Adaptive cubature Kalman filter based on variational Bayesian inference under measurement uncertainty

A novel variational Bayesian inference based on adaptive cubature Kalman filter(VBACKF)algorithm is proposed for the problem of state estimation in a target tracking system with time-varying measurement noise and random measurement losses. Firstly, the Inverse-Wishart(IW) distribution is chosen to model the covariance matrix of time-varying measurement noise in the cubature Kalman filter framework. Secondly, the Bernoulli random variable is introduced as the judgement factor of the measurement l...     

Cubature Kalman probability hypothesis density filter based on multi-sensor consistency fusion①

The GM-PHD framework as recursion realization of PHD filter is extensively applied to multi-target tracking system .A new idea of improving the estimation precision of time-varying multi-target in non-linear system is proposed due to the advantage of computation efficiency in this paper .First, a novel cubature Kalman probability hypothesis density filter is designed for single sensor measure -ment system under the Gaussian mixture framework .Second , the consistency fusion strategy for multi-sensor measurement is proposed through constructing consistency matrix .Furthermore, to take the advantage of consistency fusion strategy , fused measurement is introduced in the update step of cubature Kalman probability hypothesis density filter to replace the single-sensor measurement .Then a cubature Kalman probability hypothesis density filter based on multi-sensor consistency fusion is proposed .Capabilily of the proposed algorithm is illustrated through simulation scenario of multi-sen-sor multi-target tracking .     

Two-stage prediction and update particle filtering algorithm based on particle weight optimization in multi-sensor observation

The reasonable measuring of particle weight and effective sampling of particle state are consid- ered as two important aspects to obtain better estimation precision in particle filter. Aiming at the comprehensive treatment of above problems, a novel two-stage prediction and update particle filte- ring algorithm based on particle weight optimization in multi-sensor observation is proposed. Firstly, combined with the construction of muhi-senor observation likelihood function and the weight fusion principle, a new particle weight optimization strategy in multi-sensor observation is presented, and the reliability and stability of particle weight are improved by decreasing weight variance. In addi- tion, according to the prediction and update mechanism of particle filter and unscented Kalman fil- ter, a new realization of particle filter with two-stage prediction and update is given. The filter gain containing the latest observation information is used to directly optimize state estimation in the frame- work, which avoids a large calculation amount and the lack of universality in proposal distribution optimization way. The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.     

A novel multi-sensor multiple model particle filter with correlated noises for maneuvering target tracking

Aiming at the effective realization of particle filter for maneuvering target tracking in multi-sensor measurements,a novel multi-sensor multiple model particle filtering algorithm with correlated noises is proposed.Combined with the kinetic evolution equation of target state,a multi-sensor multiple model particle filter is firstly constructed,which is also used as the basic framework of a new algorithm.In the new algorithm,in order to weaken the adverse influence from random measurement noises in the measuring process of particle weight,a weight optimization strategy is introduced to improve the reliability and stability of particle weight.In addition,considering the correlated noise existing in the practical engineering,a decoupling method of correlated noise is given by the rearrangement and transformation of the state transition equation and measurement equation.Since the weight optimization strategy and noise decoupling method adopt respectively the center fusion structure and the off-line way,it improves the adverse effect effectively on computational complexity for increasing state dimension and sensor number.Finally,the theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.     

Robust monocular SLAM towards motion disturbance

The standard extended Kalman filter-based simultaneously localization and mapping （EKF-SLAM） algorithm has a drawback that it could not handle the sudden motion caused by the motion disturbance. This prevents the SLAM system from real applications. Many techniques have been developed to make the system more robust to the motion disturbance. In this paper, we propose a robust monocular SLAM algorithm. First, when the motion model-based system failed to track the features, a KLT tracker will be activated for each feature. Second, the KLT tracked features are used to update the camera states. Third, the difference between the camera states and the predictions is used to adjust the input motion noise. Finally, we do the standard EKF-SLAM with the new input motion noise. In order to make the system more reliable, a joint compatibility branch and bound algorithm are used to check the outliers, and an IEKF filter is used to make the motion estimation smoother when the camera encounters sudden movement. The experiments are done on an image sequence caught by a shaking hand-held camera, which show that the proposed method is very robust to large motion disturbance.     

