Optimum decoding-based smoothing algorithm for dynamic systems

A new smoothing algorithm for discrete models is presented. For the disturbance noise and the observation noise, only independency is assumed. Moreover the models’ functions are not limited to continuous functions, i.e. they can be non-continuous. This algorithm estimates the states by first quantizing them and then using the Viterbi decoding algorithm. Simulation results have shown that for some non-linear models the new algorithm performs better than the extended Kalman filter algorithm, while it performs almost as well as the Kalman filter algorithm for linear models with gaussian noise.     

Discrete-time complementary models and smoothing algorithms: The correlated noise case

The concept of complementary models for discrete-time linear finite-dimensional systems with correlated observation and process noise is developed. Using this concept, a new algorithm for the fixed interval smoothing problem is obtained. The new algorithm offers great flexibility with respect to changes in the initial state variancePi_{0}. Next, the relationship among the new smoothing algorithm, the two-filter smoother, and the reversed-time Kalman filter is explored. It is shown that a similarity transformation on the Hamiltonian system simultaneously produces the new smoothing algorithm, as well as the reversed-time Kalman filter.     

Non-linear smoothing algorithm for multi-dimensional dynamic systems

A new sub-optimum smoothing algorithm is presented for multi-dimensional dynamic systems. This algorithm is based upon quantization, multiple hypothesis testing, and the Viterbi decoding algorithm. The estimation of state vectors is carried out sequentially, component-by-component, and in parallel. A considerable memory reduction is achieved for state estimation implementation with the proposed algorithm. Simulation results, some of which are presented, show that the sub-optimum algorithm performs better than the extended Kalman filter algorithm for some non-linear multi-dimensional models with white gaussian disturbance and observation noises. In addition, the performance of the sub-optimum algorithm is almost as good as the Kalman filter algorithm for linear multi-dimensional models with white gaussian noise.     

扫描输入彩色地图的去噪与自动分割*

针对扫描输入纸质彩色地图噪声干扰严重,自动分割效果不理想的现状,提出了一种先去噪后分割的地图分割新方法。根据噪声性质采用维纳滤波与小波阈值萎缩相结合的去噪方法,进行基于CIE-L*a*b*色空间的颜色聚类。与传统的彩图分割相比,该方法对噪声具有更强的鲁棒性,处理速度快且分割清晰。     

WSNs网络下移动机器人HuberM-CKF离散滤波定位*

针对传统移动机器人定位算法精度不高的问题,提出一种基于无线传感器网络HurbM-CKalman滤波(HCKF)算法的移动机器人定位算法。利用HurbM极大似然估计代价函数,求解线性化后CKF观测矩阵,从而解决CKF滤波算法在未知非高斯白噪声干扰下估计精度不高问题。然后,在体育馆基于WSNs网络构建了移动机器人定位实验环境,并结合移动机器人动力学模型,对HCKF、CKF算法的定位精度进行对比。结果显示,在不含噪声干扰和含未知噪声干扰两种情况下,HCKF算法定位精度分别比CKF算法提高7%和15%。     

Optimal non-linear estimation†

For the non-linear estimation problem with non-linear plant and observation models, white gaussian excitations and continuous data, the state-vector a posteriori probabilities for prediction and smoothing are obtained via the 'partition theorem'. Moreover, for the special class of non-linear estimation problems with linear models excited by white gaussian noise, and with non-gaussian initial state, explicit results are obtained for the a posteriori probabilities, the optimal estimates and the corresponding error-covariance matrices for filtering, prediction and smoothing. In addition, for the latter problem, approximate but simpler expressions are obtained by using a gaussian sum approximation of the initial state-vector probability density. As a special case of the above results, optimal linear smoothing algorithms are obtained in a new form.     

Non-linear state smoothing for discrete dynamic systems with past histories

A state smoothing scheme is presented for dynamic systems with past histories. The state in the future is a given (linear or non-linear) function of the disturbance noise and both the present and N — I past discrete values of the state. An observation in the present is a given function of the observation noise and both the present and N — 1 past discrete values of the state. The proposed scheme is based upon multiple hypothesis testing and the Viterbi algorithm of information theory, Simulation results, some of which are presented, have shown that the proposed scheme performs well.     

