This paper considers multi-frequency passive radar and develops a multi-frequency joint direction of arrival(DOA)estimation algorithm to improve estimation accuracy and resolution.The developed algorithm exploits the ...This paper considers multi-frequency passive radar and develops a multi-frequency joint direction of arrival(DOA)estimation algorithm to improve estimation accuracy and resolution.The developed algorithm exploits the sparsity of targets in the spatial domain.Specifically,we first extract the required frequency channel data and acquire the snapshot data through a series of preprocessing such as clutter suppression,coherent integration,beamforming,and constant false alarm rate(CFAR)detection.Then,based on the framework of sparse Bayesian learning,the target’s DOA is estimated by jointly extracting the multi-frequency data via evidence maximization.Simulation results show that the developed algorithm has better estimation accuracy and resolution than other existing multi-frequency DOA estimation algorithms,especially under the scenarios of low signalto-noise ratio(SNR)and small snapshots.Furthermore,the effectiveness is verified by the field experimental data of a multi-frequency FM-based passive radar.展开更多
Inverse synthetic aperture radar(ISAR) imaging can be regarded as a narrow-band version of the computer aided tomography(CT). The traditional CT imaging algorithms for ISAR, including the polar format algorithm(PFA) a...Inverse synthetic aperture radar(ISAR) imaging can be regarded as a narrow-band version of the computer aided tomography(CT). The traditional CT imaging algorithms for ISAR, including the polar format algorithm(PFA) and the convolution back projection algorithm(CBP), usually suffer from the problem of the high sidelobe and the low resolution. The ISAR tomography image reconstruction within a sparse Bayesian framework is concerned. Firstly, the sparse ISAR tomography imaging model is established in light of the CT imaging theory. Then, by using the compressed sensing(CS) principle, a high resolution ISAR image can be achieved with limited number of pulses. Since the performance of existing CS-based ISAR imaging algorithms is sensitive to the user parameter, this makes the existing algorithms inconvenient to be used in practice. It is well known that the Bayesian formalism of recover algorithm named sparse Bayesian learning(SBL) acts as an effective tool in regression and classification,which uses an efficient expectation maximization procedure to estimate the necessary parameters, and retains a preferable property of the l0-norm diversity measure. Motivated by that, a fully automated ISAR tomography imaging algorithm based on SBL is proposed.Experimental results based on simulated and electromagnetic(EM) data illustrate the effectiveness and the superiority of the proposed algorithm over the existing algorithms.展开更多
Bayesian networks (BNs) have become increasingly popular in recent years due to their wide-ranging applications in modeling uncertain knowledge. An essential problem about discrete BNs is learning conditional probabil...Bayesian networks (BNs) have become increasingly popular in recent years due to their wide-ranging applications in modeling uncertain knowledge. An essential problem about discrete BNs is learning conditional probability table (CPT) parameters. If training data are sparse, purely data-driven methods often fail to learn accurate parameters. Then, expert judgments can be introduced to overcome this challenge. Parameter constraints deduced from expert judgments can cause parameter estimates to be consistent with domain knowledge. In addition, Dirichlet priors contain information that helps improve learning accuracy. This paper proposes a constrained Bayesian estimation approach to learn CPTs by incorporating constraints and Dirichlet priors. First, a posterior distribution of BN parameters is developed over a restricted parameter space based on training data and Dirichlet priors. Then, the expectation of the posterior distribution is taken as a parameter estimation. As it is difficult to directly compute the expectation for a continuous distribution with an irregular feasible domain, we apply the Monte Carlo method to approximate it. In the experiments on learning standard BNs, the proposed method outperforms competing methods. It suggests that the proposed method can facilitate solving real-world problems. Additionally, a case study of Wine data demonstrates that the proposed method achieves the highest classification accuracy.展开更多
