the theory of large-dimensional random matrix. A new blind target detection method based on linear contraction-standard condition number (LS-SCN) is proposed. By solving the optimization matrix of sample covariance matrix of large dimensional system and using M-P law, the relationship between detection threshold and shrinkage coefficient is derived, and the single object detection algorithm and multi-objective detection algorithm based on LS-SCN are presented respectively. The algorithm does not require prior information such as noise variance, target scattering matrix and target azimuth, so it is insensitive to noise changes and is suitable for large array systems. In view of the lack of rapid-beat number relative to the number of array elements, by analyzing the statistical distribution of linear shrinkage coefficient of echo signal covariance matrix, a blind MIMO radar multi-target detection algorithm based on shrinkage coefficient detection (SCD) is proposed. Furthermore, in order to reduce the computational complexity, the shrinkage coefficient is simplified and the ratio of eigenvalue to moment (EMR) is selected as the detection statistic. A blind detection algorithm for MIMO radar based on EMR is proposed. Simulation results show that the two algorithms can significantly improve the performance of MIMO radar blind detection in the absence of fast beat number. Traditional target detection methods usually only consider the case of ideal white noise, but in practice there will be correlation noise due to coupling between array elements. To solve this problem, a correlation noise model is established, and a blind detection algorithm for MIMO radar targets based on stochastic matrix theory is proposed. In this algorithm, the asymptotic distribution of eigenvalues of the sample covariance matrix is derived by means of multiplicative free convolution S- transform, additive free convolution R- transform and Stieltjes transform, and its decision threshold is calculated by combining the detection idea of standard condition number. In order to achieve the blind detection of MIMO radar under the background of correlated noise.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN958

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