Marginalized particle filter for spacecraft attitude estimation from vector measurements

An algorithm based on the marginalized particle filters （MPF） is given in details in this paper to solve the spacecraft attitude estimation problem： attitude and gyro bias estimation using the biased gyro and vector observations. In this algorithm, by marginalizing out the state appearing linearly in the spacecraft model, the Kalman filter is associated with each particle in order to reduce the size of the state space and computational burden. The distribution of attitude vector is approximated by a set of particles and estimated using particle filter, while the estimation of gyro bias is obtained for each one of the attitude particles by applying the Kalman filter. The efficiency of this modified MPF estimator is verified through numerical simulation of a fully actuated rigid body. For comparison, unscented Kalman filter （UKF） is also used to gauge the performance of MPE The results presented in this paper clearly derfionstrate that the MPF is superior to UKF in coping with the nonlinear model.     

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.     

Simulation-based Optimal Design of α-β-γ-δ Filter

The existing third-order tracker known as α-β-γ filter has been used for target tracking and predicting for years. The filter can track the target's position and velocity, but not the acceleration. To extend its capability, a new fourth-order target tracker called α-β-γ-δ filter is proposed. The main objective of this study was to find the optimal set of filter parameters that leads to minimum position tracking errors. The tracking errors between using the α-β-γ filter and the α-β-γ-δ filter are compared. As a result, the new filter exhibits significant improvement in position tracking accuracy over the existing third-order filter, but at the expense of computational time in search of the optimal filter. To reduce the computational time, a simulation-based optimization technique via Taguchi method is introduced.     

Optimal design and verification of temporal and spatial filters using second-order cone programming approach

11 Introduction The filters are widely used in many applications of signal processing. Filter design is an important research problem in many diverse application areas. The filters we usually refer to are temporal filters, which pass the frequency components of interest and attenuate the others. A spatial filter passes the signal radiating from a specific location and attenuates signals from other locations. Beamformer that widely used in radar, sonar,and wireless communications is a kind of …     

HTS filter subsystem for future mobile communication system

A high-temperature superconducting （HTS） filter subsystem consisting of a 10-pole HTS filter with group delay self-equalization, a cryogenic low noise amplifier （LNA）, and a cooling device is presented in this paper. The HTS filter has a 0.51% fractional bandwidth at 1955 MHz, simulated in Sonnet, and is fabricated using double-sided TI2Ba2CaCu208 films on 0.5 mm thick LaAIO3 substrate. Measured responses show that the minimum insertion loss of the filter is 0.16 dB, and the out-of-band rejection is better than 75 dB. The slope is 27 dB MHz^-1 at the low band edge and 22 dB MHz-1 at the upper band edge. The HTS filter subsystem has a gain of 19.3 dB and a noise figure （NF） of 0.8 dB. Furthermore, the phase distortion of HTS filters is considered, and an efficient design approach for self-equalized HTS filters is proposed.     

Robust RBPF for mobile robot using panoramic vision

We present a novel vision-based approach to self-localization that uses an improved scheme to integrate feature-based matching of panoramic images with a Rao-Blackwellized Particle Filter （RBPF） for mobile robot Simultaneous Localization and Mapping （SLAM）. The matching for SIFT multi-dimension features is implemented with a KD-tree which introduce the Mahalanobis distance instead of the Euclidean distance for matching features. The particle filter is combined with Gaussian Mixture Unscented Particle Filters （GMUPF） to for initializing landmarks and a Single-Cluster Graph-Partitioning algorithm for outlier rejection. The landmark position estimation and update is also implemented through GMUPF by which a single update step from moving and sensing can be done and the change to the map certainty can be done in constant time. Experiment results on real robot in our indoor environment show the advantages of our methods over previous approaches.     

Robust fault detection for discrete-time Markovian jump systems with mode-dependent time-delays

This paper investigates a fault detection problem for a class of discrete-time Markovian jump systems with norm-bounded uncertainties and mode-dependent time-delays. Attention is focused on constructing the residual generator based on the filter of which its parameters matrices are dependent on the system mode, that is, the fault detection filter is a Markovian jump system as well. The design of fault detection filter is reduced to H-infinity filtering problem by using H-infinity control theory, which can guarantee the difference between the residual and the fault (or, more generally weighted fault) as small as possible in the context of enhancing the robustness of residual to modeling errors, control inputs and unknown inputs. Sufficient condition for the existence of the above filters is established by means of linear matrix inequalities, which can be readily solved by using standard numerical software. A numerical example is given to illustrate the feasibility of the proposed method.     