The sparse recovery algorithms formulate synthetic aperture radar （SAR） imaging problem in terms of sparse representation （SR） of a small number of strong scatters＇ positions among a much large number of potentia...The sparse recovery algorithms formulate synthetic aperture radar （SAR） imaging problem in terms of sparse representation （SR） of a small number of strong scatters＇ positions among a much large number of potential scatters＇ positions, and provide an effective approach to improve the SAR image resolution. Based on the attributed scatter center model, several experiments were performed with different practical considerations to evaluate the performance of five representative SR techniques, namely, sparse Bayesian learning （SBL）, fast Bayesian matching pursuit （FBMP）, smoothed 10 norm method （SL0）, sparse reconstruction by separable approximation （SpaRSA）, fast iterative shrinkage-thresholding algorithm （FISTA）, and the parameter settings in five SR algorithms were discussed. In different situations, the performances of these algorithms were also discussed. Through the comparison of MSE and failure rate in each algorithm simulation, FBMP and SpaRSA are found suitable for dealing with problems in the SAR imaging based on attributed scattering center model. Although the SBL is time-consuming, it always get better performance when related to failure rate and high SNR.展开更多
针对测距仪(distance measure equipment,DME)信号干扰L频段数字航空通信系统1(L-band digital aeronautical communication system 1,L-DACS1)正交频分复用(orthogonal frequency-division multiplexing,OFDM)接收机的问题,提出基于块...针对测距仪(distance measure equipment,DME)信号干扰L频段数字航空通信系统1(L-band digital aeronautical communication system 1,L-DACS1)正交频分复用(orthogonal frequency-division multiplexing,OFDM)接收机的问题,提出基于块稀疏贝叶斯学习边界优化(block sparsEbayesian learning-thEbound optimization,BSBL-BO)算法的DME脉冲干扰抑制方法。首先,利用OFDM接收机空子载波不传输有用信号的特点构造针对DME脉冲干扰信号的压缩感知模型;然后基于BSBL-BO算法重构DME脉冲干扰信号;最后在时域进行干扰消除。仿真结果表明,该方法比已有的脉冲干扰抑制方法具有更高的重构精度和更快的运算速度,进一步降低了OFDM接收机的误比特率,提高了L-DACS1系统前向链路传输性能。展开更多
L频段数字航空通信系统(L-band digital aviation communication system,L-DACS)是未来面向航路阶段的空地数据链路,其工作频段部署在两个测距仪(distance measure equipment,DME)工作频段之间,为了消除测距仪产生的高功率脉冲信号对L-D...L频段数字航空通信系统(L-band digital aviation communication system,L-DACS)是未来面向航路阶段的空地数据链路,其工作频段部署在两个测距仪(distance measure equipment,DME)工作频段之间,为了消除测距仪产生的高功率脉冲信号对L-DACS系统前向链路正交频分复用接收机的干扰,本文提出基于扩展稀疏贝叶斯-边界优化(extended block sparse Bayesian learning-boundary optimization,EBSBL-BO)算法的高功率DME脉冲干扰抑制方法。首先,利用L-DACS系统正交频分复用接收机的空子载波建立DME干扰信号压缩感知模型;然后,基于EBSBL-BO算法对DME信号进行重构;最后将高功率DME脉冲信号在时域消除。仿真结果显示:本文算法与其他稀疏贝叶斯重构算法相比,本文算法DME脉冲信号重构精度更高,正交频分复用接收机误码率更低,可有效改善L-DACS系统正交频分复用接收性能。展开更多
为了保证水下设备的长期稳定通信,提出了一种基于改进的快速边缘似然最大化的稀疏贝叶斯学习(sparse Bayesian learning based on improved fast marginal likelihood maximization, IFM-SBL)算法,对水声信道进行低复杂度、高性能的估...为了保证水下设备的长期稳定通信,提出了一种基于改进的快速边缘似然最大化的稀疏贝叶斯学习(sparse Bayesian learning based on improved fast marginal likelihood maximization, IFM-SBL)算法,对水声信道进行低复杂度、高性能的估计。特别是在低信噪比情况下,通过阈值去噪和离散傅里叶变换降噪,可以进一步提升算法的性能。仿真和海试结果表明,所提的IFM-SBL信道估计后的输出误码率与基于期望最大化的稀疏贝叶斯学习(sparse Bayesian learning based on expectation maximization, EM-SBL)算法相似,且验证了算法在低信噪比和快慢时变信道中都具有良好的鲁棒性。在运行速度方面,FM-SBL算法与IFM-SBL算法比EM-SBL算法提高了约90%,大大减少了信道估计时间。展开更多
传统的基于稀疏恢复的波达方向(direction of arrival,DOA)估计算法使用密集的采样网格,导致计算量显著增加,且对邻近入射信号的估计精度不高。针对这一问题,提出一种快速高精度DOA估计算法。该算法首先使用网格进化方法降低网格点总数...传统的基于稀疏恢复的波达方向(direction of arrival,DOA)估计算法使用密集的采样网格,导致计算量显著增加,且对邻近入射信号的估计精度不高。针对这一问题,提出一种快速高精度DOA估计算法。该算法首先使用网格进化方法降低网格点总数。然后,对噪声方差和信号功率进行二次估计,进而使用离网求根稀疏贝叶斯学习(off-grid root sparse Bayesian learning,OGRSBL)技术来实现入射角的精确估计。仿真表明,相比传统稀疏贝叶斯学习类算法,所提算法计算效率高,同时对紧邻信号有着更好的估计能力。展开更多
基金supported by the National Natural Science Foundation of China(62071335,61931015,61831009)the Technological Innovation Project of Hubei Province of China(2019AAA061).
