Bayesian edge detector for SAR imagery using discontinuity-adaptive Markov random feld modeling

Synthetic aperture radar（SAR）image is severely affected by multiplicative speckle noise,which greatly complicates the edge detection.In this paper,by incorporating the discontinuityadaptive Markov random feld（DAMRF）and maximum a posteriori（MAP）estimation criterion into edge detection,a Bayesian edge detector for SAR imagery is accordingly developed.In the proposed detector,the DAMRF is used as the a priori distribution of the local mean reflectivity,and a maximum a posteriori estimation of it is thus obtained by maximizing the posteriori energy using gradient-descent method.Four normalized ratios constructed in different directions are computed,based on which two edge strength maps（ESMs）are formed.The fnal edge detection result is achieved by fusing the results of two thresholded ESMs.The experimental results with synthetic and real SAR images show that the proposed detector could effciently detect edges in SAR images,and achieve better performance than two popular detectors in terms of Pratt＇s fgure of merit and visual evaluation in most cases.     

基于GPR模型的自适应平方根容积卡尔曼滤波算法

与传统算法一样,动态系统的参数化模型(含噪声统计特性)未知或不够准确易导致容积卡尔曼滤波(CKF)效果严重下降,甚至滤波结果发散.为此,利用高斯过程回归(GPR)方法对训练数据进行学习,得到动态系统的状态转移GPR模型和量测GPR模型以及噪声统计特性,用以替代或增强原有动态系统模型,并将其融入到平方根容积卡尔曼滤波(SRCKF)中,分别提出了无模型高斯过程SRCKF (MFGP-SRCKF)和模型增强高斯过程SRCKF (MEGP-SRCKF)两种算法.仿真结果表明:这两种新的自适应滤波算法提高了动态系统模型精度,且实时自适应调整噪声的协方差,克服了传统算法滤波性能易受系统模型限制的问题;与MFGP-SRCKF相比,在给定一个不够准确的参数化模型,且有限的训练数据未能遍布估计状态空间的情况下,MEGP-SRCKF具备更高的滤波精度.     

FrFT-CSWSF:Estimating cross-range velocities of ground moving targets using multistatic synthetic aperture radar

Estimating cross-range velocity is a challenging task for space-borne synthetic aperture radar（SAR）, which is important for ground moving target indication（GMTI）. Because the velocity of a target is very small compared with that of the satellite, it is difficult to correctly estimate it using a conventional monostatic platform algorithm. To overcome this problem, a novel method employing multistatic SAR is presented in this letter. The proposed hybrid method, which is based on an extended space-time model（ESTIM） of the azimuth signal, has two steps： first, a set of finite impulse response（FIR） filter banks based on a fractional Fourier transform（FrFT） is used to separate multiple targets within a range gate; second, a cross-correlation spectrum weighted subspace fitting（CSWSF） algorithm is applied to each of the separated signals in order to estimate their respective parameters. As verified through computer simulation with the constellations of Cartwheel, Pendulum and Helix, this proposed time-frequency-subspace method effectively improves the estimation precision of the cross-range velocities of multiple targets.     

Model-based robust estimation and fault detection for MEMS-INS/GPS integrated navigation systems

In micro-electro-mechanical system based inertial navigation system（MEMS-INS）/global position system（GPS） integrated navigation systems, there exist unknown disturbances and abnormal measurements. In order to obtain high estimation accuracy and enhance detection sensitivity to faults in measurements, this paper deals with the problem of model-based robust estimation（RE） and fault detection（FD）. A filter gain matrix and a post-filter are designed to obtain a RE and FD algorithm with current measurements, which is different from most of the existing priori filters using measurements in one-step delay. With the designed filter gain matrix, the H-infinity norm of the transfer function from noise inputs to estimation error outputs is limited within a certain range; with the designed post-filter, the residual signal is robust to disturbances but sensitive to faults. Therefore, the algorithm can guarantee small estimation errors in the presence of disturbances and have high sensitivity to faults. The proposed method is evaluated in an integrated navigation system, and the simulation results show that it is more effective in position estimation and fault signal detection than priori RE and FD algorithms.     

装备研制费用参数估算方法研究

参数法是进行装备研制费用估算的有效方法之一.介绍了参数费用估算的历史和基本原理,分析了装备研制费用的主要驱动因素,将其作为估算参数,并给出了装备研制费用参数估算的基本流程.最后,基于国际先进的参数估算软件True Planning,对国外新一代直升机进行了参数估算的实例研究,显示了参数估算的有效性.     

基于倒谱的运动模糊图像参数估计方法

通过分析运动模糊图像点扩展函数的倒谱特性,提出了基于倒谱特性的运动模糊参数估计方法。仿真实验结果与理论分析一致,验证了理论分析的正确性。数值实验表明,该方法具有较高的准确性,且具有较强的鲁棒性。     

一种基于DFT的高精度载波频偏估计算法

针对无线数字通信中的载波频率同步问题,提出一种新的载波频偏估计算法。利用离散傅里叶变换（DFT）内插技术,在较大频偏范围内估计出载波频偏,并且在估计精度上相对于DFT法有了很大的提高。这种算法不需要增加DFT数据长度,适合突发通信方式。进行了Monte Carlo仿真实验,仿真结果表明新算法不仅具有宽的估计范围,还具有高的估计精度。     

水下复杂环境下基于SINS/USBL/DVL多源信息融合的组合导航算法

针对复杂水下声场环境下高精度、长航时导航与定位的需求,构建了捷联惯性导航系统(SINS)/超短基线(USBL)相对测量信息的观测方程和SINS/声学多普勒测速仪(DVL)的观测方程,提出了一种融合SINS/USBL/DVL多源信息的组合定位算法.为解决声学量测信息不确定引起的导航性能下降的问题,充分考虑水声野值所导致的...     

U-D分解的固定点平滑新算法及其在飞行试验中的应用

 本文根据Rauch固定点平滑公式,提出了一种U-D分解的固定点平滑新算法。这一算法不仅具有良好的数值稳定性和可靠性,而且计算量较少;计算效率是Bryson-Ho Y C固定点平滑计算效率的1.5倍以上。将这种新算法用于飞机运动状态初值的确定,提高了飞机气动参数辨识精度。     

多重共线性诊断及其在飞行器非线性气动模型结构确定和参数估计中的应用

本文基于飞行器气动建模的目的，详细地给出了不同情形下非线性气动模型的多项式及样条函数描述方法。指出由于多种因素的影响，必然导致候选非线性气动模型结构中存在共线性问题，从而使得传统的模型结构确定和参数估计方法不再适用。本文基于共线性诊断，候选模型的选择及主元回归等方法 效地解决了存在共线性情况下非线气动模型结构的确定和参数估计问题。