機載相控陣雷達KA-STAP技術研究
發(fā)布時間：2018-10-13 12:14
【摘要】：空時自適應處理(STAP)是機載相控陣雷達進行動目標檢測的關鍵技術。在非均勻雜波環(huán)境下,缺乏足夠多的獨立同分布(IID)訓練樣本估計雜波的統(tǒng)計特性,導致自適應性能嚴重下降,這是STAP處理所面臨的最大問題。因此,發(fā)展能夠適應非均勻環(huán)境的新型STAP算法已經(jīng)成為STAP技術的研究方向之一。將檢測環(huán)境的先驗知識合理地運用在雷達信號處理中,實現(xiàn)知識輔助的空時自適應處理(KA-STAP)目前成為解決雜波非均勻問題和改善STAP性能的研究熱點。此外,運算量巨大是STAP面臨的另一問題。為此,本論文的研究將圍繞KA-STAP技術及其數(shù)值域求解算法展開,主要包括以下幾個方面:1.研究了探測環(huán)境的先驗雜波協(xié)方差估計問題。利用真實場景的地形地貌、數(shù)字高程模型(DEM)數(shù)據(jù)、雷達系統(tǒng)參數(shù)、載機平臺運動參數(shù)等先驗信息,準確估計待檢測單元的先驗雜波模型。2.提出了一種知識輔助的自適應功率剩余(KA-APR)非均勻樣本檢測算法。該算法間接利用雜波先驗知識對STAP訓練樣本進行非均勻檢測,比常規(guī)檢測方法具有更好的檢測性能。根據(jù)提出的KA-APR檢測算法判斷待測采樣數(shù)據(jù)的類型(均勻或非均勻),研究了不同類型檢測樣本的自適應算法智能選擇問題。3.研究了KA-STAP色加載技術及其數(shù)值域求解算法。色加載算法利用雜波先驗知識對采樣雜波進行預白化濾波,降低雜波子空間的維數(shù),減少后續(xù)STAP對IID樣本數(shù)的需求。詳細推導了一種有效的最優(yōu)加載因子求解算法,使得色加載方法達到最優(yōu)性能。深入研究了基于QR分解和逆QR分解的色加載數(shù)值域求解算法,該算法避免了雜波樣本協(xié)方差矩陣直接求逆(SMI)問題,具有更好的數(shù)值穩(wěn)定性,便于通過高度并行的流水運算結構快速遞推實現(xiàn),滿足機載雷達STAP對大數(shù)據(jù)吞吐量實時處理的需求。4.構建了一個機載相控陣雷達KA-STAP仿真系統(tǒng)。分析了系統(tǒng)的設計方法和實現(xiàn)結構,便于KA-STAP技術的工程應用。
[Abstract]:Space-time adaptive processing (STAP) is a key technique for moving target detection in airborne phased array radar. In the non-uniform clutter environment, the lack of enough independent co-distributed (IID) training samples to estimate the statistical characteristics of clutter leads to a serious deterioration of adaptive performance, which is the biggest problem faced by STAP processing. Therefore, the development of new STAP algorithm which can adapt to non-uniform environment has become one of the research directions of STAP technology. The prior knowledge of detection environment is applied to radar signal processing reasonably, and the space-time adaptive processing (KA-STAP), which is aided by knowledge, has become the research focus in solving the clutter nonuniformity problem and improving the performance of STAP. In addition, the huge amount of computing is another problem facing STAP. Therefore, this paper will focus on the KA-STAP technology and its numerical range algorithm, including the following aspects: 1. The problem of prior clutter covariance estimation for detecting environment is studied. Based on the prior information such as terrain and geomorphology of real scene, (DEM) data of digital elevation model, radar system parameters and platform motion parameters, the prior clutter model of the unit to be detected is estimated accurately. 2. A knowledge aided adaptive power residue (KA-APR) nonuniform sample detection algorithm is proposed. The algorithm indirectly utilizes the prior knowledge of clutter to detect non-uniform STAP training samples, which has better detection performance than conventional detection methods. According to the proposed KA-APR detection algorithm to judge the type of sample data (uniform or non-uniform), the intelligent selection problem of adaptive algorithm for different types of samples is studied. The KA-STAP color loading technique and its numerical range solving algorithm are studied. The color loading algorithm uses the prior knowledge of clutter to prewhiten the sampled clutter to reduce the dimension of clutter subspace and to reduce the demand of subsequent STAP for IID sample number. An effective algorithm for solving the optimal loading factor is derived in detail, which makes the color loading method achieve the optimal performance. Based on QR decomposition and inverse QR decomposition, the algorithm of solving chromatic loading numerical range is studied in depth. The algorithm avoids the clutter sample covariance matrix to solve the inverse (SMI) problem directly, and has better numerical stability. It is easy to realize by high parallel pipelining operation structure quickly and recursively, which meets the need of real-time processing of big data throughput by airborne radar STAP. 4. An airborne phased array radar KA-STAP simulation system is constructed. The design method and implementation structure of the system are analyzed, which is convenient for the engineering application of KA-STAP technology.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN958.92
本文編號：2268583
[Abstract]:Space-time adaptive processing (STAP) is a key technique for moving target detection in airborne phased array radar. In the non-uniform clutter environment, the lack of enough independent co-distributed (IID) training samples to estimate the statistical characteristics of clutter leads to a serious deterioration of adaptive performance, which is the biggest problem faced by STAP processing. Therefore, the development of new STAP algorithm which can adapt to non-uniform environment has become one of the research directions of STAP technology. The prior knowledge of detection environment is applied to radar signal processing reasonably, and the space-time adaptive processing (KA-STAP), which is aided by knowledge, has become the research focus in solving the clutter nonuniformity problem and improving the performance of STAP. In addition, the huge amount of computing is another problem facing STAP. Therefore, this paper will focus on the KA-STAP technology and its numerical range algorithm, including the following aspects: 1. The problem of prior clutter covariance estimation for detecting environment is studied. Based on the prior information such as terrain and geomorphology of real scene, (DEM) data of digital elevation model, radar system parameters and platform motion parameters, the prior clutter model of the unit to be detected is estimated accurately. 2. A knowledge aided adaptive power residue (KA-APR) nonuniform sample detection algorithm is proposed. The algorithm indirectly utilizes the prior knowledge of clutter to detect non-uniform STAP training samples, which has better detection performance than conventional detection methods. According to the proposed KA-APR detection algorithm to judge the type of sample data (uniform or non-uniform), the intelligent selection problem of adaptive algorithm for different types of samples is studied. The KA-STAP color loading technique and its numerical range solving algorithm are studied. The color loading algorithm uses the prior knowledge of clutter to prewhiten the sampled clutter to reduce the dimension of clutter subspace and to reduce the demand of subsequent STAP for IID sample number. An effective algorithm for solving the optimal loading factor is derived in detail, which makes the color loading method achieve the optimal performance. Based on QR decomposition and inverse QR decomposition, the algorithm of solving chromatic loading numerical range is studied in depth. The algorithm avoids the clutter sample covariance matrix to solve the inverse (SMI) problem directly, and has better numerical stability. It is easy to realize by high parallel pipelining operation structure quickly and recursively, which meets the need of real-time processing of big data throughput by airborne radar STAP. 4. An airborne phased array radar KA-STAP simulation system is constructed. The design method and implementation structure of the system are analyzed, which is convenient for the engineering application of KA-STAP technology.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN958.92
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