Star pattern recognition based on features invariant under rotation

A star pattern recognition algorithm is proposed on the basis of features invariant under rotation. Guidance star identification via the algorithm is performed on star images captured by star sensor and simulated images. The results indicate that the proposed method presents better robustness against position and magnitude noise than conventional ones and eliminates rotation procedure and avoids the influence caused by grid size choice. The database feature storage for each pattern consists of simply four floating point numbers.     

Decentralised cooperative attitude tracking using modified Rodriguez parameters based on relative attitude information

In this article, the problem of decentralised cooperative attitude tracking based on relative attitude information is considered in the context of deep space spacecraft formation flying. We use modified Rodriguez parameters for attitude representation due to its nonreduction. We first present a distributed sliding-mode estimator and a cooperative attitude tracking control law such that the attitudes and angular velocities of all spacecraft track their references. Then we extend the result to three different cases. In the first case, we use a passivity approach to derive a control law without angular velocity measurements, which is of significance for applications with low-cost configurations of spacecraft. In the second case, to relax the special connectivity condition given in the original control design, we obtain a locally asymptotical stability condition for any undirected connected communication topology. In the third case, we consider cooperative attitude tracking in the presence of a dynamic communication topology. Simulation results are presented to validate the effectiveness of these control laws.     

Nonlinear internal model based attitude tracking and disturbance rejection

In this paper, we consider the attitude tracking and disturbance rejection problem of rigid spacecraft, which is subject to some disturbances generated by nonlinear exosystems. A new nonlinear internal model is designed to manipulate the uncertainties caused by external disturbances, which includes the linear internal model as a special case. A numerical example is given to illustrate the theoretical result presented in the paper. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Deadzone compensation in discrete time using adaptive fuzzy logic

A fuzzy logic (FL) compensator is designed for control of nonlinear discrete-time systems with input deadzone. The classification property of FL systems makes them a natural candidate for the rejection of errors induced by the deadzone, which has regions in which it behaves differently. A discrete-time tuning algorithm is given for the FL parameters so that the deadzone compensation scheme becomes adaptive, guaranteeing bounded tracking errors and parameter estimates. A rigorous proof of stability and performance is given and a simulation example verifies performance. Unlike standard discrete-time adaptive control techniques, no certainty equivalence assumption is needed     

Vector quantization in DCT domain using fuzzy possibilistic c-means based on penalized and compensated constraints

In this paper, fuzzy possibilistic c-means (FPCM) approach based on penalized and compensated constraints are proposed to vector quantization (VQ) in discrete cosine transform (DCT) for image compression. These approaches are named penalized fuzzy possibilistic c-means (PFPCM) and compensated fuzzy possibilistic c-means (CFPCM). The main purpose is to modify the FPCM strategy with penalized or compensated constraints so that the cluster centroids can be updated with penalized or compensated terms iteratively in order to find near-global solution in optimal problem. The information transformed by DCT was separated into DC and AC coefficients. Then, the AC coefficients are trained by using the proposed methods to generate better codebook based on VQ. The compression performances using the proposed approaches are compared with FPCM and conventional VQ method. From the experimental results, the promising performances can be obtained using the proposed approaches.     

全局自适应神经网络跟踪控制及逼近域确定

针对自适应神经网络跟踪控制问题,提出一种确定逼近域的方法.采用参考信号取代未知非线性函数中的系统输出,神经网络用于逼近以参考信号为输入的未知不确定项.可以利用参考信号的界预先确定神经网络逼近域,再采用自适应鲁棒方法处理由于函数输入置换所引起的另一类不确定项.所得到的闭环系统是全局稳定的.仿真实例说明了该控制方法的有效性.     

Robust image hashing using non-uniform sampling in discrete Fourier domain

This paper proposes a robust image hashing method in discrete Fourier domain that can be applied in such fields as image authentication and retrieval. In the pre-processing stage, image resizing and total variation based filtering are first used to regularize the input image. Then the secondary image is obtained by the rotation projection, and the robust frequency feature is extracted from the secondary image after discrete Fourier transform. More sampling points are chosen from the low- and middle-frequency component to represent the salient content of the image effectively, which is achieved by the non-uniform sampling. Finally, the intermediate sampling feature vectors are scrambled and quantized to produce the resulting binary hash securely. The security of the method depends entirely on the secret key. Experiments are conducted to show that the present method has satisfactory robustness against perceptual content-preserving manipulations and has also very low probability for collision of the hashes of distinct images.     