Adaptive detectors in the Krylov subspace

The validity of the application of the Krylov subspace techniques in adaptive filtering and detection is investigated. A new verification of the equivalence of two well-known methods in the Krylov subspace, namely the multistage Wiener filters （MWF） and the auxiliary-vector filtering （AVF）, is given in this paper. The MWF and AVF are incorporated into two well-known detectors, namely, the adaptive matched filter （AMF） and Kelly＇s generalized likelihood ratio test （CLRT） including their diagonally loaded versions, which form new detectors. Compared to the conventional AMF, CLRT, and their diagonally loaded versions as well as the reduced-rank AMF and GLRT, the probabilities of detection （PDs） of the new detectors are improved especially when the sample support is low. More importantly, the new detectors are robust of the rank selection of the clutter subspace compared to the reduced-rank AMF and GLRT. These new detectors all possess asymptotic constant false alarm rate （CFAR） property.     

Exponential nonlinear observer based on the differential state-dependent Riccati equation

This paper presents a novel nonlinear continuous-time observer based on the differential state-dependent Riccati equation(SDRE) filter with guaranteed exponential stability.Although impressive results have rapidly emerged from the use of SDRE designs for observers and filters,the underlying theory is yet scant and there remain many unanswered questions such as stability and convergence.In this paper,Lyapunov stability analysis is utilized in order to obtain the required conditions for exponential stability of the estimation error dynamics.We prove that under specific conditions,the proposed observer is at least locally exponentially stable.Moreover,a new definition of a detectable state-dependent factorization is introduced,and a close relation between the uniform detectability of the nonlinear system and the boundedness property of the state-dependent differential Riccati equation is established.Furthermore,through a simulation study of a second order nonlinear model,which satisfies the stability conditions,the promising performance of the proposed observer is demonstrated.Finally,in order to examine the effectiveness of the proposed method,it is applied to the highly nonlinear flux and angular velocity estimation problem for induction machines.The simulation results verify how effectively this modification can increase the region of attraction and the observer error decay rate.     

H_∞ Filter Design for Discrete-time Systems with Missing Measurements

For packet-based transmission of data over a network, or temporary sensor failure, etc., data samples may be missing in the measured signals. This paper deals with the problem of H∞ filter design for linear discrete-time systems with missing measurements. The missing measurements will happen at any sample time, and the probability of the occurrence of missing data is assumed to be known. The main purpose is to obtain both full-and reduced-order filters such that the filter error systems are exponentially mean-square stable and guarantee a prescribed H∞ performance in terms of linear matrix inequality (LMI). A numerical example is provided to demonstrate the validity of the proposed design approach.     

Improving Software Quality Prediction by Noise Filtering Techniques

Accuracy of machine learners is affected by quality of the data the learners are induced on. In this paper, quality of the training dataset is improved by removing instances detected as noisy by the Partitioning Filter. The fit dataset is first split into subsets, and different base learners are induced on each of these splits. The predictions are combined in such a way that an instance is identified as noisy if it is misclassified by a certain number of base learners. Two versions of the Partitioning Filter are used： Multiple-Partitioning Filter and Iterative-Partitioning Filter. The number of instances removed by the filters is tuned by the voting scheme of the filter and the number of iterations. The primary aim of this study is to compare the predictive performances of the final models built on the filtered and the un-filtered training datasets. A case study of software measurement data of a high assurance software project is performed. It is shown that predictive performances of models built on the filtered fit datasets and evaluated on a noisy test dataset are generally better than those built on the noisy （un-filtered） fit dataset. However, predictive performance based on certain aggressive filters is affected by presence of noise in the evaluation dataset.     

Adaptive output feedback control for nonlinear time-delay systems using neural network

This paper extends the adaptive neural network （NN） control approaches to a class of unknown output feedback nonlinear time-delay systems. An adaptive output feedback NN tracking controller is designed by backstepping technique. NNs are used to approximate unknown functions dependent on time delay, Delay-dependent filters are introduced for state estimation. The domination method is used to deal with the smooth time-delay basis functions. The adaptive bounding technique is employed to estimate the upper bound of the NN approximation errors. Based on Lyapunov- Krasovskii functional, the semi-global uniform ultimate boundedness of all the signals in the closed-loop system is proved, The feasibility is investigated by two illustrative simulation examples.     

Optimal State Estimation and Fault Diagnosis for a Class of Nonlinear Systems

This study proposes a scheme for state estimation and,consequently,fault diagnosis in nonlinear systems.Initially,an optimal nonlinear observer is designed for nonlinear systems subject to an actuator or plant fault.By utilizing Lyapunov's direct method,the observer is proved to be optimal with respect to a performance function,including the magnitude of the observer gain and the convergence time.The observer gain is obtained by using approximation of Hamilton-Jacobi-Bellman(HJB)equation.The approximation is determined via an online trained neural network(NN).Next a class of affine nonlinear systems is considered which is subject to unknown disturbances in addition to fault signals.In this case,for each fault the original system is transformed to a new form in which the proposed optimal observer can be applied for state estimation and fault detection and isolation(FDI).Simulation results of a singlelink flexible joint robot(SLFJR)electric drive system show the effectiveness of the proposed methodology.     