Research on control technology of elderly-assistant & walking-assistant robot based on tactile-slip sensation

In this paper,according to the old people’s physical characteristics and their technical requirements for comfort and mastery when operating the robot,a control approach driven by tactile and slip senses is investigated to control the elderly-assistant & walking-assistant robot. First,on the basis of the proposed driving control system program of tactile and slip,a detection system of tactile and slip senses are designed. Based on the tactile and slip feature representation and extraction,an improved classification and recognition method is proposed which combines K-nearest neighbor (KNN) algorithm and K-means algorithm. And then,a robot control system based on TMS320F2812 is designed in this paper,including its hardware and software design. Then,a moving control method including the fuzzy adaptive control algorithm is presented for the walking-assistant robot to realize some different moving properties. At last,by the experimental verification in the walking-assistant robot,the research results show that the tactile and slip senses detection and recognition method is effective,and the whole control system has good feasibility and adaptability.     

A Regularized Super Resolution Algorithm for Generalized Gaussian Noise

In this paper,an iterative regularized super resolution (SR) algorithm considering non-Gaussian noise is proposed.Based on the assumption of a generalized Gaussian distribution for the contaminating noise,an lp norm is adopted to measure the data fidelity term in the cost function.In the meantime,a regularization functional defined in terms of the desired high resolution (HR) image is employed,which allows for the simultaneous determination of its value and the partly reconstructed image at each iteration step.The convergence is thoroughly studied.Simulation results show the effectiveness of the proposed algorithm as well as its superiority to conventional SR methods.     

Application of ESPRIT in Broad Beam HF Ground Wave Radar Sea Surface Current Mapping

HF surface wave radar system OSMAR2000 is a broad-beam sea-state detecting radar.ESPRIT (Estimation of Signal Parameters via Rotational Invariance Technique) algorithm is proposed to apply in DOA (direction of arrival) determination of sea echoes.The algorithm of ESPRIT is briefly introduced first.Then discussions are made on the technique for application in the OSMAR2000 framework.Numerical simulation results are presented to demonstrate the feasibility of radial current mapping based on this method.The algorithm manifests significant performance and computational advantages compared with that of MUSIC.Data acquired by OSMAR2000 are processed to give radial current map and the synthesized vector currents are compared with the in-situ measurement with traditional means.The results show the validity of ESPRIT application in DOA determination for broad-beam radar.     

An in-pipe micro robot actuated by piezoelectric bimorphs

A novel in-pipe micro robot providing stable and accurate locomotion inside a tubular structure with diameters ranging from 16 mm to 18 mm is presented in this paper. Driven by impulsive voltages, deflections of the piezoelectric bimorphs are generated and then converted into translational locomotion by the principle of the Impact Drive Mechanism （IDM）. Theoretical analysis of the proposed system is performed based on a simplified mechanical model. Then dynamic simulations of the dynamic behavior are performed. Finally, an experiment is conducted to investigate the moving ability of this device. The results demonstrate that a maximum translational velocity of 3.5 mm/s can be obtained under an im- pulsive driving voltage with peak value at 50 V and frequency of 1100 Hz. Both theoretical analysis and experimental trials prove that the principle of IDM actuated by piezoelectric bimorphs is feasible and robust for achieving accurate locomotion of the micro robot in pipes with diameters of less than 20 mm.     

Adaptive Order Polynomial Fitting for Pulmonary Nodule Segmentation in Chest Radiograph

Segmentation of pulmonary nodules in chest radiographs is a particularly challenging task due to heavy noise and superposition of fibs, vessels, and other complicated anatomical structures in lung field. In this paper, an adaptive order polynomial fitting based ray- casting algorithm is proposed for pulmonary nodule segmentation in chest radiographs. Instead of detecting nodule edge points directly, the nodule intensity proffies are first fitted by using the polynomials with adaptively determined orders. Then, the edge positions are identified through analyzing the local minimum of the fitted curves. The performance of the proposed algorithm was evaluated over an image database with 148 nodule cases in chest radiographs that were collected from a variety of digital radiograph modalities. The preliminary results show the proposed algorithm can obtain a high rate of successful segmentations.     

