An algorithm and observability research of autonomous navigation and joint attitude determination in asteroid exploration descent stage

In this paper, an autonomous relative navigation and joint attitude determination algorithm in asteroid exploration descent stage is researched based on feature point information of perpendicular asteroid surface image observed by optical navigation camera, distance vectors from spacecraft to asteroid measured by three angled installed lidars and relative velocity increment measured by accelerometer when the relative distance vector to the centroid of asteroid can not be obtained. The inertial attitude of spacecraft is determined by sun vector, star vectors and inertial angular velocity respectively measured by sun sensor, star trackers and inertial reference unit. Also, in order to obtain measurement error model transferred from sensor noise, a covariance matrix solver considering error correlation is presented via the error model of normalized vector to first order. Numerical simulation and improved observability evaluation of filtering are undertaken to discuss the results of complete sensor observation and weak observation of lidars, and verify the effectiveness of the presented relative navigation and attitude determination algorithm.     

Optimality analysis of one-step OOSM filtering algorithms in target tracking

In centralized multisensor tracking systems, there are out-of-sequence measurements (OOSMs) frequently arising due to different time delays in communication links and varying pre-processing times at the sensor. Such OOSM arrival can induce the “negative-time measurement update” problem, which is quite common in real multisensor tracking systems. The A1 optimal update algorithm with OOSM is presented by Bar-Shalom for one-step case. However, this paper proves that the optimality of A1 algorithm is lost in direct discrete-time model (DDM) of the process noise, it holds true only in discretized continuous-time model (DCM). One better OOSM filtering algorithm for DDM case is presented. Also, another new optimal OOSM filtering algorithm, which is independent of the discrete time model of the process noise, is presented here. The performance of the two new algorithms is compared with that of A1 algorithm by Monte Carlo simulations. The effectiveness and correctness of the two proposed algorithms are validated by analysis and simulation results.     

A combination algorithm for MEMS hydrophone signal and noise separation北大核心CSCD

In the light of the problem of MEMS hydrophone data “submerged” in strong noise field, the combination filter of LMS adaptive noise cancellation and Fourier transform filtering is proposed. The filter algorithm is applied to MEMS hydrophone signal and noise separation. When the frequency of signal is given, the combination filter algorithm is used for signal extraction and the ideal signal performance comparison. Simulation results show that the effect of extraction resolution in strong noise field of -15 dB is higher in the algorithm. The algorithm can be used to search similar to the “black box” case. The filter is used to separate signal and noise in the Fen machine test of North University of China. The results show that the algorithm is efficient and Practicability. ©, 2014, The Editorial Office of Chinese Journal of Sensors and Actuators. All right reserved.     

基于期望最大化算法的自适应噪声交互多模型滤波

A novel method under the interactive multiple model (IMM) filtering framework is presented in this paper, in which the expectation-maximization (EM) algorithm is used to identify the process noise covariance Q online. For the existing IMM filtering theory, the matrix Q is determined by means of design experience, but Q is actually changed with the state of the maneuvering target. Meanwhile it is severely influenced by the environment around the target, i.e., it is a variable of time. Therefore, the experiential covariance Q can not represent the influence of state noise in the maneuvering process exactly. Firstly, it is assumed that the evolved state and the initial conditions of the system can be modeled by using Gaussian distribution, although the dynamic system is of a nonlinear measurement equation, and furthermore the EM algorithm based on IMM filtering with the Q identification online is proposed. Secondly, the truncated error analysis is performed. Finally, the Monte Carlo simulation results are given to show that the proposed algorithm outperforms the existing algorithms and the tracking precision for the maneuvering targets is improved efficiently.     

