本文選題：空時自適應(yīng)處理 + 非正側(cè)視��； 參考：《南京郵電大學(xué)》2017年碩士論文

【摘要】：非正側(cè)視下機載陣列雷達(dá)的雜波分布具有嚴(yán)重的距離依賴性,不滿足獨立同分布的條件。而且,在實際應(yīng)用環(huán)境中,具有空時二維結(jié)構(gòu)的機載雷達(dá)系統(tǒng)自由度很高,滿足獨立同分布的訓(xùn)練樣本數(shù)目嚴(yán)重不足。此時,準(zhǔn)確估計待檢測距離單元的雜波協(xié)方差矩陣較為困難,傳統(tǒng)的空時自適應(yīng)處理(Space-Time Adaptive Processing,STAP)算法的雜波抑制效果嚴(yán)重下降。為解決上述問題,本文對非正側(cè)視下機載雷達(dá)的雜波抑制算法進(jìn)行研究。首先,針對非正側(cè)視下的雜波距離依賴性問題,本文在配準(zhǔn)補償算法的基礎(chǔ)上,提出了一種基于自適應(yīng)平衡加載的配準(zhǔn)補償新方法。在重構(gòu)數(shù)據(jù)和配準(zhǔn)補償?shù)倪^程中,分別進(jìn)行自適應(yīng)平衡加載,提高對重構(gòu)數(shù)據(jù)和待檢測距離單元雜波協(xié)方差矩陣估計的準(zhǔn)確性,并通過仿真驗證了算法的有效性。其次,在非正側(cè)視環(huán)境下,雜波分布具有嚴(yán)重的非均勻性,現(xiàn)有算法常常面臨著訓(xùn)練樣本不足的問題,導(dǎo)致雜波協(xié)方差矩陣的估計誤差較大。針對這個問題,本文首先提出了最遠(yuǎn)配準(zhǔn)補償算法,將最遠(yuǎn)檢測距離單元處作為參考單元進(jìn)行配準(zhǔn),有效補償了近程雜波的不平穩(wěn)性。為了進(jìn)一步解決樣本不足的問題,提出一種基于最遠(yuǎn)配準(zhǔn)補償?shù)目諘r自回歸濾波方法。仿真實驗表明,該方法在在訓(xùn)練樣本數(shù)目較少的情況下,主瓣區(qū)性能得到明顯改善,具有較好的雜波抑制性能。
[Abstract]:The clutter distribution of an airborne array radar is highly range-dependent and does not satisfy the condition of independent co-distribution. Moreover, in the practical application environment, the degree of freedom of the airborne radar system with space-time two-dimensional structure is very high, and the number of training samples satisfying the independent and same distribution is seriously insufficient. At this point, it is difficult to estimate the clutter covariance matrix of the distance unit to be detected accurately, and the traditional space-time adaptive processing algorithm of Space-Time Adaptive processing STAP (Space-Time Adaptive processing) algorithm has a serious decline in clutter suppression effect. In order to solve the above problem, the clutter suppression algorithm of airborne radar with non-positive side-looking is studied in this paper. Firstly, a new registration compensation method based on adaptive balanced loading is proposed based on the registration compensation algorithm. In the process of data reconstruction and registration compensation, adaptive balanced loading is carried out to improve the accuracy of estimation of clutter covariance matrix for reconstructed data and distance unit to be detected, and the validity of the algorithm is verified by simulation. Secondly, the clutter distribution has serious inhomogeneity in the non-positive side-looking environment. The existing algorithms often face the problem of insufficient training samples, resulting in a large estimation error of the clutter covariance matrix. In order to solve this problem, the furthest registration compensation algorithm is proposed in this paper. The furthest detection distance unit is used as the reference unit for registration, which effectively compensates the uneven stability of short-range clutter. In order to solve the problem of shortage of samples, a space-time autoregressive filtering method based on furthest registration compensation is proposed. Simulation results show that the performance of the main lobe region is improved obviously and the performance of clutter suppression is better.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN959.73

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本文編號：1949281