Improved position tracking performance of a pneumatic actuator using a fuzzy logic controller with velocity,system lag and friction compensation

In this paper a new control algorithm for position tracking of sinusoidal trajectories is introduced to improve the accuracy of a pneumatic actuator. A proportional valve is used to control the position of a rodded cylinder through a fuzzy logic (FL) approach with added velocity feedback (VF), f system lag compensator (LC) and friction compensator (FC). Parameters of the FL controller and the VF and FC compensators are tuned off-line manually. Parameters for the LC compensator are obtained by an on-line algorithm. Experimental results show that the proposed FL+VF+LC+FC controller is able to significantly improve tracking performance. The contribution of each component towards overall performance is carefully documented. The results compare favourably with the performance obtained with more advanced model-based algorithms as reported in the literature. The simpler non-model based nature of the controller together with the careful documentation of the algorithm and system performance, means that other researchers can readily evaluate the controller for their own applications.

     

Spacecraft attitude determination and control: Quaternion based method

In this review, we discuss in detail the quaternion based methods for spacecraft attitude determination and control. We summarize some recent developments on this research area. We start with some brief but complete discussions on the theory of quaternion which will be sufficient for the discussion in the remaining part of the review. We review the progress of quaternion based attitude determination which has been well recognized and achieved great success by using Newton’s method. We also present a different and more elegant treatment on an analytic solution to Wahba’s problem. For quaternion based control system design, we focus on some recently developed reduced quaternion models which use only vector component of the quaternion in the state space models. We discuss some new design method that has the following features: (a) it has an analytic solution of LQR, and (b) the designed system reduces disturbance effect, global stabilizes the nonlinear spacecraft system, and is robust to the modeling uncertainty. The presentation of the review is self-complete. It includes all the background information that is needed to understand the development involving the system modeling, the attitude determination, and the attitude control system design methods.     

基于自适应模糊与输入输出线性化的卫星姿态控制

将自适应模糊控制与输入输出线性化控制相结合，构成混合控制器，并将其应用于挠性卫星的姿态机动控制．给出了卫星姿态控制器的基本形式，分析了控制器参数的选取准则．在线调节自适应模糊控制器的参数，以补偿不确定性卫星的姿态跟踪误差．仿真结果表明，该控制算法通过在线学习能有效地克服挠性卫星的不确定性，具有较强的鲁棒性，从而有效地提高了挠性卫星的姿态控制精度．     

Competitiveness and compensation in decision making: A fuzzy set based interpretation

The multiple objective decision problem is investigated—particularly the question of the form of the decision function. Different models are investigated. The relationship of competitiveness and compensation to the form of the models is studied. Differing methods of including distinctions between objectives are discussed.     

Novel target segmentation and tracking based on fuzzy membership distribution for vision-based target tracking system

One of the basic processes of a vision-based target tracking system is the detection process that separates an object from the background in a given image. A novel target detection technique for suppression of the background clutter is presented that uses a predicted point that is estimated from a tracking filter. For every pixel, the three-dimensional feature that is composed of the x-position, the y-position and the gray level of its position is used for evaluating the membership value that describes the probability of whether the pixel belongs to the target or to the background. These membership values are transformed into the membership level histogram. We suggest an asymmetric Laplacian model for the membership distribution of the background pixel and determine the optimal membership value for detecting the target region using the likelihood criterion. The proposed technique is applied to several infra-red image sequences and CCD image sequences to test segmentation and tracking. The feasibility of the proposed method is verified through comparison of the experimental results with the other techniques.     

Novel method and system for pattern recognition and processing using data encoded as Fourier series in Fourier space

A method and system for pattern recognition and processing is reported that has a data structure and theoretical basis that are unique. This novel approach anticipates the signal processing action of an ensemble of neurons as a unit and intends to simulate aspects of brain that give rise to capabilities such as intelligence, pattern recognition, and reasoning that have not been reproduced with past approaches such as neural networks that are based individual simulated “neuronal units.” Information representative of physical characteristics or representations of physical characteristics is transformed into a Fourier series in Fourier space within an input context of the physical characteristics that is encoded in time as delays corresponding to modulation of the Fourier series at corresponding frequencies. Associations are formed between Fourier series by filtering the Fourier series and by using a spectral similarity between the filtered Fourier series to determine the association based on Poissonian probability. The associated Fourier series are added to form strings of Fourier series. Each string is ordered by filtering it with multiple selected filters to form multiple time order formatted subset Fourier series, and by establishing the order through associations with one or more initially ordered strings to form an ordered string. Associations are formed between the ordered strings to form complex ordered strings that relate similar items of interest. The components of the system based on the algorithm are active based on probability using weighting factors based on activation rates.     