结合区域分割和双边滤波的图像去噪新算法

提出一种结合区域分割和双边滤波的图像高斯噪声抑制新算法。基于像素的双边滤波器在滤波时,由于平滑系数的选择受到噪声的干扰,在图像边缘区域的滤波存在一定的盲目性,导致滤波结果中结构信息不能有效保持。本文在图像分割的基础上利用区域图来指导双边滤波过程,根据区域内的噪声属性和区域间的相似程度来分别计算相应像素间的滤波平滑系数。通过对区域内与区域间进行不同模式的滤波,增强了滤波算法对图像结构的自适应性。实验结果表明,该算法在获得良好去噪效果的同时,能有效保持图像的结构信息。     

Smoothing of ultrasound images using a new selective average filter

Ultrasound images are strongly affected by speckle noise making visual and computational analysis of the structures more difficult. Usually, the interference caused by this kind of noise reduces the efficiency of extraction and interpretation of the structural features of interest. In order to overcome this problem, a new method of selective smoothing based on average filtering and the radiation intensity of the image pixels is proposed. The main idea of this new method is to identify the pixels belonging to the borders of the structures of interest in the image, and then apply a reduced smoothing to these pixels, whilst applying more intense smoothing to the remaining pixels. Experimental tests were conducted using synthetic ultrasound images with speckle noisy added and real ultrasound images from the female pelvic cavity. The new smoothing method is able to perform selective smoothing in the input images, enhancing the transitions between the different structures presented. The results achieved are promising, as the evaluation analysis performed shows that the developed method is more efficient in removing speckle noise from the ultrasound images compared to other current methods. This improvement is because it is able to adapt the filtering process according to the image contents, thus avoiding the loss of any relevant structural features in the input images.     

A general smoothing equation for signal estimation using randomly delayed observations in the correlated signal-noise case

This paper treats the least-squares linear smoothing problem for signal estimation using measurements contaminated by additive white noise correlated with the signal, with stochastic delays. We derive a general smoothing equation which is applied to obtain specific smoothing algorithms, which are referred in the signal estimation literature as fixed-point, fixed-interval, and fixed-lag smoothing. Using an innovation approach, the general smoothing equation is derived without requiring the whole knowledge of the state-space model generating the signal, but only covariance information of the signal and the observation noise, as well as the delay probabilities.     

改进滑动平均滤波在PSO辨识中的应用

在实际系统信号中不可避免的会存在噪声和瞬时扰动,噪声过大会严重影响粒子群优化算法(pso)的辨识结果.针对强噪声环境下利用PSO算法进行参数辨识精度差甚至不能收敛的问题,提出了一种改进的滑动平均滤波算法,通过动态修正滑动平均后的数据相位,来实现无滞后的滑动平均滤波效果.仿真实验表明,对这种改进滑动平均滤波算法应用于PS...     

State smoothing and filtering with stack sequential schemes for non-linear dynamic systems with an Nth-order memory

A stack sequential decoding algorithm is used estimate states of dynamic models with an Nth-order memory.The state at the future and observation at the present are linear or non-linear functions of the disturbance noise, the observation noise, and either the present or N - 1 past discrete values of the state. States are estimated by approximating the state model by a finite state machine and then using a stack sequential decoding algorithm of Information Theory. The proposed suboptimum scheme is faster and more practical than the estimation schemes using the Viterbi decoding algorithm.     

自适应高斯粒子滤波在导航中的应用

针对粒子滤波中重要性密度函数难以选取和粒子退化导致的计算精度下降的问题,提出一种新的自适应高斯粒子滤波算法。通过高斯混合密度函数和UT变换来获取状态均值和协方差阵,选择并计算合适的自适应因子来调节均值和方差,在迭代过程中可动态调节重要性密度函数,并用WEM和EM步骤代替重采样,上述滤波算法考虑了最新量测信息的影响,使滤波性能明显改善,能更好地解决非线性非高斯系统模型的抗干扰问题。将提出的算法应用于SINS/GPS组合导航系统跑车试验中,结果表明上述滤波算法能提高导航解算的精度,其性能明显优于已有滤波,同时验证了当系统出现噪声干扰突然变化时提出算法的有效性。     