Research on wavelet-based algorithm for image contrast enhancement

A novel wavelet-based algorithm for image enhancement is proposed in the paper. On the basis of multiscale analysis, the proposed algorithm solves efficiently the problem of noise over-enhancement, which commonly occurs in the traditional methods for contrast enhancement. The decomposed coefficients at same scales are processed by a nonlinear method, and the coefficients at different scales are enhanced in different degree. During the procedure, the method takes full advantage of the properties of Human visual system so as to achieve better performance. The simulations demonstrate that these characters of the proposed approach enable it to fully enhance the content in images, to efficiently alleviate the enhancement of noise and to achieve much better enhancement effect than the traditional approaches.     

Research on bionic quadruped robot based on hydraulic driver

A prototype of hydraulically powered quadruped robot is presented.The aim of the research is to develop a versatile robot platform which could travel fleetly in outdoor terrain with long time of endurance and high load carrying ability.The current version is 1.1m long and 0.48 m wide,and weights about 150 kg.Each leg has four rotational joints driven by hydraulic cylinders and one passive translational joint with spring.The torso carries the control system and the power system.A novel control algorithm is developed based on a Spring-Loaded Inverted Pendulum model and the principle of joint function separation.The robot can not only cross a 150 mm high obstacle in static gait and trot at 2.5km/h and lkm/h on the level-ground and 10°sloped-terrain respectively,but also automatically keep balanced under lateral disturbance.In this paper,the mechanical structure and control systems are also discussed.Simulations and experiments are carried out to validate the design and algorithms.     

Design and Implementation of a Novel Algorithm for Iterative Decoding of Product Codes

A novel product code iterative decoding algorithm and its high speed implementation scheme are proposed in this paper.Based on partial combination of selected columns of check matrix,the reduced-complexity syndrome decoding method is proposed to decode sub-codes of product code and deliver soft output information.So iterative decoding of product codes is possible.The fast sorting algorithm and a look-up method are proposed for high speed implementation of this algo-rithm.Compared to the conventional weighing iterative algorithm,the proposed algorithm has lower complexity while offering better performance,which is demonstrated by simulations and implementation analysis.The implementation scheme and verilog HDL simulation show that it is feasible to achieve high speed decoding with the proposed algorithm.     

Robust Speech Recognition Using a Harmonic Model

Automatic speech recognition under conditions of a noisy environment remains a challenging problem. Traditionally, methods focused on noise structure, such as spectral subtraction, have been employed to address this problem, and thus the performance of such methods depends on the accuracy in noise estimation. In this paper, an alternative method, using a harmonic-based spectral reconstruction algorithm, is proposed for the enhancement of robust automatic speech recognition. Neither noise estimation nor noise-model training are required in the proposed approach. A spectral subtraction integrated autocorrelation function is proposed to determine the pitch for the harmonic model. Recognition results show that the harmonic-based spectral reconstruction approach outperforms spectral subtraction in the middle- and lowsignal noise ratio (SNR) ranges. The advantage of the proposed method is more manifest for non-stationary noise, as the algorithm does not require an assumption of stationary noise.     

Realization of magnetic signal acquisition scheme for banknotes

In order to solve the problem of low signal-to-noise ratio(about 15 d B) in magnetic signal acquisition of banknotes, a new method of magnetic signal acquisition and processing is proposed taking RMB as an example. In this method, weak signa detection is performed to reduce the noise accompanied with the signal. Seven orders of Chebyshev(Ⅰ) filter and the anti-jamming technology are used in the PCB layout, and grounding modes are introduced to reduce the noise of the amplitude waveform. The proposed method reduce the final output noise by 2/3 and the sig nal-to-noise ratio is increased to 24 d B. The experimental results show that the magnetic signal of RMB banknotes are acquired by the circuit stability, which provides an important guarantee for the improvements of the anti-counterfeit and discrimination for banknotes performance.     