SCR algorithm: Saving/restoring states of file systems

Fault-tolerance is very important in cluster computing and has been implemented in many famous cluster-computing systems using checkpoint/restart mechanisms,But existent check-pointing algorithms cannot restore the states of a file system when roll-backing the running of a program,so there are many restrictions on file accesses in existent fault-tolerance systems.SCR algorithm,an algorithm based on atomic operation and consistent schedule,which can restore the states of file systems,is presented into idem-potent operations and non-idem-potent operations.systems are classified into idem-potent operations and non-idem-potent operations.A non-idem-potent operation modifies a file system‘s states,while an idem-potent operation does not.SCR algorithm tracks changes of the file system states.It logs each non-idem-potent operation used by user programs and the information that can restore the operation in disks.When check-pointing roll-backing the program,SCR algorithm will revert the file system states to the last checkpoint time.By using SCR algorithm,users are allowed to use any file operation in their programs.     

A Fusion Kalman Filter and UFIR Estimator Using the Influence Function Method

In this paper,the Kalman filter(KF)and the unbiased finite impulse response(UFIR)filter are fused in the discrete-time state-space to improve robustness against uncertainties.To avoid the problem where fusion filters may give up some advantages of UFIR filters by fusing based on noise statistics,we attempt to find a way to fuse without using noise statistics.The fusion filtering algorithm is derived using the influence function that provides a quantified measure for disturbances on the resulting filtering outputs and is termed as an influence finite impulse response(IFIR)filter.The main advantage of the proposed method is that the noise statistics of process noise and measurement noise are no longer required in the fusion process,showing that a critical feature of the UFIR filter is inherited.One numerical example and a practice-oriented case are given to illustrate the effectiveness of the proposed method.It is shown that the IFIR filter has adaptive performance and can automatically switch from the Kalman estimate to the UFIR estimates according to operating conditions.Moreover,the proposed method can reduce the effects of optimal horizon length on the UFIR estimate and can give the state estimates of best accuracy among all the compared methods.     

Filtering images contaminated with pep and salt type noise with pulse-coupled neural networks

As a typical type of noise in many image related applications, pep and salt (PAS) type noise reduces the quality of the image seriously. Median filter and its variations[1—3] are the earliest filtering algorithms for filtering this type of noise, with the drawback that filtering is performed to all of the pixels in the image, which leads to a severe blur of the filtered image. To tackle this problem, Sun and Neuval[4], and Florencio et al.[5] pre-sented a switch median filtering method whic…     

Robust graph coloring based on the matrix semi-tensor product with application to examination timetabling

This paper investigates the robust graph coloring problem with application to a kind of examination timetabling by using the matrix semi-tensor product, and presents a number of new results and algorithms. First, using the matrix semi-tensor product, the robust graph coloring is expressed into a kind of optimization problem taking in an algebraic form of matrices, based on which an algorithm is designed to find all the most robust coloring schemes for any simple graph. Second, an equivalent problem of robust graph coloring is studied, and a necessary and sufficient condition is proposed, from which a new algorithm to find all the most robust coloring schemes is established. Third, a kind of examination timetabling is discussed by using the obtained results, and a method to design a practicable timetabling scheme is presented. Finally, the effectiveness of the results/algorithms presented in this paper is shown by two illustrative examples.     

Remove Irrelevant Atomic Formulas for Timed Automaton Model Checking

Most of the timed automata reachability analysis algorithms in the literature explore the state spaces by enumeration of symbolic states, which use time constraints to represent a set of concrete states. A time constraint is a conjunction of atomic formulas which bound the differences of clock values. In this paper, it is shown that some atomic formulas of symbolic states generated by the algorithms can be removed to improve the model checking time- and space-efficiency. Such atomic formulas are called as irrelevant atomic formulas. A method is also presented to detect irrelevant formulas based on the test-reset information about clock variables. An optimized model-checking algorithm is designed based on these techniques. The case studies show that the techniques presented in this paper significantly improve the space- and time-efficiency of reachability analysis.     

Studies on algorithms for self-stabilizing communication protocols

In this paper the algorithms for self-stabilizing communication protocols are studied.First some concepts and a formal method for describing the proposed algorithms are described,then an improved algorithm for achieving global states is presented.The study shows that the improved algorithm can be applied to obtain the global states in the case of a loss of cooperation of the different processes in th protocol,which can be used as a recovery point that will be used by the following recovery procdure.Thus,the improved algorithm can be used to self-stabilize a communication protocol.Meanwhile,a recovery algorithm for selastabilizing communication protocols is presented.After a failure is detected,all processes can eventually know the error.The recovery algorithm uses the contextual information exchanged during the progress of the protocol and recorded on the stable memory.The proof of correctness and analysis of complexity for these algorithms have been made.The availability and efficiency of the algorithms have been verified by illustrating the example protocols.Finally,some conclusions and remarks are given.     