一种新的基于噪声点检测的脉冲噪声去噪算法

中值滤波是广泛应用于去除脉冲噪声的一种非线性去噪方法,但是单一地使用中值滤波方法去除脉冲噪声会造成图像细节信息的丢失,从而使图像变得模糊。基于噪声点检测的脉冲噪声滤波方法可以在滤除噪声的同时有效地保持图像的细节信息。该文在此基础上提出了一种新的基于噪声点检测的脉冲噪声滤波算法,该算法在检测噪声点时用被检测点的中值滤波结果作为判定该点是否为噪声点的依据。而在滤除被检测到的噪声点时,采用的是迭代的中值滤波算法。从实验结果中可以看到,与其它中值滤波算法相比,该文的算法在去除脉冲噪声时能取得较好的效果。     

自适应平方根无迹卡尔曼滤波算法

将高斯过程回归融入平方根无迹卡尔曼滤波(SRUKF)算法,本文提出了一种不确定系统模型协方差自适应调节滤波算法.该算法分为学习和估计两部分:学习阶段用高斯过程对训练数据进行学习,得到系统回归模型及噪声协方差;估计阶段由回归模型代替状态方程和观测方程,相应的噪声协方差实时自适应调整.该方法克服了传统方法容易受系统动态模型不确定性和噪声协方差不准确限制的问题,仿真结果验证了算法的有效性.     

基于深度学习的复杂观测条件下工频磁异常探测

复杂观测条件下使用工频磁场探测人员、车辆、飞行目标等多类型目标造成的磁场扰动时,受到复杂环境下电磁噪声、供电设备及外来物体扰动等影响,工频磁场扰动信号具有噪声多、干扰强等特征,为有效削弱噪声及干扰对工频磁场扰动信号的影响,实现工频磁场扰动探测,该文利用实验数据对复杂观测条件下的磁场扰动信号进行特征分析,提出了一种基于深度学习的工频磁场异常探测方法,通过提取正常状态与有扰动状态的信号序列,将该信号输入神经网络训练,得到准确检测工频磁场异常信号的网络模型。实验结果表明,该方法的识别准确率在80%以上。     

New design of linear least-squares fixed-interval smoother using covariance information

This paper derives recursive linear least-squares fixed-interval smoothing algorithm using covariance information by applying an invariant imbedding method to a Wiener-Hopf integral equation. The algorithm is obtained for the white plus coloured observation noise. The signal process is nonstationary stochastic. Autocovariance functions of the signal and the coloured noise are expressed using a degenerate kernel. The degenerate kernel can represent general covariance functions of nonstationary stochastic processes by a finite sum of nonrandom functions.     

基于井下环境的SIFT算法研究

针对煤矿环境中现有图像特征匹配算法不适用的问题,提出了新的特征匹配算法。该算法首先对图像进行Curvelet去噪预处理,然后采用SIFT进行特征向量的构建和匹配,通过RANSAC方法的优化改进去除误匹配点,通过建立投影变换模型实现图像的拼接。实验表明,该算法在噪声大、光照不均、模糊的复杂环境中有较好的鲁棒性,解决了煤矿环境中图像容易误匹配的现象,拼接效果平滑自然。     

基于小波多层阈值平滑处理的航测图像降噪

在基于小波的图像降噪处理算法中,有时候存在着对图像信息的过扼杀和对噪声信息的欠滤出,使得对图像降噪后反而降低了信噪比,这就不利于对图像的分析和观察,尤其是对航测l冬I像的处理影响很大,主要是由于阈值的选取和处理方法不合适引起的.在提出了针对不同类型噪声的三种平滑阈值降噪处理方法,结合Birge-Massart策略得出的多层阈值,对小波系数进行软-硬阈值之间的处理,就是在不同尺度的频带内对小波系数进行逐渐向零收缩,以达到较好的保持有用信息,降低噪声,仿真实验表明对一般低信噪比(SNR)的航测图像做降噪处理时,效果很好.     

基于改进粒子群算法的自适应陷波器设计

在信号处理中,接收信号常伴随着干扰和噪声,这就需要最优滤波器来实现,其中工频干扰的消除则以自适应陷波器为最优。利用粒子群算法自适应地调节其权值,得到与干扰信号接近的期望信号,最终达到消除干扰得到有用信号的目的。同时,针对此算法存在局部收敛和收敛速度不高的问题,提出了改进方法。计算机仿真结果表明了该改进粒子群算法在自适应陷波器设计上的有效性,并取得了较高的效率。