【摘要】：雷達(dá)信號(hào)分選是電子偵察系統(tǒng)中的一項(xiàng)關(guān)鍵技術(shù)，不斷優(yōu)化信號(hào)分選方法有助于提高偵察系統(tǒng)的信號(hào)處理能力。目前，，用于信號(hào)分選的一些可測(cè)特征參數(shù)主要有脈沖到達(dá)時(shí)間（TOA）、到達(dá)角（AOA）、脈寬（PW）、載頻（RF）、脈幅（PA）等，傳統(tǒng)雷達(dá)信號(hào)分選方法是對(duì)TOA進(jìn)行處理，得到脈沖重復(fù)間隔（PRI），再根據(jù)該參數(shù)的特性進(jìn)行信號(hào)分選。為了提高信號(hào)處理的速度和分選正確率，常選用AOA、PW和RF作為預(yù)分選參數(shù)，然后使用PRI進(jìn)行主分選。 本文重點(diǎn)研究密集信號(hào)環(huán)境下的雷達(dá)信號(hào)分選算法，包括對(duì)上述可用的雷達(dá)信號(hào)分選參數(shù)進(jìn)行簡要介紹，并描述如何進(jìn)行雷達(dá)信號(hào)環(huán)境的數(shù)字模擬。在仿真建模的基礎(chǔ)上，對(duì)幾種傳統(tǒng)的基于PRI的雷達(dá)信號(hào)分選方法進(jìn)行了對(duì)比分析。為適應(yīng)現(xiàn)代戰(zhàn)場(chǎng)環(huán)境的需求，許多學(xué)者在不斷地探索和研究實(shí)時(shí)高效的雷達(dá)信號(hào)分選方法，包括對(duì)傳統(tǒng)算法進(jìn)行不斷改進(jìn)，使用多參數(shù)進(jìn)行信號(hào)分選，采用盲分離技術(shù)和脈沖特征提取等。在此，本文研究基于多參數(shù)的分選方法，因此，對(duì)幾種常見的多參數(shù)信號(hào)分選算法進(jìn)行了介紹。針對(duì)這些算法在進(jìn)行分選時(shí)是處于一種被動(dòng)狀態(tài)，即算法需要通過設(shè)定一系列的原則來判定數(shù)據(jù)對(duì)象是否屬于同一類，提出了基于集對(duì)分析聚類的雷達(dá)信號(hào)分選方法，集對(duì)分析方法是直接從數(shù)據(jù)對(duì)象間的相似性出發(fā)進(jìn)行研究的，具有計(jì)算速度快的優(yōu)點(diǎn)。針對(duì)原基于集對(duì)分析聚類的雷達(dá)信號(hào)分選方法的聚類結(jié)果具有不穩(wěn)定性，本文對(duì)該方法進(jìn)行了改進(jìn)，并通過仿真實(shí)驗(yàn)進(jìn)行了驗(yàn)證，為了進(jìn)一步研究改進(jìn)算法的性能，對(duì)改進(jìn)算法在含不同噪聲比例的情況下進(jìn)行了仿真分析。 此外，在使用支持向量聚類算法進(jìn)行信號(hào)分選時(shí)，為了避免該算法的一些影響，提出了一種基于支持向量聚類改進(jìn)的雷達(dá)信號(hào)分選算法，該算法采用將支持向量聚類與上述改進(jìn)的集對(duì)分析方法相結(jié)合的方法，不僅有效避免了使用支持向量聚類算法所帶來的一些不利因素，同時(shí)也改善了改進(jìn)的集對(duì)分析方法的抗噪性能，通過實(shí)驗(yàn)對(duì)該算法進(jìn)行了驗(yàn)證。
[Abstract]:Radar signal sorting is a key technology in electronic reconnaissance system. Continuous optimization of signal sorting method is helpful to improve the signal processing ability of reconnaissance system. At present, some measurable characteristic parameters used in signal sorting are mainly pulse arrival time (TOA),) arrival angle (TOA), pulse width (AOA), (PW), carrier frequency (RF), pulse amplitude (PA), etc. The traditional method of radar signal sorting is to process TOA. The pulse repeat interval (PRI), is obtained and the signal is sorted according to the characteristic of the parameter. In order to improve the speed and accuracy of signal processing, AOA,PW and RF are often selected as presorting parameters, and then PRI is used for primary sorting. This paper focuses on the radar signal sorting algorithm under the dense signal environment, including a brief introduction of the available radar signal sorting parameters, and describes how to carry out the digital simulation of the radar signal environment. On the basis of simulation modeling, several traditional radar signal sorting methods based on PRI are compared and analyzed. In order to meet the needs of the modern battlefield environment, many scholars are constantly exploring and studying real-time and efficient radar signal sorting methods, including the continuous improvement of traditional algorithms and the use of multi-parameter signal sorting. Blind separation technique and pulse feature extraction were used. In this paper, the multi-parameter sorting method is studied, so several common multi-parameter signal sorting algorithms are introduced. Because these algorithms are in a passive state when sorting, that is, the algorithm needs to set a series of principles to determine whether the data object belongs to the same class, a method of radar signal sorting based on set pair analysis and clustering is proposed. The method of set pair analysis is studied directly from the similarity of data objects, which has the advantage of fast computing speed. In view of the instability of the clustering results of the original clustering method based on set pair clustering, this paper improves the method and verifies it through simulation experiments, in order to further study the performance of the improved algorithm. The improved algorithm is simulated and analyzed with different noise ratio. In addition, in order to avoid the influence of the support vector clustering algorithm, an improved radar signal sorting algorithm based on support vector clustering is proposed. This algorithm combines support vector clustering with the improved set pair analysis method, which not only effectively avoids some unfavorable factors brought by using support vector clustering algorithm. At the same time, the improved set pair analysis method is improved, and the algorithm is verified by experiments.
【學(xué)位授予單位】：江南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN957.51

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