离散时间系统量化动态输出反馈的H∞控制

The problem of quantized dynamic output feedback H_∞control for discrete-time linear time-invariant(LTI)systems is investigated in this paper.The quantizer considered is dynamic and composed of an adjustable"zoom"parameter and a static quantizer.Static quantizer ranges are of practical significance and are fully considered.First,taking quantization errors into account, a quantized control strategy is dependent not only on the controller states but also on the system measurement outputs,which is proposed such that the quantized closed-loop system is asymptotically stable and with a prescribed H_∞performance bound.Then, on the basis of this result,an iterative LMI-based optimization algorithm is developed to optimize the static quantizer ranges to meet H_∞performance requirements for closed-loop systems.An example is presented to illustrate the effectiveness of the proposed method.     

Design and implementation of vision-based transport for a mobile robot via Kalman filter

A vision-based scheme for object recognition and transport with a mobile robot is proposed in this paper. First, camera calibration is experimentally performed with Zhenyou Zhang’s method, and a distance measurement method with the monocular camera is presented and tested. Second, Kalman filtering algorithm is used to predict the movement of a target with HSI model as the input and the seed filling algorithm as the image segmentation approach. Finally, the motion control of the pan-tilt camera and mobile robot is designed to fulfill the tracking and transport task. The experiment results demonstrate the robust object recognition and fast tracking capabilities of the proposed scheme.     

一种基于扩展交替投影神经网络的弱信号分离方法研究

Aiming at a kind of specific situation encountered in practice, the paper proposes a weak-signal separation algorithm based on Extended Alternating Projection Neural Networks (EAPNN) by combining the time-domain features of the signal with the frequency-domain features of the signal and taking advantage of conclusions on EAPNN. Simulation results demonstrate that the algorithm is effective and that the EAPNN-based signal separationalgorithm is better than the RLS-based signal separation algorithm. Although the EAPNN-based algorithm isdesigned for the specific situation, it is also applicable to the other situations and a basic frame of the EAPNN-basedsignal separation is presented. Owing to adopting neural network structure, the EAPNN-based algorithm is prone toparallel computation and VLSI design, consequently can satisfy real-time processing needs.     

A new information fusion white noise deconvolution estimator

The white noise deconvolution or input white noise estimation problem has important applications in oil seismic exploration, communication and signal processing. By the modern time series analysis method, based on the autoregressive moving average （ARMA） innovation model, a new information fusion white noise deconvolution estimator is presented for the general multisensor systems with different local dynamic models and correlated noises. It can handle the input white noise fused filtering, prediction and smoothing problems, and it is applicable to systems with colored measurement noises. It is locally optimal, and is globally suboptimal. The accuracy of the fuser is higher than that of each local white noise estimator. In order to compute the optimal weights, the formula computing the local estimation error cross-covariances is given. A Monte Carlo simulation example for the system with Bernoulli-Gaussian input white noise shows the effectiveness and performances.     

基于内容的空域图像水印算法研究

In this paper,a novel space domain watermarking scheme based on image content is presented.The proposed method can use the human visual system(HVS) to adaptively embed a watermark in an image by decomposing the host image,selecting streak blocks,modifying the intensities of the block.Experimental results show that the watermarking scheme proposed in this paper is invisible and robust to cropping,and also has good robustness to compression,filtering,noise adding,image enhancement,and rnosaie.     