文摘This paper considers multi-frequency passive radar and develops a multi-frequency joint direction of arrival(DOA)estimation algorithm to improve estimation accuracy and resolution.The developed algorithm exploits the sparsity of targets in the spatial domain.Specifically,we first extract the required frequency channel data and acquire the snapshot data through a series of preprocessing such as clutter suppression,coherent integration,beamforming,and constant false alarm rate(CFAR)detection.Then,based on the framework of sparse Bayesian learning,the target’s DOA is estimated by jointly extracting the multi-frequency data via evidence maximization.Simulation results show that the developed algorithm has better estimation accuracy and resolution than other existing multi-frequency DOA estimation algorithms,especially under the scenarios of low signalto-noise ratio(SNR)and small snapshots.Furthermore,the effectiveness is verified by the field experimental data of a multi-frequency FM-based passive radar.
基金Project(61171133)supported by the National Natural Science Foundation of ChinaProject(11JJ1010)supported by the Natural Science Fund for Distinguished Young Scholars of Hunan Province,ChinaProject(61101182)supported by the National Natural Science Foundation for Young Scientists of China
文摘Inverse synthetic aperture radar(ISAR) imaging can be regarded as a narrow-band version of the computer aided tomography(CT). The traditional CT imaging algorithms for ISAR, including the polar format algorithm(PFA) and the convolution back projection algorithm(CBP), usually suffer from the problem of the high sidelobe and the low resolution. The ISAR tomography image reconstruction within a sparse Bayesian framework is concerned. Firstly, the sparse ISAR tomography imaging model is established in light of the CT imaging theory. Then, by using the compressed sensing(CS) principle, a high resolution ISAR image can be achieved with limited number of pulses. Since the performance of existing CS-based ISAR imaging algorithms is sensitive to the user parameter, this makes the existing algorithms inconvenient to be used in practice. It is well known that the Bayesian formalism of recover algorithm named sparse Bayesian learning(SBL) acts as an effective tool in regression and classification,which uses an efficient expectation maximization procedure to estimate the necessary parameters, and retains a preferable property of the l0-norm diversity measure. Motivated by that, a fully automated ISAR tomography imaging algorithm based on SBL is proposed.Experimental results based on simulated and electromagnetic(EM) data illustrate the effectiveness and the superiority of the proposed algorithm over the existing algorithms.
基金supported by the National Natural Science Foundation of China(61573285)the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University,China(CX201619)
文摘Bayesian networks (BNs) have become increasingly popular in recent years due to their wide-ranging applications in modeling uncertain knowledge. An essential problem about discrete BNs is learning conditional probability table (CPT) parameters. If training data are sparse, purely data-driven methods often fail to learn accurate parameters. Then, expert judgments can be introduced to overcome this challenge. Parameter constraints deduced from expert judgments can cause parameter estimates to be consistent with domain knowledge. In addition, Dirichlet priors contain information that helps improve learning accuracy. This paper proposes a constrained Bayesian estimation approach to learn CPTs by incorporating constraints and Dirichlet priors. First, a posterior distribution of BN parameters is developed over a restricted parameter space based on training data and Dirichlet priors. Then, the expectation of the posterior distribution is taken as a parameter estimation. As it is difficult to directly compute the expectation for a continuous distribution with an irregular feasible domain, we apply the Monte Carlo method to approximate it. In the experiments on learning standard BNs, the proposed method outperforms competing methods. It suggests that the proposed method can facilitate solving real-world problems. Additionally, a case study of Wine data demonstrates that the proposed method achieves the highest classification accuracy.
