A measurement system for the scattering characteristics of warhead fragments based on high-speed imaging systems offers advantages such as simple deployment,flexible maneuverability,and high spatiotemporal resolution,...A measurement system for the scattering characteristics of warhead fragments based on high-speed imaging systems offers advantages such as simple deployment,flexible maneuverability,and high spatiotemporal resolution,enabling the acquisition of full-process data of the fragment scattering process.However,mismatches between camera frame rates and target velocities can lead to long motion blur tails of high-speed fragment targets,resulting in low signal-to-noise ratios and rendering conventional detection algorithms ineffective in dynamic strong interference testing environments.In this study,we propose a detection framework centered on dynamic strong interference disturbance signal separation and suppression.We introduce a mixture Gaussian model constrained under a joint spatialtemporal-transform domain Dirichlet process,combined with total variation regularization to achieve disturbance signal suppression.Experimental results demonstrate that the proposed disturbance suppression method can be integrated with certain conventional motion target detection tasks,enabling adaptation to real-world data to a certain extent.Moreover,we provide a specific implementation of this process,which achieves a detection rate close to 100%with an approximate 0%false alarm rate in multiple sets of real target field test data.This research effectively advances the development of the field of damage parameter testing.展开更多
针对测距仪(distance measure equipment,DME)信号严重干扰L频段数字航空通信系统(L-band digital aviation communication system,L-DACS)前向链路接收机的问题,提出基于相关稀疏变分贝叶斯(correlated sparse variational Bayesian,cS...针对测距仪(distance measure equipment,DME)信号严重干扰L频段数字航空通信系统(L-band digital aviation communication system,L-DACS)前向链路接收机的问题,提出基于相关稀疏变分贝叶斯(correlated sparse variational Bayesian,cSVB)算法的DME脉冲干扰抑制方法。所提方法利用L-DACS系统正交频分复用(orthogonal frequency division multiplexing,OFDM)接收机的空子载波信息构建接收信号的压缩感知方程;然后,根据cSVB算法进行三层次贝叶斯信号建模,最后选择了两种变体算法重构DME干扰信号,并将其从时域接收信号中去除。理论分析与仿真结果表明,所提出的干扰抑制方法可以充分利用信号先验信息,进一步降低DME干扰信号估计的归一化均方误差,有效改善L-DACS系统的误码性能,提高传输可靠性。展开更多
传统压缩感知(CS,Compressive Sensing)成像方法一般假定目标精确位于事先划定的成像网格上,实际中由于散射点空间位置是连续分布的,因此偏离网格(Off-grid)问题必然存在.这会引起真实回波测量值与默认系统观测矩阵之间失配,导致传统CS...传统压缩感知(CS,Compressive Sensing)成像方法一般假定目标精确位于事先划定的成像网格上,实际中由于散射点空间位置是连续分布的,因此偏离网格(Off-grid)问题必然存在.这会引起真实回波测量值与默认系统观测矩阵之间失配,导致传统CS成像方法性能恶化.本文基于频率分集多输入多输出(FD-MIMO,Frequency Diverse Multiple-Input Multiple-Output)雷达,针对Off-grid目标提出了一种基于贝叶斯压缩感知的稀疏自聚焦(SAF-BCS,Sparse Autofocus Imaging Method Based on Bayesian Compressive Sensing)成像算法.该算法依据最大后验(MAP,Maximum A Posteriori)准则,利用变分贝叶斯学习技术求解含有Off-grid目标的稀疏像.与传统稀疏重构方法相比,所提方法充分利用了目标先验信息,可自适应调整参数,能够更好地反演稀疏目标,同时具有校正Off-grid目标的网格位置偏差以及估计噪声功率等优势.仿真结果表明SAF-BCS算法对网格划分不敏感,具有稳健的成像性能.展开更多
考虑到运动目标跟踪系统机动、隐身等人为对抗特征以及非视距、干扰、遮挡等环境因素,其系统建模、估计与辨识过程中越来越无法回避非线性、非高斯以及参数未知等复杂系统特征的影响.针对过程噪声先验信息不准确以及量测噪声非高斯环境...考虑到运动目标跟踪系统机动、隐身等人为对抗特征以及非视距、干扰、遮挡等环境因素,其系统建模、估计与辨识过程中越来越无法回避非线性、非高斯以及参数未知等复杂系统特征的影响.针对过程噪声先验信息不准确以及量测噪声非高斯环境下运动目标的非线性状态估计问题,提出一种基于自然梯度的噪声自适应变分贝叶斯(Variational Bayes,VB)滤波算法.首先,利用指数族分布具有统一表达形式的优势,构建参数化逆威沙特(Inverse-Wishart,IW)分布作为状态一步预测误差协方差的共轭先验分布,同时选取学生t分布重构因量测随机缺失导致的具有非高斯特点的似然函数;其次,在变分贝叶斯优化框架下采用平均场理论将状态变量联合后验分布近似分解为独立的变分分布,在此基础上,结合坐标上升方法更新各变量的变分分布参数;进而,结合Fisher信息矩阵推导置信下界最大化关于状态估计及其估计误差协方差的自然梯度,使非线性状态后验分布的近似分布沿梯度下降,以实现对状态后验概率密度函数(Probability density function,PDF)的“紧密”逼近.理论分析和仿真实验表明:相对传统的非线性滤波方法,本文算法对噪声不确定问题具有较好的自适应能力,并且能够获得较高的状态估计精度.展开更多
