脈沖噪聲環(huán)境下的近場(chǎng)源多維參數(shù)估計(jì)
發(fā)布時(shí)間：2018-08-30 11:21
【摘要】：波達(dá)方向(DOA)估計(jì)是一種定位信源的技術(shù),在多個(gè)領(lǐng)域中起著重要的作用。傳統(tǒng)的DOA估計(jì)算法都是假定信源處于遠(yuǎn)場(chǎng)的情況下,即陣元接收到的是平面波。但當(dāng)信源位于菲涅爾(Fresnel)區(qū)域時(shí),遠(yuǎn)場(chǎng)假設(shè)此時(shí)將不再成立,所以傳統(tǒng)的DOA估計(jì)算法將不再有用。目前的近場(chǎng)源參數(shù)估計(jì)大多是在高斯噪聲背景下進(jìn)行的研究,然而自然界中的一些噪聲往往具有很強(qiáng)的脈沖性而無(wú)法用高斯分布來(lái)描述。在這種非高斯噪聲環(huán)境下,原來(lái)基于高斯噪聲數(shù)學(xué)模型設(shè)計(jì)的近場(chǎng)參數(shù)估計(jì)算法將會(huì)失效。目前,分?jǐn)?shù)低階統(tǒng)計(jì)量已經(jīng)成為研究脈沖噪聲環(huán)境下陣列信號(hào)處理算法的有力工具。本文結(jié)合傳播算子(PM)和分?jǐn)?shù)低階統(tǒng)計(jì)量(FLOM),提出基于分?jǐn)?shù)低階相關(guān)和傳播算子的MUSIC算法和基于分?jǐn)?shù)低階相關(guān)和傳播算子的求根MUSIC 算法,分別簡(jiǎn)記為 PM-FLOM-MUSIC 算法和 PM-FLOM-ROOT-MUSIC算法。但基于分?jǐn)?shù)低階統(tǒng)計(jì)量的算法需要脈沖的先驗(yàn)知識(shí),而先驗(yàn)知識(shí)在實(shí)際應(yīng)用中一般很難得到。故提出基于傳播算子和非線性壓縮核函數(shù)變換相關(guān)(NCCFTC)的求根 MUSIC 算法,簡(jiǎn)記為 PM-NCCFTC-ROOT-MUSIC 算法。一般的近場(chǎng)源參數(shù)估計(jì)算法都是假定信號(hào)源頻率已知,此時(shí)算法的應(yīng)用將受到限制。故本文提出了基于分?jǐn)?shù)低階統(tǒng)計(jì)量和基于NCCFTC的近場(chǎng)頻率、波達(dá)角度和距離的三維參數(shù)估計(jì)算法,簡(jiǎn)記為FLOM-ESPRIT算法和NCCFTC-ESPRIT算法。由于基于稀疏重構(gòu)理論的陣列參數(shù)估計(jì)算法比傳統(tǒng)的基于子空間的陣列參數(shù)估計(jì)算法有著更高的性能。故本文基于壓縮感知稀疏重構(gòu)理論,結(jié)合分?jǐn)?shù)低階統(tǒng)計(jì)量和NCCFTC,提出基于分?jǐn)?shù)低階統(tǒng)計(jì)量的矢量化稀疏重構(gòu)近場(chǎng)源參數(shù)估計(jì)算法,簡(jiǎn)記為FLOM-VEC,和基于NCCFTC的矢量化稀疏重構(gòu)近場(chǎng)源參數(shù)估計(jì)算法,簡(jiǎn)記為NCCFTC-VEC。仿真實(shí)驗(yàn)證明了算法的有效性。
[Abstract]:Direction of arrival (DOA) estimation is a technique for locating information sources, which plays an important role in many fields. The traditional DOA estimation algorithms assume that the source is in the far field, that is, the plane wave is received by the array element. However, when the source is located in the Fresnel (Fresnel) region, the far-field assumption will no longer hold, so the traditional DOA estimation algorithm will no longer be useful. At present, most of the near-field source parameter estimation is carried out under the background of Gao Si noise. However, some noises in nature often have strong impulsive properties and cannot be described by Gao Si distribution. In this non-Gao Si noise environment, the near-field parameter estimation algorithm, which was originally designed based on Gao Si noise mathematical model, will fail. At present, fractional low order statistics have become a powerful tool to study array signal processing algorithm in impulse noise environment. In this paper, based on the propagation operator (PM) and fractional low order statistics (FLOM), a MUSIC algorithm based on fractional low order correlation and propagation operator and a MUSIC algorithm based on fractional low order correlation and propagation operator are proposed, which are abbreviated as PM-FLOM-MUSIC algorithm and PM-FLOM-ROOT-MUSIC algorithm, respectively. But the algorithm based on fractional low order statistics requires prior knowledge of pulse, which is difficult to obtain in practice. Therefore, a root-seeking MUSIC algorithm based on propagation operator and (NCCFTC) is proposed, which is abbreviated as PM-NCCFTC-ROOT-MUSIC algorithm. In general, it is assumed that the frequency of the signal source is known, and the application of the algorithm will be limited. In this paper, an algorithm for estimating near field frequency, angle of arrival and distance based on fractional low order statistics and NCCFTC is proposed, which is abbreviated as FLOM-ESPRIT algorithm and NCCFTC-ESPRIT algorithm. Because the array parameter estimation algorithm based on sparse reconstruction theory has