脉冲噪声环境下的近场源多维参数估计
发布时间：2018-08-30 11:21
【摘要】：波达方向(DOA)估计是一种定位信源的技术,在多个领域中起着重要的作用。传统的DOA估计算法都是假定信源处于远场的情况下,即阵元接收到的是平面波。但当信源位于菲涅尔(Fresnel)区域时,远场假设此时将不再成立,所以传统的DOA估计算法将不再有用。目前的近场源参数估计大多是在高斯噪声背景下进行的研究,然而自然界中的一些噪声往往具有很强的脉冲性而无法用高斯分布来描述。在这种非高斯噪声环境下,原来基于高斯噪声数学模型设计的近场参数估计算法将会失效。目前,分数低阶统计量已经成为研究脉冲噪声环境下阵列信号处理算法的有力工具。本文结合传播算子(PM)和分数低阶统计量(FLOM),提出基于分数低阶相关和传播算子的MUSIC算法和基于分数低阶相关和传播算子的求根MUSIC 算法,分别简记为 PM-FLOM-MUSIC 算法和 PM-FLOM-ROOT-MUSIC算法。但基于分数低阶统计量的算法需要脉冲的先验知识,而先验知识在实际应用中一般很难得到。故提出基于传播算子和非线性压缩核函数变换相关(NCCFTC)的求根 MUSIC 算法,简记为 PM-NCCFTC-ROOT-MUSIC 算法。一般的近场源参数估计算法都是假定信号源频率已知,此时算法的应用将受到限制。故本文提出了基于分数低阶统计量和基于NCCFTC的近场频率、波达角度和距离的三维参数估计算法,简记为FLOM-ESPRIT算法和NCCFTC-ESPRIT算法。由于基于稀疏重构理论的阵列参数估计算法比传统的基于子空间的阵列参数估计算法有着更高的性能。故本文基于压缩感知稀疏重构理论,结合分数低阶统计量和NCCFTC,提出基于分数低阶统计量的矢量化稀疏重构近场源参数估计算法,简记为FLOM-VEC,和基于NCCFTC的矢量化稀疏重构近场源参数估计算法,简记为NCCFTC-VEC。仿真实验证明了算法的有效性。
[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.
【学位授予单位】：大连海事大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN911.7
本文编号：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.
【学位授予单位】：大连海事大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN911.7
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