文摘A measurement system for the scattering characteristics of warhead fragments based on high-speed imaging systems offers advantages such as simple deployment,flexible maneuverability,and high spatiotemporal resolution,enabling the acquisition of full-process data of the fragment scattering process.However,mismatches between camera frame rates and target velocities can lead to long motion blur tails of high-speed fragment targets,resulting in low signal-to-noise ratios and rendering conventional detection algorithms ineffective in dynamic strong interference testing environments.In this study,we propose a detection framework centered on dynamic strong interference disturbance signal separation and suppression.We introduce a mixture Gaussian model constrained under a joint spatialtemporal-transform domain Dirichlet process,combined with total variation regularization to achieve disturbance signal suppression.Experimental results demonstrate that the proposed disturbance suppression method can be integrated with certain conventional motion target detection tasks,enabling adaptation to real-world data to a certain extent.Moreover,we provide a specific implementation of this process,which achieves a detection rate close to 100%with an approximate 0%false alarm rate in multiple sets of real target field test data.This research effectively advances the development of the field of damage parameter testing.
文摘针对测距仪(distance measure equipment,DME)信号严重干扰L频段数字航空通信系统(L-band digital aviation communication system,L-DACS)前向链路接收机的问题,提出基于相关稀疏变分贝叶斯(correlated sparse variational Bayesian,cSVB)算法的DME脉冲干扰抑制方法。所提方法利用L-DACS系统正交频分复用(orthogonal frequency division multiplexing,OFDM)接收机的空子载波信息构建接收信号的压缩感知方程;然后,根据cSVB算法进行三层次贝叶斯信号建模,最后选择了两种变体算法重构DME干扰信号,并将其从时域接收信号中去除。理论分析与仿真结果表明,所提出的干扰抑制方法可以充分利用信号先验信息,进一步降低DME干扰信号估计的归一化均方误差,有效改善L-DACS系统的误码性能,提高传输可靠性。
文摘传统压缩感知(CS,Compressive Sensing)成像方法一般假定目标精确位于事先划定的成像网格上,实际中由于散射点空间位置是连续分布的,因此偏离网格(Off-grid)问题必然存在.这会引起真实回波测量值与默认系统观测矩阵之间失配,导致传统CS成像方法性能恶化.本文基于频率分集多输入多输出(FD-MIMO,Frequency Diverse Multiple-Input Multiple-Output)雷达,针对Off-grid目标提出了一种基于贝叶斯压缩感知的稀疏自聚焦(SAF-BCS,Sparse Autofocus Imaging Method Based on Bayesian Compressive Sensing)成像算法.该算法依据最大后验(MAP,Maximum A Posteriori)准则,利用变分贝叶斯学习技术求解含有Off-grid目标的稀疏像.与传统稀疏重构方法相比,所提方法充分利用了目标先验信息,可自适应调整参数,能够更好地反演稀疏目标,同时具有校正Off-grid目标的网格位置偏差以及估计噪声功率等优势.仿真结果表明SAF-BCS算法对网格划分不敏感,具有稳健的成像性能.
文摘考虑到运动目标跟踪系统机动、隐身等人为对抗特征以及非视距、干扰、遮挡等环境因素,其系统建模、估计与辨识过程中越来越无法回避非线性、非高斯以及参数未知等复杂系统特征的影响.针对过程噪声先验信息不准确以及量测噪声非高斯环境下运动目标的非线性状态估计问题,提出一种基于自然梯度的噪声自适应变分贝叶斯(Variational Bayes,VB)滤波算法.首先,利用指数族分布具有统一表达形式的优势,构建参数化逆威沙特(Inverse-Wishart,IW)分布作为状态一步预测误差协方差的共轭先验分布,同时选取学生t分布重构因量测随机缺失导致的具有非高斯特点的似然函数;其次,在变分贝叶斯优化框架下采用平均场理论将状态变量联合后验分布近似分解为独立的变分分布,在此基础上,结合坐标上升方法更新各变量的变分分布参数;进而,结合Fisher信息矩阵推导置信下界最大化关于状态估计及其估计误差协方差的自然梯度,使非线性状态后验分布的近似分布沿梯度下降,以实现对状态后验概率密度函数(Probability density function,PDF)的“紧密”逼近.理论分析和仿真实验表明:相对传统的非线性滤波方法,本文算法对噪声不确定问题具有较好的自适应能力,并且能够获得较高的状态估计精度.
