Synergy Decision for Radar and IRST Data Fusion

A new synergy decision method for radar and infrared search and track (IRST) data fusion is proposed, to solve such problems as how to decrease opportunities for radar suffering from being locked on by adverse electronic support measures (ESM), how to retrieve range information of the target during radar off, and how to detect the maneuver of the target. Firstly, polynomials used to predict target motion states are constructed. Secondly, a set of discriminants for detecting target maneuver are established by comparing the predicted values with the observations from IRST. Thirdly, a set of decisions are presented. Lastly, simulation is performed on the given scenario to test the validity of the method.

Adaptive Modification of the Rolling Force Prediction

In rolling process, the rolling force is an important parameter. The precision of the predicted rolling force will directly affect the precision of the finished product. By using adaptive control theory and fusing the measured and predicted data, the precision of the predicted rolling force is gradually improved. This system has been used in plant for more than one year, and the result of the application shows that the system has steady and reliable performance, and high precision.

Multisensor Fuzzy Stochastic Fusion Based on Genetic Algorithms

To establish a parallel fusion approach of processing high dimensional information, the model and criterion of multisensor fuzzy stochastic data fusion were presented. In order to design genetic algorithm fusion, the fusion parameter coding, initial population and fitness function establishing, and fuzzy logic controller designing for genetic operations and probability choosing were completed. The discussion on the highly dimensional fusion was given. For a moving target with the division of 1 64 (velocity) and 1 75 (acceleration), the precision of fusion is 0 94 and 0 98 respectively. The fusion approach can improve the reliability and decision precision effectively.

Sensor Registration in Asynchronous Data Fusion

To find an effective method to estimate and remove the registration error in asynchronous multisensor system, Kalman filtering technique and least squares approach have been proposed to estimate and remove sensor bias and sensor frame tilt errors in multisensor systems with asynchronous data. Simulation results is presented to demonstrate the performance of these approaches. The least squares approach can compress measurements to any time. The Kalman filter algorithm can detect registration errors and use the information to converge tracks from independent sensors. This is particularly important if the data from the sensors are to be fused.

INTELLIGENT FUSION FOR AEROENGINE WEAR FAULT DIAGNOSIS

Four common oil analysis techniques, including the ferrography analysis （FA）, the spectrometric oil analysis （SOA）, the particle count analysis （PCA）, and the oil quality testing （OQT）, are used to implement the military aeroengine wear fault diagnosis during the test drive process. To improve the precision and the reliability of the diagnosis, the aeroengine wear fault fusion diagnosis method based on the neural networks （NN） and the Dempster-Shafter （D-S） evidence theory is proposed. Firstly, according to the standard value of the wear limit, original data are pre-processed into Boolean values. Secondly, sub-NNs are established to perform the single diagnosis, and their training samples are dependent on experiences from experts. After each sub-NN is trained, diagnosis results are obtained. Thirdly, the diagnosis results of each sub-NN are considered as the basic probability allocation value to faults. The improved D-S evidence theory is applied to the fusion diagnosis, and the final fusion results are obtained. Finally, the method is verified by a diagnosis example.

APPLICATION OF MULTI-SENSOR DATA FUSION BASED ON FUZZY NEURAL NETWORK IN ROTA TING MECHANICAL FAILURE DIAGNOSIS

At present, multi-se nsor fusion is widely used in object recognition and classification, since this technique can efficiently improve the accuracy and the ability of fault toleranc e. This paper describes a multi-sensor fusion system, which is model-based and used for rotating mechanical failure diagnosis. In the data fusion process, the fuzzy neural network is selected and used for the data fusion at report level. By comparing the experimental results of fault diagnoses based on fusion data wi th that on original separate data,it is shown that the former is more accurate than the latter.

Asynchronous Data Fusion of Two Different Sensors

An algorithm is presented for fusion of tracks created by radar and IR sensor which have different dimensional measurement data. It’s assumed that these sensors are asynchronous and the measurement data are transmitted to a central station at different rates. By means of the technique of time matching, two sets of asynchronous data are fused and then the filter is updated according to the fused information. The results show that the accuracy of the filter effect has been improved.

FAULT-TOLERANT INTEGRATED NAVIGATION SYSTEM BASED ON NEURONS

In this paper, the multisensor data fusion technique of a fault tolerant integrated navigation system is discussed. A neural approach for data fusion is proposed for multisensor integrated systems. The simulation results show that this neural approach for data fusion is feasible.

Distributed Computation Models for Data Fusion System Simulation

An attempt has been made to develop a distributed software infrastructure model for onboard data fusion system simulation, which is also applied to netted radar systems, onboard distributed detection systems and advanced C3I systems. Two architectures are provided and verified: one is based on pure TCP/IP protocol and C/S model, and implemented with Winsock, the other is based on CORBA (common object request broker architecture). The performance of data fusion simulation system, i.e. reliability, flexibility and scalability, is improved and enhanced by two models. The study of them makes valuable explore on incorporating the distributed computation concepts into radar system simulation techniques.