Software sensor for slab reheating furnace

It has long been thought that a reheating furnace, with its inherent measurement difficulties and complex dynamics, posed almost insurmountable problems to engineers in steel plants. A novel software sensor is proposed to make more effective use of those measurements that are already available, which has great importance both to slab quality and energy saving. The proposed method is based on the mixtures of Gaussian processes (GP) with the expectation maximization (EM) algorithm employed for parameter estimation of the mixture of models. The mixture model can alleviate the computational complexity of GP and also accords with the changes of operating condition in practical processes. It is demonstrated by on-line estimation of the furnace gas temperature in 1580 reheating furnace in Baosteel Corporation (Group).     

Channel noise-induced phase transition of spiral wave in networks of Hodgkin-Huxley neurons

The phase transition of spiral waves in networks of Hodgkin-Huxley neurons induced by channel noise is investigated in detail.All neurons in the networks are coupled with small-world connections,and the results are compared with the case for regular networks,in which all neurons are completely coupled with nearest-neighbor connections.A statistical variable is defined to study the collective behavior and phase transition of the spiral wave due to the channel noise and topology of the network.The effect of small-world connection networks is described by local regular networks and long-range connection with certain probability p.The numerical results confirm that (1) a stable rotating spiral wave can be developed and maintain robust with low p,where the breakup of the spiral wave and turbulence result from increasing the probability p to a certain threshold;(2) appropriate intensity of the optimized channel noise can develop a spiral wave among turbulent states in small-world connection networks of H-H neurons;and (3) regular connection networks are more robust to channel noise than small-world connection networks.A spiral wave in a small-world network encounters instability more easily as the membrane temperature is increased to a certain high threshold.     

Identification of Modal Parameters with Linear Structure under Non-stationary Ambient Excitation

Empirical mode decomposition (EMD) is proposed to identify linear structure under non-stationary excitation,and non-white noise coefficient is introduced under the assumption of random signals consisting of white noise and non-white noise signals. The cross-correlation function of resoonse signal is decomoosed into mode functions and residue by EMD method. The identification technique of the modal parameters of single freedom degree is applied to each mode function to obtain natural frequencies, damping ratios and mode shapes. The results of identification of the five-degree freedom linear system demonstrate that the proposed method is effective in identifying the parameters of linear structures under non-stationary ambient excitation.     

Edge detection of range images using genetic neural networks

Due to the complexity and asymmetrical illumination, the images of object are difficult to be effectively segmented by some routine method. In this paper, a kind of edge detection method based on image features and genetic algorithms neural network for range images was proposed. Fully considering the essential difference between an edge point and a noise point, some characteristic parameters were extracted from range maps as the input nodes of the network in the algorithm. Firstly, a genetic neural network was designed and implemented. The neural network is trained by genetic algorithm, and then genetic neural network algorithm is combined with the virtue of global optimization of genetic algorithm and the virtue of parallel computation of neural network, so that this algorithm is of good global property. The experimental results show that this method can get much faster and more accurate detection results than the classical differential algorithm, and has better antinoise performance.     

Wavelet Neural Networks for Adaptive Equalization by Using the Orthogonal Least Square Algorithm

Equalizers are widely used in digital communication systems for corrupted or time varying channels. To overcome performance decline for noisy and nonlinear channels, many kinds of neural network models have been used in nonlinear equalization. In this paper, we propose a new nonlinear channel equalization, which is structured by wavelet neural networks. The orthogonal least square algorithm is applied to update the weighting matrix of wavelet networks to form a more compact wavelet basis unit, thus obtaining good equalization performance. The experimental results show that performance of the proposed equalizer based on wavelet networks can significantly improve the neural modeling accuracy and outperform conventional neural network equalization in signal to noise ratio and channel non-linearity.