higher performance than the traditional subspace-based array parameter estimation algorithm. Therefore, based on the theory of compressed sensing sparse reconstruction, combined with fractional low order statistics and NCCFTC, a vectorized sparse reconstruction near field source parameter estimation algorithm based on fractional low order statistics is proposed. An algorithm for estimating the parameters of vectorized sparse reconstructed near-field source based on FLOM-VEC, and NCCFTC, which is abbreviated as NCCFTC-VEC. Simulation results show that the algorithm is effective.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN911.7
本文編號(hào)：2212909
[Abstract]:Direction of arrival (DOA) estimation is a technique for locating information sources, which plays an important role in many fields. The traditional DOA estimation algorithms assume that the source is in the far field, that is, the plane wave is received by the array element. However, when the source is located in the Fresnel (Fresnel) region, the far-field assumption will no longer hold, so the traditional DOA estimation algorithm will no longer be useful. At present, most of the near-field source parameter estimation is carried out under the background of Gao Si noise. However, some noises in nature often have strong impulsive properties and cannot be described by Gao Si distribution. In this non-Gao Si noise environment, the near-field parameter estimation algorithm, which was originally designed based on Gao Si noise mathematical model, will fail. At present, fractional low order statistics have become a powerful tool to study array signal processing algorithm in impulse noise environment. In this paper, based on the propagation operator (PM) and fractional low order statistics (FLOM), a MUSIC algorithm based on fractional low order correlation and propagation operator and a MUSIC algorithm based on fractional low order correlation and propagation operator are proposed, which are abbreviated as PM-FLOM-MUSIC algorithm and PM-FLOM-ROOT-MUSIC algorithm, respectively. But the algorithm based on fractional low order statistics requires prior knowledge of pulse, which is difficult to obtain in practice. Therefore, a root-seeking MUSIC algorithm based on propagation operator and (NCCFTC) is proposed, which is abbreviated as PM-NCCFTC-ROOT-MUSIC algorithm. In general, it is assumed that the frequency of the signal source is known, and the application of the algorithm will be limited. In this paper, an algorithm for estimating near field frequency, angle of arrival and distance based on fractional low order statistics and NCCFTC is proposed, which is abbreviated as FLOM-ESPRIT algorithm and NCCFTC-ESPRIT algorithm. Because the array parameter estimation algorithm based on sparse reconstruction theory has higher performance than the traditional subspace-based array parameter estimation algorithm. Therefore, based on the theory of compressed sensing sparse reconstruction, combined with fractional low order statistics and NCCFTC, a vectorized sparse reconstruction near field source parameter estimation algorithm based on fractional low order statistics is proposed. An algorithm for estimating the parameters of vectorized sparse reconstructed near-field source based on FLOM-VEC, and NCCFTC, which is abbreviated as NCCFTC-VEC. Simulation results show that the algorithm is effective.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN911.7
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，本文編號(hào)：2212909
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