Nonlinear disturbance attenuation control for four-leg active power filter based on voltage source inverter

Active power filter （APF） based on voltage source inverter （VSI） is one of the important measures for handling the power quality problem. Mathematically, the APF model in a power grid is a typical nonlinear one. The idea of passivity is a powerful tool to study the stabilization of such a nonlinear system. In this paper, a state-space model of the four-leg APF is derived, based on which a new H-infinity controller for current tracking is proposed from the passivity point of view. It can achieve not only asymptotic tracking, but also disturbance attenuation in the sense of L2-gain. Subsequently, a sufficient condition to guarantee the boundedness and desired mean of the DC voltage is also given. This straightforward condition is consistent with the power-balancing law of electrical circuits. Simulations performed on PSCAD platform verify the validity of the new approach.     

An efficient visual tracking method for multiple moving targets

An efficient algorithm of the edge detection according to integrating the edge gradient with the average filter is proposed, which can significantly reduce sensitivity of the background subtraction method to noise and illumination. Taking into account the features of the target such as color, size, etc., a new modified Nearest Neighbor （NN） algorithm for data association using the target features is designed. A designed Interacting Multiple Model （IMM） filter is utilized to track the maneuvering target motion, i.e. the feature point （called the centroid of the target） motion of the target. The algorithms are validated via an example with natural video sequences. The results show the algorithms are performances and validity for visual tracking. In complex environment, the algorithm can still work well.     

Discrete-time approximation of Wonhain filters

A continuous-rime finite-state Markov chain observed in white noise is considered. The well-known result of Wonham filter provides a formula for obtaining posterior probabilities. Although the filter is of finite dimension, numerical schemes are needed in applications because of the nonlinearity and because the observations are frequently collected in discrete moments. In this work, we develop approximation schemes of Wonham filters by constructing discrete-time recursive algorithms. We prove the convergence of the algorithm by weak convergence method and martingale averaging techniques. Numerical experiments are also famished to demonstrate the performance of our algorithms.     

A Neural Network Approach for Designing 2-D FIR Filters with Arbitrary Magnitude Responses

In this paper, a Neural Network Approach （NNA） is presented for the design of two-dimensional （2-D） Finite-Impulse Response （FIR） filters with arbitrary magnitude responses. Its main goal is to minimize the frequency-domain error function. To illustrate the feasibility of the NNA, a Back.Propagation Neural Network （BPNN） model is chosen, and the convergence and stability of the BPNN are studied. The method avoids matrix inversion operation, and makes a very fast calculation of the filter＇s coefficients possible when the learning rate of the BPNN is selected suitably. The novel feature of the proposed approximation approach is that it can be used to design any kind of 2-D linear-phase FIR filters without any symmetry constraint on their magnitude-frequency responses. Several optimal design examples are given to illustrate the effectiveness of the proposed approach.     

The Grade Difference Format and MGM^p （1,n） Optimization Model

There are some applications that need to use the gray system techniques in distributed computing, in the basis of the grade difference format, MGMp （1, n） model is proposed and some precision grade difference formats are obtained. Their relations and the mathematics＇ mechanism are discussed. The error will be interjected to them. In the cases of absolute and comparative error, the gray model, which is a new gray GMGp （1,n） model, is expounded and discussed. The models have nice anti-interference; the example manifests that the model has a good effect.     

A functional presentation of Pi calculus

From the very beginning process algebra introduced the dichotomy between channels and processes. This dichotomy prevails in all present process calculi. The situation is in contrast to that withlambda calculus which has only one class of entities-the lambda terms. We introduce in this papera process calculus called Lamp in which channels are process names. The language is more uniform than existing process calculi in two aspects-. First it has a unified treatment of channels and processes.There is only one class of syntactical entities-processes. Second it has a unified presentation ofboth first order and higher order process calculi. The language is functional in the sense that lambda calculus is functional. Two bisimulation equivalences, barbed and closed bisimilarities, are proved to coincide.A natural translation from Pi calculus to Lamp is shown to preserve both operational and algebraic semantics. The relationship between lazy lambda calculus and Lamp is discussed.     

Fault detection for a class of impulsive switched systems

The problem of fault detection for a class of nonlinear impulsive switched systems is investigated in this paper. Fault detection filters are designed such that the augmented systems are stable, and the residual error signal generated by the filters guarantees the H∞ performance for disturbances and faults. Sufficient conditions for the design of fault detection(FD) filters are presented by linear matrix inequalities. Moreover, the filter gains are characterized according to a solution of a convex optimization. Finally, an example derived from a pulse-width-modulation-driven boost converter is given to illustrate the effectiveness of the FD design approach.