本文選題：UUV + 陣列自適應噪聲抵消�。� 參考：《西北工業(yè)大學》2014年博士論文

【摘要】：隨著新型無人水下航行器（Unmanned Underwater Vehicle, UUV）的發(fā)展其航速將得到進一步提高，UUV聲納陣列所接收到的自噪聲和混響級也隨之大幅增強，同時由于水聲對抗技術的不斷發(fā)展和廣泛應用，如何有效地抑制和消除自噪聲、混響以及干擾對遠程微弱目標探測的影響，提高UUV陣列對微弱信號檢測的能力是目前進行海洋資源探測開發(fā)和加強海防的現實迫切需求。 本文在對UUV聲納陣列自噪聲產生機理和特性分析的基礎上，針對陣列信號多通道處理的特點，將基于多通道差分方法與基于核函數的非線性自適應濾波器技術相結合，重點研究了基于核函數的非線性自適應濾波器理論收斂性能、非平穩(wěn)環(huán)境中基于單核函數以及基于多核函數的線性與非線性加權組合的陣列自適應噪聲抵消方法，針對多輸入多輸出（MIMO）陣列的空時兩維自適應處理對混響和干擾進行抑制的方法，并通過仿真實驗對本文所提方法的有效性進行了驗證分析。本文的主要研究成果和創(chuàng)新點如下： 1.針對動態(tài)非平穩(wěn)輸入信號會導致基于核函數的非線性自適應濾波器所構造的在線“字典”中出現和輸入信號統(tǒng)計分布不匹配的冗余失效元素問題，提出了存在冗余失配“字典”元素情況下基于單高斯核函數方法的最小均方誤差KLMS非線性濾波器誤差均值和均方收斂特性的理論計算方法，為非平穩(wěn)環(huán)境中基于單核函數的非線性自適應濾波器性能分析和設計提供了有力理論工具。仿真結果表明：基于所推理論計算方法預測出的收斂曲線與蒙特卡洛仿真實驗平均后所得的均方誤差學習曲線在瞬時動態(tài)過程和穩(wěn)態(tài)階段均一致吻合。因此不僅驗證了所推理論計算方法的正確性和有效性，而且該理論計算方法為非線性自適應噪聲抵消濾波器在非平穩(wěn)動態(tài)應用中針對“字典”提出自適應更新準則提供了理論依據。 2.針對動態(tài)非平穩(wěn)噪聲環(huán)境，在多通道差分方法提供相關參考噪聲情況下，提出了具有1-范數的FOBOS-KLMS-1和自適應-范數的FOBOS-KLMS-a兩種促在線“字典”稀疏自適應噪聲抵消方法，同時證明了在引入1-范數促稀疏操作后，所提兩種FOBOS-KLMS方法在均值意義上仍然是平穩(wěn)且嚴格收斂的。兩種FOBOS-KLMS方法通過對基于單高斯核函數的非線性自適應濾波器引入1-范數稀疏正則項后，得到以向前向后算子分裂方法定義的在線“字典”元素自適應更新策略，即對在線“字典”中對函數擬合估計貢獻權值小于給定門限的“字典”元素進行刪除操作。利用湖試噪聲數據的仿真結果表明：在聲納陣列被加速和減速的非平穩(wěn)變化過程中，與常規(guī)線性方法相比核自適應濾波方法對噪聲估計的均方誤差低了7dB，而且所提兩種“字典”自適應稀疏方法降低了陣列非線性自適應噪聲抵消器的“階數”，因此計算復雜度和對存儲空間的要求更低，為工程實際中陣列在線自適應噪聲抵消應用奠定基礎。 3.針對基于多核的方法較之單核方法具有更多的系統(tǒng)自由度和特征能夠有效解決動態(tài)系統(tǒng)在線辨識和核函數參數必須離線選擇問題的優(yōu)點，提出基于K個高斯核函數的最小均方誤差MKLMS1、MKLMS2、MKLMS3三種多核自適應濾波算法，并提出了前兩種多核濾波器在預先給定“字典”元素情況下的理論收斂分析計算方法，通過所推理論表達式可以比較三種不同類型的多核自適應濾波器的性能特點。仿真結果表明：基于所提計算方法預測出的理論收斂曲線與蒙特卡洛仿真實驗平均后所得的誤差學習曲線在瞬態(tài)階段和穩(wěn)態(tài)階段均一致吻合，，不僅驗證了所推多核自適應濾波器理論性能計算方法的正確性和有效性，而且提供了基于多核函數的非線性自適應濾波器性能分析、比較和設計手段。 4.針對陣列接收噪聲組成分量的空時復雜性，同時根據多核函數的自適應濾波器結構，提出兩種基于線性核函數與非線性高斯核函數加權組合的雙核歸一化最小均方誤差濾波BKNLMS1方法和BKNLMS2方法。針對陣列多通道差分方法提供相關噪聲的復雜性，通過對線性核函數和非線性高斯核函數分別加權得到兩種綜合自適應濾波器。不僅考慮線性相關噪聲的抵消，而且進行非線性相關噪聲的抑制，并利用湖試噪聲數據分別對單頻和調頻接收信號進行相關檢測驗證陣列自適應噪聲抵消效果。仿真結果表明：所提兩種方法可以同時自適應抵消線性和非線性噪聲分量從而改善信噪比提高檢測概率，在相同檢測概率下不僅相對傳統(tǒng)線性自適應噪聲抵消器的檢測概率提高了5dB，而且比單核KNLMS算法非線性自適應噪聲抵消方法提高了2dB，因此所提兩種方法在對聲納陣列自適應噪聲抵消的工程實際中具有很強的實用價值。 5.針對UUV舷側MIMO陣列對低速運動目標檢測時易受到混響和干擾影響的問題，提出針對MIMO陣列基于子空間估計降維的空時兩維自適應處理對混響和干擾進行抑制的方法。該方法結合扁長橢球波函數的時限帶限特性近似構造出降維的雜波子空間，并利用與發(fā)射波形正交的輔助匹配濾波通道估計出干擾加噪聲協方差矩陣，通過“逼零”方法求得MIMO陣列系統(tǒng)的空時權矢量。仿真結果表明：當存在非理想因素影響時，該方法與其它方法相比能夠更有效抑制混響和干擾且UUV舷側MIMO-STAP降維運算量更低。 本文研究成果對改善信噪比提高UUV陣列對遠程微弱信號檢測性能具有重要的理論意義和實用價值，對其它水下聲納陣列系統(tǒng)的降噪問題解決具有借鑒意義。
