为有效评估高重频对抗制导武器的效果,研究干扰频率、波门宽度、编码方式和干扰时机等因素对激光高重频干扰效果的影响,首先,通过深入分析导引头抗干扰关键技术和高重频干扰机理,建立了码型识别模型、波门设置模型和高重频干扰模型;而后...为有效评估高重频对抗制导武器的效果,研究干扰频率、波门宽度、编码方式和干扰时机等因素对激光高重频干扰效果的影响,首先,通过深入分析导引头抗干扰关键技术和高重频干扰机理,建立了码型识别模型、波门设置模型和高重频干扰模型;而后,设计了弹道仿真流程,基于过重力补偿比例制导导弹弹道仿真平台,评估了不同影响因素对高重频干扰效果的影响。仿真结果表明:干扰频率和波门宽度对高重频干扰效果的影响较大,频率越高、波门宽度越大干扰效果越好;LFSR状态码对高重频干扰的抗干扰性能比二间隔码好;且在干扰频率达到100 k Hz时,高重频对波门宽度为20μs的二变间隔码的干扰效率能达到100%,脱靶量达到510.4 m;而干扰时机对干扰效果影响较小。文中的研究成果可为高重频干扰装备研制和战术使用提供一定的参考和依据。展开更多
The problem of underdetermined blind source separation of adjacent satellite interference is proposed in this paper. Density Clustering algorithm(DC-algorithm) presented in this article is different from traditional m...The problem of underdetermined blind source separation of adjacent satellite interference is proposed in this paper. Density Clustering algorithm(DC-algorithm) presented in this article is different from traditional methods. Sparseness representation has been applied in underdetermined blind signal source separation. However, some difficulties have not been considered, such as the number of sources is unknown or the mixed matrix is ill-conditioned. In order to find out the number of the mixed signals, Short Time Fourier Transform(STFT) is employed to segment received mixtures. Then, we formulate the blind source signal as cluster problem. Furthermore, we construct Cost Function Pair and Decision Coordinate System by using density clustering. At the end of this paper, we discuss the performance of the proposed method and verify the novel method based on several simulations. We verify the proposed method on numerical experiments with real signal transmission, which demonstrates the validity of the proposed method.展开更多
Amplitude variations with offset or incident angle （AVO/AVA） inversion are typically combined with statistical methods, such as Bayesian inference or deterministic inversion. We propose a joint elastic inversion met...Amplitude variations with offset or incident angle （AVO/AVA） inversion are typically combined with statistical methods, such as Bayesian inference or deterministic inversion. We propose a joint elastic inversion method in the time and frequency domain based on Bayesian inversion theory to improve the resolution of the estimated P- and S-wave velocities and density. We initially construct the objective function using Bayesian inference by combining seismic data in the time and frequency domain. We use Cauchy and Gaussian probability distribution density functions to obtain the prior information for the model parameters and the likelihood function, respectively. We estimate the elastic parameters by solving the initial objective function with added model constraints to improve the inversion robustness. The results of the synthetic data suggest that the frequency spectra of the estimated parameters are wider than those obtained with conventional elastic inversion in the time domain. In addition, the proposed inversion approach offers stronger antinoising compared to the inversion approach in the frequency domain. Furthermore, results from synthetic examples with added Gaussian noise demonstrate the robustness of the proposed approach. From the real data, we infer that more model parameter details can be reproduced with the proposed joint elastic inversion.展开更多
文摘为有效评估高重频对抗制导武器的效果,研究干扰频率、波门宽度、编码方式和干扰时机等因素对激光高重频干扰效果的影响,首先,通过深入分析导引头抗干扰关键技术和高重频干扰机理,建立了码型识别模型、波门设置模型和高重频干扰模型;而后,设计了弹道仿真流程,基于过重力补偿比例制导导弹弹道仿真平台,评估了不同影响因素对高重频干扰效果的影响。仿真结果表明:干扰频率和波门宽度对高重频干扰效果的影响较大,频率越高、波门宽度越大干扰效果越好;LFSR状态码对高重频干扰的抗干扰性能比二间隔码好;且在干扰频率达到100 k Hz时,高重频对波门宽度为20μs的二变间隔码的干扰效率能达到100%,脱靶量达到510.4 m;而干扰时机对干扰效果影响较小。文中的研究成果可为高重频干扰装备研制和战术使用提供一定的参考和依据。
基金supported by a grant from the national High Technology Research and development Program of China (863 Program) (No.2012AA01A502)National Natural Science Foundation of China (No.61179006)Science and Technology Support Program of Sichuan Province(No.2014GZX0004)
文摘The problem of underdetermined blind source separation of adjacent satellite interference is proposed in this paper. Density Clustering algorithm(DC-algorithm) presented in this article is different from traditional methods. Sparseness representation has been applied in underdetermined blind signal source separation. However, some difficulties have not been considered, such as the number of sources is unknown or the mixed matrix is ill-conditioned. In order to find out the number of the mixed signals, Short Time Fourier Transform(STFT) is employed to segment received mixtures. Then, we formulate the blind source signal as cluster problem. Furthermore, we construct Cost Function Pair and Decision Coordinate System by using density clustering. At the end of this paper, we discuss the performance of the proposed method and verify the novel method based on several simulations. We verify the proposed method on numerical experiments with real signal transmission, which demonstrates the validity of the proposed method.
基金supported by the National Nature Science Foundation Project(Nos.41604101 and U1562215)the National Grand Project for Science and Technology(No.2016ZX05024-004)+2 种基金the Natural Science Foundation of Shandong(No.BS2014NJ005)Science Foundation from SINOPEC Key Laboratory of Geophysics(No.33550006-15-FW2099-0027)the Fundamental Research Funds for the Central Universities
文摘Amplitude variations with offset or incident angle （AVO/AVA） inversion are typically combined with statistical methods, such as Bayesian inference or deterministic inversion. We propose a joint elastic inversion method in the time and frequency domain based on Bayesian inversion theory to improve the resolution of the estimated P- and S-wave velocities and density. We initially construct the objective function using Bayesian inference by combining seismic data in the time and frequency domain. We use Cauchy and Gaussian probability distribution density functions to obtain the prior information for the model parameters and the likelihood function, respectively. We estimate the elastic parameters by solving the initial objective function with added model constraints to improve the inversion robustness. The results of the synthetic data suggest that the frequency spectra of the estimated parameters are wider than those obtained with conventional elastic inversion in the time domain. In addition, the proposed inversion approach offers stronger antinoising compared to the inversion approach in the frequency domain. Furthermore, results from synthetic examples with added Gaussian noise demonstrate the robustness of the proposed approach. From the real data, we infer that more model parameter details can be reproduced with the proposed joint elastic inversion.
