【摘要】：機(jī)載氣象雷達(dá)下視檢測(cè)低空風(fēng)切變時(shí),有用的雷達(dá)信號(hào)往往被強(qiáng)雜波所掩蓋,實(shí)現(xiàn)目標(biāo)檢測(cè)的第一步是雜波抑制。相較于傳統(tǒng)雷達(dá),相控陣?yán)走_(dá)通過空時(shí)二維濾波,能夠較好地抑制地雜波,可在強(qiáng)雜波背景下獲得優(yōu)越的目標(biāo)檢測(cè)性能。傳統(tǒng)STAP(Space-time Adaptive Processing,STAP)技術(shù)一般是建立在均勻雜波環(huán)境下的,實(shí)際環(huán)境中雜波的非均勻特性導(dǎo)致了傳統(tǒng)STAP方法的性能下降。隨著高精度傳感器的發(fā)展和對(duì)待檢測(cè)目標(biāo)特性研究的不斷深入,將各種信息融入STAP技術(shù)以提高STAP處理能力的知識(shí)輔助型STAP(Knowledge Aided STAP,KA-STAP)越來越受到人們的關(guān)注。研究如何將各種輔助知識(shí)引入低空風(fēng)切變檢測(cè),以提高機(jī)載氣象雷達(dá)參數(shù)估計(jì)精度和檢測(cè)能力具有重要的實(shí)際意義。首先,論文從相控陣機(jī)載氣象雷達(dá)的接收數(shù)據(jù)模型入手,介紹了文章使用的風(fēng)場(chǎng)及其雷達(dá)回波、地雜波的仿真思路。第二,論文根據(jù)RTCA DO-120準(zhǔn)則,介紹了機(jī)載氣象雷達(dá)風(fēng)切變檢測(cè)的主要流程,對(duì)其中的關(guān)鍵性步驟——風(fēng)速估計(jì)、風(fēng)速梯度估計(jì)、F因子計(jì)算進(jìn)行了較為詳細(xì)的介紹,并對(duì)風(fēng)切變的檢測(cè)流程進(jìn)行了驗(yàn)證。第三,論文將地形數(shù)據(jù)、地表散射數(shù)據(jù)作為輔助知識(shí),引入雜波訓(xùn)練樣本挑選過程,實(shí)現(xiàn)了對(duì)非均勻環(huán)境下地雜波協(xié)方差矩陣的估計(jì);同時(shí)將風(fēng)場(chǎng)回波功率特征、風(fēng)場(chǎng)譜寬、雷達(dá)工作參數(shù)等先驗(yàn)信息引入了風(fēng)切變場(chǎng)的空時(shí)導(dǎo)向矢量建模中,建立了能夠描述風(fēng)切變場(chǎng)分布式氣象目標(biāo)特性的空時(shí)導(dǎo)向矢量。第四,論文以基于輔助知識(shí)的雜波協(xié)方差矩陣和風(fēng)切變空時(shí)導(dǎo)向矢量為基礎(chǔ),提出了一種基于多通道聯(lián)合自適應(yīng)處理(Multiple Doppler Channels Joint Adaptive Processing,M-CAP)的,能夠用于低空風(fēng)切變場(chǎng)分布式氣象目標(biāo)風(fēng)速估計(jì)的空時(shí)降維處理器結(jié)構(gòu)。該處理器通過前置的加權(quán)多普勒濾波器將全空時(shí)雜波局部化為特定多普勒通道下的定向有源干擾,然后在空域逐多普勒通道進(jìn)行自適應(yīng)處理,實(shí)現(xiàn)風(fēng)速估計(jì),在保證估計(jì)精度的同時(shí),使運(yùn)算量大大減小。最后,論文提出了一種相控陣體制下基于壓縮感知(Compressive Sensing,CS)的低空風(fēng)切變譜矩估計(jì)方法,該方法根據(jù)風(fēng)切變場(chǎng)的空時(shí)特點(diǎn)建立基于中心風(fēng)速和速度譜寬的過冗余字典;然后使用壓縮感知技術(shù)對(duì)風(fēng)場(chǎng)回波進(jìn)行重構(gòu),能夠在脈沖較少條件下,獲得較為精確的待檢測(cè)單元風(fēng)場(chǎng)風(fēng)速及速度譜寬,同時(shí)討論了利用中心速度和速度譜寬的非耦合特性的快速算法以及將譜寬作為先驗(yàn)知識(shí)進(jìn)一步降低運(yùn)算量的措施。
[Abstract]:When airborne weather radar detects wind shear at low altitude, useful radar signals are often masked by strong clutter. The first step to achieve target detection is clutter suppression. Compared with traditional radar, phased array radar can suppress ground clutter better by space-time two-dimensional filtering, and can obtain superior target detection performance in strong clutter background. The traditional STAP (Space-time Adaptive Processing,STAP) technique is generally based on the uniform clutter environment. The non-uniform characteristics of the clutter in the actual environment lead to the deterioration of the performance of the traditional STAP method. With the development of high-precision sensors and the research on the characteristics of target detection, more and more attention has been paid to integrating various kinds of information into STAP technology to improve the processing capability of STAP. It is of great practical significance to study how to introduce various auxiliary knowledge into low-altitude wind shear detection in order to improve the accuracy of airborne meteorological radar parameter estimation and detection ability. Firstly, starting with the receiving data model of phased array airborne meteorological radar, the paper introduces the wind field and the simulation idea of radar echo and ground clutter. Secondly, according to the RTCA DO-120 criterion, the paper introduces the main flow of airborne meteorological radar wind shear detection, and introduces the key steps of the process in detail: wind speed estimation, wind speed gradient estimation, F factor calculation. The flow of wind shear detection is verified. Thirdly, the terrain data and the surface scattering data are taken as the auxiliary knowledge, and the clutter training sample selection process is introduced to estimate the ground clutter covariance matrix in the non-uniform environment. At the same time, the prior information, such as wind echo power characteristics, wind field spectrum width and radar operating parameters, are introduced into the space-time guidance vector modeling of wind shear field, and the space-time guidance vector which can describe the distributed meteorological target characteristics of wind shear field is established. Fourthly, based on the clutter covariance matrix based on auxiliary knowledge and the wind shear space-time guidance vector, a multi-channel joint adaptive processing (Multiple Doppler Channels Joint Adaptive Processing,M-CAP) is proposed. Space-time dimension reduction processor architecture which can be used for wind speed estimation of distributed meteorological targets in low-altitude wind shear field. In this processor, the space-time clutter is localized into directional active interference under a specific Doppler channel through a predefined weighted Doppler filter, and then adaptive processing is carried out in the spatial domain by Doppler channel to realize wind speed estimation. At the same time, the calculation cost is greatly reduced. Finally, this paper proposes a low level wind shear spectral moment estimation method based on compressed sensing (Compressive Sensing,CS) in phased array system. According to the space-time characteristics of wind shear field, an overredundant dictionary based on center wind speed and velocity spectrum width is established. Then the compressed sensing technique is used to reconstruct the echo of the wind field, and the wind velocity and velocity spectrum width of the unit to be detected can be obtained under the condition of less pulse. At the same time, a fast algorithm using the characteristics of center velocity and velocity spectrum width is discussed, and the measures to further reduce the computation by using spectrum width as a priori knowledge are discussed.
【學(xué)位授予單位】：中國民航大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：V321.225
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本文編號(hào)：2416655