本文選題：輔助知識庫 + 知識輔助�。� 參考：《電子科技大學(xué)》2016年碩士論文

【摘要】：雷達(dá)在地/海/城市等強(qiáng)雜波背景中對目標(biāo)進(jìn)行探測時(shí),弱小目標(biāo)往往被強(qiáng)雜波掩蓋,而飛行器隱身性能和機(jī)動性能的提高進(jìn)一步增加了雷達(dá)探測的難度。與傳統(tǒng)的微弱目標(biāo)探測技術(shù)僅利用雷達(dá)回波數(shù)據(jù)不同,基于輔助知識庫的微弱目標(biāo)探測技術(shù)通過對地形、高程及氣象等先驗(yàn)環(huán)境信息進(jìn)行分析處理來構(gòu)建輔助知識庫,從而對傳統(tǒng)的檢測跟蹤算法進(jìn)行知識輔助,有效提高了傳統(tǒng)檢測跟蹤算法在復(fù)雜環(huán)境下的探測性能,成為微弱目標(biāo)探測領(lǐng)域的研究熱點(diǎn)。然而,輔助知識庫作為一種新興技術(shù),仍有大量技術(shù)問題有待解決,如輔助知識的獲取、動態(tài)更新和工程化應(yīng)用等。本文圍繞以上問題,研究了輔助知識的獲取和動態(tài)更新算法,以及雷達(dá)輔助知識庫的建模和工程化應(yīng)用方案,具體如下:1.研究了地形覆蓋信息、高程信息、雜波幅度分布模型信息、強(qiáng)弱雜波分區(qū)等多種輔助知識的獲取方法;提出了一種結(jié)構(gòu)化、參數(shù)化的雷達(dá)輔助知識庫建模方法。2.提出了基于雙AD檢驗(yàn)的動態(tài)更新算法和基于指數(shù)平滑的動態(tài)更新算法,兩種算法可對相應(yīng)的輔助知識進(jìn)行有效的動態(tài)更新,大幅提高輔助知識與探測環(huán)境的匹配程度。3.提出了基于強(qiáng)弱雜波分區(qū)的知識輔助恒虛警檢測算法,該算法利用強(qiáng)弱雜波分區(qū)知識將復(fù)雜探測環(huán)境劃分為局部的均勻探測環(huán)境,從而有效的提升了背景雜波功率水平的估計(jì)精度,提高了非均勻探測環(huán)境下雷達(dá)的目標(biāo)檢測能力及虛警點(diǎn)控制能力。4.在分析了輔助知識庫建模過程中資源需求的基礎(chǔ)上,提出了雷達(dá)輔助知識庫的工程應(yīng)用方案。5.設(shè)計(jì)并實(shí)現(xiàn)了雷達(dá)輔助知識庫軟件,能夠?qū)崿F(xiàn)輔助知識庫快速建模、動態(tài)更新、知識提取及知識輔助檢測跟蹤等功能。以上所提出的算法均通過仿真實(shí)驗(yàn)及雷達(dá)實(shí)測數(shù)據(jù)驗(yàn)證,結(jié)果證明了雷達(dá)輔助知識庫的有效性。
[Abstract]:When radar detects a target in a ground / sea / city background, the weak target is often masked by a strong clutter, and the stealth and maneuverability of the aircraft further increase the difficulty of radar detection. Unlike the traditional weak target detection technology, which only uses radar echo data, the weak target detection technology based on auxiliary knowledge base constructs the auxiliary knowledge base by analyzing and processing the prior environmental information, such as terrain, elevation and meteorology, etc. Thus, the traditional detection and tracking algorithm is supported by knowledge, which effectively improves the detection performance of the traditional detection and tracking algorithm in complex environment, and becomes a research hotspot in the field of weak target detection. However, as a new technology, there are still a lot of technical problems to be solved, such as the acquisition of auxiliary knowledge, dynamic updating and engineering application. In this paper, the acquisition and dynamic updating algorithm of auxiliary knowledge and the modeling and engineering application scheme of radar aided knowledge base are studied, which are as follows: 1. In this paper, the acquisition methods of terrain coverage information, elevation information, clutter amplitude distribution model information, strong and weak clutter partition and so on are studied, and a structured and parameterized method of radar aided knowledge base modeling is proposed. A dynamic updating algorithm based on double AD test and a dynamic updating algorithm based on exponential smoothing are proposed. The two algorithms can effectively update the corresponding auxiliary knowledge and greatly improve the matching degree between the auxiliary knowledge and the detection environment. A Knowledge-Aided CFAR detection algorithm based on strong and weak clutter partition is proposed. The algorithm divides the complex detection environment into local uniform detection environment by using the strong and weak clutter partition knowledge. Therefore, the estimation accuracy of background clutter power level is improved effectively, and the target detection ability and false alarm control ability of radar under non-uniform detection environment are improved. Based on the analysis of the resource requirements in the modeling process of the auxiliary knowledge base, the engineering application scheme of the radar aided knowledge base is proposed. The software of radar aided knowledge base is designed and implemented, which can realize the functions of fast modeling, dynamic updating, knowledge extraction and knowledge aided detection and tracking. The proposed algorithms are verified by simulation experiments and radar measured data, and the results show the effectiveness of the radar auxiliary knowledge base.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TN959;TP391.9

【參考文獻(xiàn)】

相關(guān)期刊論文 前7條

1 范西昆;曲毅;;知識輔助機(jī)載雷達(dá)雜波抑制方法研究進(jìn)展[J];電子學(xué)報(bào);2012年06期

2 李青華;姚云萍;;一種基于知識輔助的CFAR檢測器[J];雷達(dá)科學(xué)與技術(shù);2012年01期

3 鄒鯤;趙修斌;田孝華;謝洪森;;非高斯雜波中知識輔助的信號檢測算法[J];信號處理;2012年01期

4 謝洪森;鄒鯤;;基于GIS信息的CFAR檢測器[J];現(xiàn)代雷達(dá);2011年08期

5 陶勇;胡衛(wèi)東;;基于方位特性表征的屬性散射中心模型參數(shù)估計(jì)方法[J];信號處理;2010年05期

6 陶勇;胡衛(wèi)東;;基于知識的雷達(dá)目標(biāo)識別研究[J];現(xiàn)代雷達(dá);2009年01期

7 李壯志;張尤賽;;基于DEM的雷達(dá)地雜波仿真[J];計(jì)算機(jī)仿真;2007年05期

，

本文編號：2036011