海杂波的特性分析与目标检测处理
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摘要
海杂波的特性分析和目标检测是雷达信号处理领域的一个研究分支,如何对海杂波进行准确的建模,并从海杂波检测目标,在军用和民用领域都是一个非常重要的研究方向。本文在对IPIX雷达实测海杂波数据进行统计建模、混沌非线性动态和AR模型分析的基础上,对相关的目标检测算法进行了研究和分析。
本文研究了IPIX雷达实测海杂波数据的统计特性,分析了其统计特性参数的选择和计算,对该数据在不同极化方式和海情下的幅度分布特性进行了拟合,由于统计特性随极化方式和海情而变化,很难准确地建立海杂波的统计模型,难以使用某种单一的传统统计模型对IPIX雷达海杂波进行准确建模。随后提出将海杂波作为一种平稳的AR模型,通过估计模型的阶数和参数建立AR模型,并据此建立了一种能够准确地模拟海杂波的线性预测检测器。
根据海杂波的非线性动态特性,利用相空间重构原理对IPIX雷达实测海杂波进行非线性的相空间重构,讨论了计算嵌入维m的两种方法(伪近邻法和Cao方法)和延迟时间τ的三种方法(自相关法、互信息法、C-C方法及其改进)。此外还分析了另一个非线性相空间重构方法Volterra级数滤波器系数的确定。在相空间重构参数的选择上力求准确,以便能准确地恢复和预测海杂波序列。据此建立了基于径向基神经网络(RBFN)和Volterra级数滤波器网络(VSFN)的海杂波非线性预测检测器。
本文将建立好的基于AR模型的线性预测检测器和基于RBFN和VSFN的非线性预测检测器应用于同一组IPIX雷达实测数据数据进行海杂波的预测,与混沌特性海杂波背景下的预测结果进行了比较,给出了一种基于预测模型的目标检测方法。
针对非相参雷达中频信号中存在的微多普勒效应,本文分析了其频谱特性和时频特性。由于基于频域变换和时频分析的方法很难检测出这种微多普勒频移,提出了一种基于模糊C均值聚类算法的目标检测方法,可对非相参雷达中频信号中的目标实现时域检测,文中还讨论了噪声对于特性分析和目标检测算法的影响。基于模糊聚类的算法在非相参雷达中频信号处理中是一种新的尝试,因此本选题具有一定的实用和理论价值。
Analysis of sea clutter characteristic and target detection is one of research branch of Radar signal processing fields. How to realize accurate modeling of sea clutter and how to detect targets from sea clutter is a very important research direction both in civil and military fields. Based on statistical modeling, chaotic and nonlinear dynamic modeling and AR modeling for IPIX radar real sea clutters, some research and analysis is conducted in this paper.
The statistical characteristic of the IPIX radar sea clutter is studied in this paper. By analyzing the calculation and select of statistical parameters, the amplitude of the sea clutter were fitted with several statistical distributions. Since the statistical characteristic is varied subject to different polarization and sea state, it is difficult to accurately establish the statistical model of sea clutter. In another word, it is hard to establish an accurate model by using a single traditional statistical distribution model. Then it is proposed to treat the sea clutter as a stationary AR model and establish AR model by estimate their order and parameters. Accordingly, a linear predictor and detector which can accurately simulate the sea clutter are established.
Based on the nonlinear dynamic characteristic of the sea clutter and phase space reconstruction theory, nonlinear phase space of IPIX real sea clutter is reconstructed. For that suppose, three methods for calculating delay timeτ(Autocorrelation function method, mutual information method, C-C method and its improved algorithm) are discussed in this paper. Besides, two methods for determining embedding dimension m (false nearest neighbor and Cao method) are concerned. For another nonlinear prediction phase space reconstruction method, determining method for coefficients of the Volterra series filters is analyzed. In order to accurately recover and predict the sea clutter series, the selection on parameters of phase space reconstruction shall be as precise as possible. Similarly, two nonlinear prediction predictors and detectors are established based on AR model and Radial basis function network (RBFN) and Volterra series filter network (VSFN).
The established linear and nonlinear predictor and detector based separately on AR model and RBFN and VSFN were applied to the same IPIX radar real sea clutter data for prediction. Compared with the prediction beneath simulated chaotic characteristic sea clutter background, a target detection method based on this prediction model is presented.
Micro-Doppler effect existed in non-coherent radar Intermediate Frequency is concerned in this paper. By analyzing its spectrum characteristic and time-frequency characteristic, it can be found that it is difficult to distinguish this micro-Doppler shift only by frequency transform and time-frequency analysis method. A target detection method based on fuzzy C-mean clustering algorithm is proposed, which can realize the signal detection in time domain. Further, noise effect for the characteristic analysis and target detection algorithm is discussed in this paper. The algorithm based on fuzzy clustering is a new attempt in non-coherent radar intermediate frequency signal processing. So the theme of this article has both practical and theoretical values.