[Abstract]:With the new type of unmanned underwater vehicle (Unmanned Underwater, Vehicle, UUV) its speed of development will be further improved, UUV received array sonar self noise and reverberation level has been greatly enhanced, at the same time, due to the continuous development of the underwater acoustic countermeasure technology and extensive application, how to restrain and eliminate noise, reverberation and interference the influence of distance weak target detection, improve the ability of the UUV array for weak signal detection is the detection of marine resources development and strengthen the defense of urgent needs.
Based on the UUV sonar array self noise analysis of the causes and characteristics, in the array signal processing of multi channel characteristics, based on multi channel difference method and nonlinear adaptive filter technique based on kernel function combination, focusing on the kernel function of non linear adaptive filter theory convergence based on nonstationary environments based on a single kernel function and array adaptive noise cancellation method of linear and nonlinear weighted combination of multi kernel function based on multiple input multiple output (MIMO) array of two dimensional space-time adaptive processing of reverberation and interference suppression method, and verified the validity through simulation analysis methods mentioned in this paper. The main research results and innovations are as follows:
1. according to the dynamic nonstationary input signal will lead to "the construction of online nonlinear adaptive filter based on kernel function dictionary" appears and input redundant signal does not match the statistical distribution of failure elements, puts forward the existing calculation methods of single Gauss kernel function method of the minimum mean square error KLMS nonlinear filter mean error and mean square convergence properties based on the theory of "dictionary" elements under the condition of redundant, non-stationary environment in nonlinear adaptive filter performance analysis and design based on the kernel function provides a powerful theoretical tool. The simulation results show that the convergence curve and Monte Carlo simulation experiment the average calculation method of the prediction of the theory of push the MSE learning curves are consistent well in the instantaneous dynamic process and stable stage. Therefore based on not only verified the correctness of the method and the theoretical calculation The theoretical calculation method provides a theoretical basis for nonlinear adaptive noise cancellation filter in the non-stationary dynamic applications, and proposes an adaptive update rule for dictionary.
