本文選題：超寬帶 + 目標(biāo)識(shí)別��； 參考：《北京郵電大學(xué)》2014年博士論文

【摘要】：超寬帶(Ultra-Wideband, UWB)無(wú)線通信技術(shù)具有穿透葉簇對(duì)隱蔽目標(biāo)進(jìn)行檢測(cè)和識(shí)別的能力,應(yīng)用潛力巨大。如何從UWB信號(hào)中提取葉簇隱蔽目標(biāo)的特征并對(duì)其進(jìn)行識(shí)別是需要研究的重要關(guān)鍵技術(shù)。本文選題來(lái)源于國(guó)家自然科學(xué)基金等項(xiàng)目,具有重要的理論意義和應(yīng)用價(jià)值。 本文針對(duì)基于UWB信號(hào)的目標(biāo)識(shí)別技術(shù)進(jìn)行了深入研究,主要完成了以下具有創(chuàng)新性的研究成果： 針對(duì)目標(biāo)特征的有效提取問(wèn)題,本文提出了基于稀疏表示的目標(biāo)特征提取算法。首先,構(gòu)造一個(gè)冗余完備字典,該字典的基函數(shù)可以從正弦函數(shù)、小波函數(shù)等變換基中選擇得到,也可以從測(cè)量目標(biāo)的UWB信號(hào)中學(xué)習(xí)得到；然后,在該冗余完備字典上求解目標(biāo)測(cè)量UWB信號(hào)的稀疏表示；最后,通過(guò)對(duì)稀疏分解系數(shù)進(jìn)行分析處理來(lái)提取目標(biāo)的稀疏特征�；谌~簇覆蓋目標(biāo)數(shù)據(jù)集的驗(yàn)證表明,所提取的基于稀疏表示的目標(biāo)特征具有很好的可分性,能夠顯著提高目標(biāo)識(shí)別的性能。 針對(duì)基于支持向量機(jī)(Support Vector Machine, SVM)的目標(biāo)識(shí)別性能受該算法參數(shù)影響較大,并且傳統(tǒng)的SVM參數(shù)選擇方法易陷入局部極值的問(wèn)題,提出了兩種改進(jìn)的粒子群優(yōu)化算法,來(lái)優(yōu)化選擇SVM參數(shù)。在改進(jìn)的粒子群優(yōu)化算法中,算法的控制參數(shù)可隨著算法的進(jìn)化自適應(yīng)調(diào)整,并且在每次迭代中分別引入混沌搜索和差分進(jìn)化搜索過(guò)程,從而兼顧全局搜索和局部搜索,以提高算法的收斂速度和搜索性能。進(jìn)一步提出了基于改進(jìn)算法優(yōu)化SVM的葉簇覆蓋目標(biāo)識(shí)別方法。實(shí)驗(yàn)結(jié)果表明,改進(jìn)的算法收斂速度快、搜索能力強(qiáng),能有效提高目標(biāo)識(shí)別性能。 針對(duì)基于SVM的目標(biāo)識(shí)別性能與核函數(shù)及其參數(shù)的選擇有很大關(guān)系,本文構(gòu)造了基于小波核函數(shù)的小波支持向量機(jī)(WSVM),并提出了一種混合量子粒子群優(yōu)化算法(HQPSO),來(lái)對(duì)WSVM的參數(shù)進(jìn)行優(yōu)化選擇。在HQPSO算法中,采用量子比特對(duì)粒子位置進(jìn)行編碼,用量子旋轉(zhuǎn)門(mén)實(shí)現(xiàn)對(duì)粒子最優(yōu)位置的搜索；并且每次迭代過(guò)程中包含一個(gè)局部增強(qiáng)搜索過(guò)程,通過(guò)量子門(mén)旋轉(zhuǎn)機(jī)制,可加速每個(gè)粒子朝著當(dāng)前代局部最優(yōu)與全局最優(yōu)解的方向進(jìn)行進(jìn)化。實(shí)驗(yàn)驗(yàn)證表明,借助于HQPSO算法出色的搜索能力,可以有效提高基于WSVM的目標(biāo)識(shí)別性能。 針對(duì)多種場(chǎng)景下多類(lèi)目標(biāo)的識(shí)別問(wèn)題,提出了一種基于稀疏表示的多場(chǎng)景下多目標(biāo)的識(shí)別方法。該方法分為兩個(gè)步驟：首先,從每類(lèi)目標(biāo)在不同場(chǎng)景下測(cè)量的UWB信號(hào)中學(xué)習(xí)出兩個(gè)冗余完備字典,一個(gè)用于目標(biāo)類(lèi)型識(shí)別,另一個(gè)用于目標(biāo)場(chǎng)景識(shí)別；然后基于這兩個(gè)冗余完備字典求解目標(biāo)測(cè)量UWB信號(hào)的稀疏表示,從中提取目標(biāo)的類(lèi)型信息和所在場(chǎng)景的信息。與基本的稀疏分類(lèi)相比,本文提出的方法能夠有效提高目標(biāo)識(shí)別性能,同時(shí)能夠顯著提高目標(biāo)識(shí)別的效率。 論文最后對(duì)全文研究工作進(jìn)行了總結(jié),并對(duì)基于UWB的目標(biāo)特征提取與識(shí)別問(wèn)題的研究進(jìn)行了展望。
[Abstract]:Ultra - wideband ( UWB ) wireless communication technology has the capability of detecting and identifying hidden targets by penetrating leaf clusters , and has great application potential . How to extract the characteristics of leaf cluster hidden targets from UWB signals and identify them is an important key technology to be studied . This thesis comes from the national natural science funds and other projects , and has important theoretical significance and application value .

In this paper , aiming at the target recognition technology based on UWB signal , this paper has completed the following innovative research results :

Firstly , a redundant complete dictionary is constructed . The base function of the dictionary can be selected from the transformation groups such as sine function , wavelet function and so on , and can also be learned from the UWB signal of the measurement target ;
then , solving the sparse representation of the target measurement UWB signal on the redundant complete dictionary ;
Finally , the sparse feature of the target is extracted by analyzing the sparse decomposition coefficient . Based on the validation of the leaf cluster coverage target data set , the extracted target feature based on sparse representation has very good scalability , which can significantly improve the performance of the target recognition .

In this paper , two improved particle swarm optimization algorithms are proposed to optimize the selection of SVM parameters . In the improved particle swarm optimization algorithm , we propose two improved particle swarm optimization algorithms to optimize the selection of SVM parameters . In the improved particle swarm optimization algorithm , we propose two improved particle swarm optimization algorithms to improve the convergence speed and search performance of the algorithm .

In this paper , a wavelet support vector machine ( WSVM ) based on wavelet kernel function is constructed , and a hybrid quantum particle swarm optimization algorithm is proposed to optimize the parameters of WSVM .
Experimental results show that the performance of target recognition based on WSVM can be effectively improved by means of the excellent searching ability of the QPSO algorithm .

Aiming at the problem of multi - object recognition under various scenes , a multi - scene recognition method based on sparse representation is proposed . The method is divided into two steps : firstly , two redundant complete dictionaries are learned from UWB signals measured under different scenes from each type of target , one is used for target type recognition , and the other is used for target scene recognition ;
Compared with the basic sparse classification , the proposed method can effectively improve the target recognition performance and improve the efficiency of the target recognition .

Finally , the thesis summarizes the work of full - text research , and looks forward to the research of target feature extraction and recognition based on UWB .
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TN925

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本文編號(hào)：2105669