本文研究了IPIX雷达实测海杂波数据的统计特性,分析了其统计特性参数的选择和计算,对该数据在不同极化方式和海情下的幅度分布特性进行了拟合,由于统计特性随极化方式和海情而变化,很难准确地建立海杂波的统计模型,难以使用某种单一的传统统计模型对IPIX雷达海杂波进行准确建模。随后提出将海杂波作为一种平稳的AR模型,通过估计模型的阶数和参数建立AR模型,并据此建立了一种能够准确地模拟海杂波的线性预测检测器。
根据海杂波的非线性动态特性,利用相空间重构原理对IPIX雷达实测海杂波进行非线性的相空间重构,讨论了计算嵌入维m的两种方法(伪近邻法和Cao方法)和延迟时间τ的三种方法(自相关法、互信息法、C-C方法及其改进)。此外还分析了另一个非线性相空间重构方法Volterra级数滤波器系数的确定。在相空间重构参数的选择上力求准确,以便能准确地恢复和预测海杂波序列。据此建立了基于径向基神经网络(RBFN)和Volterra级数滤波器网络(VSFN)的海杂波非线性预测检测器。
本文将建立好的基于AR模型的线性预测检测器和基于RBFN和VSFN的非线性预测检测器应用于同一组IPIX雷达实测数据数据进行海杂波的预测,与混沌特性海杂波背景下的预测结果进行了比较,给出了一种基于预测模型的目标检测方法。
针对非相参雷达中频信号中存在的微多普勒效应,本文分析了其频谱特性和时频特性。由于基于频域变换和时频分析的方法很难检测出这种微多普勒频移,提出了一种基于模糊C均值聚类算法的目标检测方法,可对非相参雷达中频信号中的目标实现时域检测,文中还讨论了噪声对于特性分析和目标检测算法的影响。基于模糊聚类的算法在非相参雷达中频信号处理中是一种新的尝试,因此本选题具有一定的实用和理论价值。
Analysis of sea clutter characteristic and target detection is one of research branch of Radar signal processing fields. How to realize accurate modeling of sea clutter and how to detect targets from sea clutter is a very important research direction both in civil and military fields. Based on statistical modeling, chaotic and nonlinear dynamic modeling and AR modeling for IPIX radar real sea clutters, some research and analysis is conducted in this paper.
The statistical characteristic of the IPIX radar sea clutter is studied in this paper. By analyzing the calculation and select of statistical parameters, the amplitude of the sea clutter were fitted with several statistical distributions. Since the statistical characteristic is varied subject to different polarization and sea state, it is difficult to accurately establish the statistical model of sea clutter. In another word, it is hard to establish an accurate model by using a single traditional statistical distribution model. Then it is proposed to treat the sea clutter as a stationary AR model and establish AR model by estimate their order and parameters. Accordingly, a linear predictor and detector which can accurately simulate the sea clutter are established.
Based on the nonlinear dynamic characteristic of the sea clutter and phase space reconstruction theory, nonlinear phase space of IPIX real sea clutter is reconstructed. For that suppose, three methods for calculating delay timeτ(Autocorrelation function method, mutual information method, C-C method and its improved algorithm) are discussed in this paper. Besides, two methods for determining embedding dimension m (false nearest neighbor and Cao method) are concerned. For another nonlinear prediction phase space reconstruction method, determining method for coefficients of the Volterra series filters is analyzed. In order to accurately recover and predict the sea clutter series, the selection on parameters of phase space reconstruction shall be as precise as possible. Similarly, two nonlinear prediction predictors and detectors are established based on AR model and Radial basis function network (RBFN) and Volterra series filter network (VSFN).
The established linear and nonlinear predictor and detector based separately on AR model and RBFN and VSFN were applied to the same IPIX radar real sea clutter data for prediction. Compared with the prediction beneath simulated chaotic characteristic sea clutter background, a target detection method based on this prediction model is presented.
Micro-Doppler effect existed in non-coherent radar Intermediate Frequency is concerned in this paper. By analyzing its spectrum characteristic and time-frequency characteristic, it can be found that it is difficult to distinguish this micro-Doppler shift only by frequency transform and time-frequency analysis method. A target detection method based on fuzzy C-mean clustering algorithm is proposed, which can realize the signal detection in time domain. Further, noise effect for the characteristic analysis and target detection algorithm is discussed in this paper. The algorithm based on fuzzy clustering is a new attempt in non-coherent radar intermediate frequency signal processing. So the theme of this article has both practical and theoretical values.
引文
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