2. according to the dynamic nonstationary noise environment in multi channel differential method to provide relevant reference noise, we proposed a 1- norm and FOBOS-KLMS-1 norm of the adaptive FOBOS-KLMS-a two promote online dictionary sparse adaptive noise cancellation method is also proved in introducing 1- norm sparse Pro operation after the two the FOBOS-KLMS method is still stable and strict convergence on average. Two kinds of FOBOS-KLMS method based on the nonlinear adaptive filter of single Gauss kernel function based on the introduction of 1- norm regularization, get online to the forward backward operator splitting method definition dictionary "element adaptive updating strategy, namely the" dictionary "in the function of online the estimation of weight less than a given threshold with" dictionary "elements removed. The simulation test results using lake noise data show that: in the sonar array Column is the acceleration and deceleration of the non-stationary process, compared with the conventional linear method of kernel adaptive filtering method for noise estimation mean square error of low 7dB, and the two "dictionary" adaptive sparse method reduces array nonlinear adaptive noise canceller "order", so the computational complexity and storage the space requirement is lower, to lay the foundation for the application of array online adaptive noise cancellation in actual engineering.
3. according to the method based on multi core compared with single degree of freedom system with kernel method and more features can effectively solve the dynamic system identification and kernel function parameter selection problem of the advantages must be offline, MKLMS2 K proposed a Gauss kernel function of the minimum mean square error MKLMS1, based on MKLMS3 three multi kernel adaptive filtering algorithm, and the calculation method of the previous two kinds of theories in the given convergence of multi-core filter elements under the condition of "dictionary", the performance characteristics of the multi core expression can push the theory of adaptive filter to compare three different types. The simulation results show that the theory of error convergence curve and Monte Carlo simulation experimental average calculation method to predict the the learning curves are consistent in the transient stage and steady stage based on not only verify the push multi-core adaptive filter theory performance calculation The validity and validity of the method are also provided, and the performance analysis, comparison and design of the nonlinear adaptive filter based on the multi kernel function are provided.
4. for the array receiving noise component space-time complexity at the same time, according to the adaptive filter structure of multi kernel function, put forward two kinds of linear kernel function and kernel function weighted combination of nonlinear Gauss dual normalized least mean square error method and BKNLMS1 filtering method based on BKNLMS2. According to the complexity of multi channel array differential method provides related noise. Two kinds of comprehensive adaptive filter by weighting of linear kernel function and kernel function. The nonlinear Gauss not only consider the linear correlation of noise cancellation, and suppression of nonlinear correlation noise, and the noise data were related to the lake trial test array adaptive noise cancellation effect on single frequency and frequency modulation signal. The simulation results show that the the two methods can also offset the linear and nonlinear adaptive noise components so as to improve the SNR. In the same measuring probability, the probability of detection under not only the relative detection probability of traditional linear adaptive noise canceller improves 5dB and KNLMS than the single core algorithm of nonlinear adaptive noise cancellation method is improved by 2dB, so the two method has great practical value in the engineering practice of sonar array adaptive noise cancellation.
5. for the UUV side of MIMO array to the low speed moving target detection by reverberation and interference problems, proposed dimensionality reduction for MIMO array subspace estimation based on the two dimensional space-time adaptive processing of reverberation and interference suppression method. This method combines prolate spheroidal wave functions with characteristics similar to construct time reduction the dimension of the clutter subspace, and by using the estimated interference plus noise covariance matrix and auxiliary waveform orthogonal matching filter channel, through the "empty weight vector zero forcing method to obtain the MIMO array system. The simulation results show that when the influence of non ideal factors, compared with other methods, this method can effectively suppress the reverberation and interference and UUV side MIMO-STAP dimensionality reduction computation is lower.
The research results in this paper are of great theoretical significance and practical value for improving the signal-to-noise ratio and improving the performance of UUV array for remote weak signal detection. It is of reference for other underwater sonar array systems to solve the problem of noise reduction.

【學位授予單位】：西北工業(yè)大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：U674.941;TN911.23

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