基金Project(61171133)supported by the National Natural Science Foundation of ChinaProject(11JJ1010)supported by the Natural Science Fund for Distinguished Young Scholars of Hunan Province,ChinaProject(61101182)supported by National Natural Science Foundation for Young Scientists of China
文摘The sparse recovery algorithms formulate synthetic aperture radar （SAR） imaging problem in terms of sparse representation （SR） of a small number of strong scatters＇ positions among a much large number of potential scatters＇ positions, and provide an effective approach to improve the SAR image resolution. Based on the attributed scatter center model, several experiments were performed with different practical considerations to evaluate the performance of five representative SR techniques, namely, sparse Bayesian learning （SBL）, fast Bayesian matching pursuit （FBMP）, smoothed 10 norm method （SL0）, sparse reconstruction by separable approximation （SpaRSA）, fast iterative shrinkage-thresholding algorithm （FISTA）, and the parameter settings in five SR algorithms were discussed. In different situations, the performances of these algorithms were also discussed. Through the comparison of MSE and failure rate in each algorithm simulation, FBMP and SpaRSA are found suitable for dealing with problems in the SAR imaging based on attributed scattering center model. Although the SBL is time-consuming, it always get better performance when related to failure rate and high SNR.
文摘针对测距仪(distance measure equipment,DME)信号干扰L频段数字航空通信系统1(L-band digital aeronautical communication system 1,L-DACS1)正交频分复用(orthogonal frequency-division multiplexing,OFDM)接收机的问题,提出基于块稀疏贝叶斯学习边界优化(block sparsEbayesian learning-thEbound optimization,BSBL-BO)算法的DME脉冲干扰抑制方法。首先,利用OFDM接收机空子载波不传输有用信号的特点构造针对DME脉冲干扰信号的压缩感知模型;然后基于BSBL-BO算法重构DME脉冲干扰信号;最后在时域进行干扰消除。仿真结果表明,该方法比已有的脉冲干扰抑制方法具有更高的重构精度和更快的运算速度,进一步降低了OFDM接收机的误比特率,提高了L-DACS1系统前向链路传输性能。
文摘基于动态时间规整的叠前道集剩余时差校正方法存在动态时间规整算法对噪声敏感,准确计算规整路径困难;算法采用逐点搬家法,直接对地震道作剩余时差校正容易引起地震波形畸变的问题。提出一种联合稀疏贝叶斯学习(Sparse Bayesian Learning,SBL)和动态时间规整(Dynamic Time Warping,DTW)的叠前道集剩余时差校正方法,采用SBL对地震道集进行稀疏表示,再利用DTW对稀疏表示结果进行剩余时差校正,处理后重构地震记录。结果表明,SBL具有良好的噪声鲁棒性,较少的局部最小值,以及全局最优解同时也是最稀疏解,稀疏分解后得到地下地层单位冲击响应,消除了子波影响,再进行时差校正就能避免波形畸变,同时实现了高保真剩余时差校正和随机噪声压制。数值模拟和实际资料处理结果表明该方法具有良好的应用效果。
文摘为了保证水下设备的长期稳定通信,提出了一种基于改进的快速边缘似然最大化的稀疏贝叶斯学习(sparse Bayesian learning based on improved fast marginal likelihood maximization, IFM-SBL)算法,对水声信道进行低复杂度、高性能的估计。特别是在低信噪比情况下,通过阈值去噪和离散傅里叶变换降噪,可以进一步提升算法的性能。仿真和海试结果表明,所提的IFM-SBL信道估计后的输出误码率与基于期望最大化的稀疏贝叶斯学习(sparse Bayesian learning based on expectation maximization, EM-SBL)算法相似,且验证了算法在低信噪比和快慢时变信道中都具有良好的鲁棒性。在运行速度方面,FM-SBL算法与IFM-SBL算法比EM-SBL算法提高了约90%,大大减少了信道估计时间。