Multitarget bearings-only tracking using fuzzy clustering technique and Gaussian particle filter

In this paper, a novel multitarget bearings-only tracking algorithm that combines the fuzzy clustering data association technique together with a Gaussian particle filter (GPF) is presented. Firstly, to deal with the data association problem that arises due to the uncertainty of the measurements, the fuzzy clustering method with the maximum entropy principle is utilized, which eliminates those invalid measurements. Secondly, this paper employs GPF to update each target state independently, since it has a much-improved performance and versatility over other Gaussian filters, especially when nontrivial nonlinearities are present. Moreover, in the multisensor scenario, a statistic test method based on the cotangent values of bearings is proposed, for associating the target bearing data observed at each sensor. Simulation results demonstrate the effectiveness of the algorithm.     

Output tracking and regulation of nonlinear system based onTakagi-Sugeno fuzzy model

On the basis of the Takagi-Sugeno (TS) fuzzy model, this paper discusses in detail the following three problems: (1) output tracking of the nonlinear system; (2) output regulation of the nonlinear system via a state feedback; (3) output regulation of the nonlinear system via a error feedback. Numerical simulations are given to illustrate the soundness of these results and the effectiveness of the new methodology solving the output tracking and regulation problem of the nonlinear system.     

全局运动补偿的动态背景下运动轨迹跟踪算法

针对动态背景下,一般跟踪算法存在着被动跟踪的滞后或偏移的问题,提出了一种结合Kalman滤波器的Mean-Shift跟踪算法.对运动矢量进行预处理,得到一个平稳更能反映运动信息的矢量场;利用Mean-Shift搜索算法精确地确定对象位置;此基础上,利用Kalman滤波器算法进行运动估计预测,来确定运动的轨迹.实验表明:与现有的方法相比,该方法可从复杂场景中更准确地对运动对象进行轨迹的跟踪.     

Distributed observer‐based leader‐follower attitude consensus control for multiple rigid bodies using rotation matrices

This paper addresses the distributed observer‐based leader‐follower attitude consensus control problem for multiple rigid bodies. An intrinsic distributed observer is proposed for each follower to estimate the leader's trajectory, which is only available to a subset of followers. The proposed observer can guarantee that the estimated attitude evolves on rotation matrices all the time, and it provides us with a simple way to design the attitude consensus control law. The dynamics of rigid bodies and distributed observer are both modeled directly on rotation matrices, so that the singularity and ambiguity can be avoided. Furthermore, adopting the idea of disturbance observer on vector space, a gyro bias observer on the rotation matrices is proposed. Based on the distributed observer, three types of attitude consensus control law are proposed, which are respectively on the basis of full‐state, biased angular velocity, and external disturbance combined with biased angular velocity. Finally, the SimMechanics experiments are provided to illustrate effectiveness of the proposed theoretical results.     

基于熵和风险态度的二型模糊多属性决策方法

针对属性权重信息完全未知的二型模糊多属性决策问题,提出了一种基于二型模糊熵和决策者风险态度的决策方法。首先,为了准确测度二型模糊集（T2FS）的不确定性,通过引入模糊因子和犹豫因子建立了二型模糊熵的公理化准则,并基于距离测度给出了对应的计算公式。其次,为了减少整体不确定信息对决策结果的影响,结合二型模糊熵构建非线性规划模型来确定属性权重。同时,将决策者的风险态度引入二型模糊信息的得分函数中并给出具体的决策步骤。最后,通过实例分析验证了该决策方法的可行性,并与现有文献对比发现该决策方法更具有灵活性。     

Compressed sensing SAR imaging based on sparse representation in fractional Fourier domain

Compressed sensing(CS)is a new technique of utilizing a priori knowledge on sparsity of data in a certain domain for minimizing necessary number of measurements.Based on this idea,this paper proposes a novel synthetic aperture radar(SAR)imaging approach by exploiting sparseness of echo data in the fractional Fourier domain.The effectiveness and robustness of the approach are assessed by some numerical experiments under various noisy conditions and different measurement matrices.Experimental results have shown that,the obtained images by using the CS technique depend on measurement matrix and have higher output signal to noise ratio than traditional pulse compression technique.Finally simulated and real data are also processed and the achieved results show that the proposed approach is capable of reconstructing the image of targets and effectively suppressing noise.