Fault detection and identification based on combining logic and model in a wall-climbing robot

A combined logic- and model-based approach to fault detection and identification （FDI） in a suction foot control system of a wall-climbing robot is presented in this paper. For the control system, some fault models are derived by kinematics analysis. Moreover, the logic relations of the system states are known in advance. First, a fault tree is used to analyze the system by evaluating the basic events （elementary causes）, which can lead to a root event （a particular fault）. Then, a multiple-model adaptive estimation algorithm is used to detect and identify the model-known faults. Finally, based on the system states of the robot and the results of the estimation, the model-unknown faults are also identified using logical reasoning. Experiments show that the proposed approach based on the combination of logical reasoning and model estimating is efficient in the FDI of the robot.     

A Lyapunov characterization of robust policy optimization

In this paper, we study the robustness property of policy optimization (particularly Gauss–Newton gradient descent algorithm which is equivalent to the policy iteration in reinforcement learning) subject to noise at each iteration. By invoking the concept of input-to-state stability and utilizing Lyapunov’s direct method, it is shown that, if the noise is sufficiently small, the policy iteration algorithm converges to a small neighborhood of the optimal solution even in the presence of noise at each iteration. Explicit expressions of the upperbound on the noise and the size of the neighborhood to which the policies ultimately converge are provided. Based onWillems’ fundamental lemma, a learning-based policy iteration algorithm is proposed. The persistent excitation condition can be readily guaranteed by checking the rank of the Hankel matrix related to an exploration signal. The robustness of the learning-based policy iteration to measurement noise and unknown system disturbances is theoretically demonstrated by the input-to-state stability of the policy iteration. Several numerical simulations are conducted to demonstrate the efficacy of the proposed method.     

Automatic target detection by optimal morphological filters

It is widely accepted that the design of morphological filters,which are optimal in some sense,is a difficult task.In this paper a novel method for optimal learning of morphological filtering parameters(Genetic training algorithm for morphological filters,GTAMF)is presented.GTAMF adopts new crossover and mutation operators called the curved cylinder crossover and master-slave mutaition to achieve optimal filtering parameters in a global searching,Experimental results show that this method is practical,easy to extend,and markedly improves the performances of morphoological filters.The operation of a morphological filter can be divided into two basic problems including morphological operation and structuring element (SE)Selection.The rules for morphological operations are predefined so that the filter‘s properties depend merely on the selection of SE.By means of adaptive optimization training,structureing elements possess the shape and structural characteristics of image targets,and give specific information to SE.Morphological filters formed in this way become certainly intelligent and can provide good filtering results and robust adaptability to image targets with clutter background.     

Joint multiple parameters estimation for coherent chirp signals using vector sensor array

In this paper, an algorithm is proposed to estimate starting frequency （SF）, chirp rate （CR）, 2-D direction-of-arrivals （DOA） and polarization of coherent chirp signals with vector sensor arrays. The fractional Fourier transformation （FRFT） is used to estimate SF and CR of chirp signals in this method. And a new correlation matrix is reconstructed to suppress the noise. The property of the vector sensor array is employed to solve the problem of insufficient rank from signal coherence. The L-shaped uniform array of expend aperture is used to improve the precision of es- timation, and the method of solving the ambiguity of angle under the condition of coherent signals is presented. The performance of this algorithm is compared with that of spatial smoothing method to verify the efficacy of this approach.     

带噪声统计估计器的Unscented卡尔曼滤波器设计

针对传统Unscented卡尔曼滤波器(UKF)在噪声先验统计未知或不准确时滤波精度下降甚至发散的问题,基于极大后验(MAP)估计原理,设计了一种带噪声统计估计器的UKF.该UKF滤波算法在进行状态估计的同时,能实时估计和修正噪声均值和协方差.相比于传统UKF,所提出的UKF具有应对噪声统计变化的自适应能力.仿真结果表明了该UKF滤波算法的有效性.

Abstract：

For the problem that the accuray of the conventional UKF declines and further diverges when the prior noise statistic is unknown or inaccurate, an unscented Kalman filter (UKF) with noise statistic estimator is designed.This UKF filtering algorithm based on maximum a posterior (MAP) estimation can estimate and correct the mean and covariance of the noise in real time while it estimates the states.The proposed UKF has the adaptive capability of dealing with variable noise statistic.The simulation results show the effectiveness of the proposed UKF filtering algorithm.