Federated UKF algorithm for mobile location estimation with TDOA/Doppler measurements

In order to enhance the location estimation performance of mobile station（MS）tracking and positioning, a new method of mobile location optimal estimation based on the federated filtering structure and the simplified unscented Kalman filter （UKF） is presented. The proposed algorithm uses the Singer mobile statement model as the reference system, and the simplified UKF as the subfilters. The subfilters receive the two groups of independently detected time difference of arrival （TDOA） measurement inputs and Doppler measurement inputs, and produce local estimation outputs to the main estimator. Then the main estimator performs the optimal fusion of the local estimation outputs according to the scalar weighted rule, and a global optimal or suboptimal estimation result is achieved. Finally the main estimator gives feedback and reset information to the subfilters and the reference system for next step estimation. In the simulations, the estimation performance of the proposed algorithm is evaluated and compared with the simplified UKF method with TDOA or Doppler measurement alone. The simulation results demonstrate that the proposed algorithm can effectively reduce the location estimation error and variance of the MS, and has favorable performance in both root mean square error（RMSE） and mean error cumulative distribution function（CDF）.

Multi-sources information fusion algorithm in airborne detection systems

To aim at the multimode character of the data from the airplane detecting system, the paper combines Dempster- Shafer evidence theory and subjective Bayesian algorithm and makes to propose a mixed structure multimode data fusion algorithm. The algorithm adopts a prorated algorithm relate to the incertitude evaluation to convert the probability evaluation into the precognition probability in an identity frame, and ensures the adaptability of different data from different source to the mixed system. To guarantee real time fusion, a combination of time domain fusion and space domain fusion is established, this not only assure the fusion of data chain in different time of the same sensor, but also the data fusion from different sensors distributed in different platforms and the data fusion among different modes. The feasibility and practicability are approved through computer simulation.

DETERMINGING BPA UNDER UNCERTAINTY ENVIRONMENTS AND ITS APPLICATION IN DATA FUSION

Dempster-Shafer (DS) theory of evidence has been widely used in many data fusion ap- plication systems. However, how to determine basic probability assignment, which is the main and the first step in evidence theory, is still an open issue. In this paper, a new method to obtain Basic Probability Assignment (BPA) is proposed based on the similarity measure between generalized fuzzy numbers. In the proposed method, species model can be constructed by determination of the min, average and max value to construct a fuzzy number. Then, a new Radius Of Gravity (ROG) method to determine the similarity measure between generalized fuzzy numbers is used to calculate the BPA functions of each instance. Finally, the efficiency of the proposed method is illustrated by the classi- fication of Iris data.

Comparison Between Four Methods for Data Fusion of ETM+ Multispectral and Pan Images

Four data fusion methods, principle component transform (PCT), brovey transform (BT), smoothing filter-based intensity modulation(SFIM), and hue, saturation, intensity (HSI), are used to merge Landsat-7 ETM+ multispectral bands with ETM+ panchromatic band. Each of them improves the spatial resolution effectively but distorts the original spectral signatures to some extent. SFIM model can produce optimal fusion data with respect to preservation of spectral integrity. However, it results the most blurred and noisy image if the coregistration between the multispectral and pan images is not accurate enough. The spectral integrity for all methods is preserved better if the original multispectral images are within the spectral range of ETM+ pan image.

A Survey on the Privacy-Preserving Data Aggregation in Wireless Sensor Networks

Wireless sensor networks(WSNs)consist of a great deal of sensor nodes with limited power,computation,storage,sensing and communication capabilities.Data aggregation is a very important technique,which is designed to substantially reduce the communication overhead and energy expenditure of sensor node during the process of data collection in a WSNs.However,privacy-preservation is more challenging especially in data aggregation,where the aggregators need to perform some aggregation operations on sensing data it received.We present a state-of-the art survey of privacy-preserving data aggregation in WSNs.At first,we classify the existing privacy-preserving data aggregation schemes into different categories by the core privacy-preserving techniques used in each scheme.And then compare and contrast different algorithms on the basis of performance measures such as the privacy protection ability,communication consumption,power consumption and data accuracy etc.Furthermore,based on the existing work,we also discuss a number of open issues which may intrigue the interest of researchers for future work.

Application of data fusion method to fault diagnosis of nuclear power plant

The work condition of nuclear power plant (NPP) is very bad, which makes ithas faults easily. In order to diagnose (he faults real time, the fusion diagnosis system is built.The data fusion fault diagnosis system adopts data fusion method and divides the fault diagnosisinto three levels, which are data fusion level, feature level and decision level. The feature leveluses three parallel neural networks whose structures are the same. The purpose of using neuralnetworks is mainly to get basic probability assignment ( BPA) of D-S evidence theory, and the neuralnetworks in feature level are used for local diagnosis. D-S evidence theory is adopted to integratethe local diagnosis results in decision level. The reactor coolant system is the study object andwe choose 2# steam generator U-tubes break of the reactor coolant system as a diagnostic example.The experiments prove that the fusion diagnosis system can satisfy the fault diagnosis requirementof complicated system, and verify that the fusion fault diagnosis system can realize the faultdiagnosis of NPP on line timely.

A New Multi-sensor Data Fusion Algorithm Based on EMD-MMSE

A new multi-sensor data fusion algorithm based on EMD-MMSE was proposed.Empirical mode decomposition(EMD)is used to extract the noise of every time series for estimating the variance of the noise.Then minimum mean square error(MMSE)estimator is used to calculate the weights of the corresponding series.Finally,the fused signal is the weighted addition of all these series.The experiments in lab testified the efficiency of this method.In addition,the comparison in fusion time and fusion results with existing fusion method based on wavelet and average technique shows the advantage of this method greatly.

Coal mine gas monitoring system based on wireless sensor network

Based on the nowadays＇ condition, it is urgent that the gas detection cable communication system must be replaced by the wireless communication systems. The wireless sensors distributed in the environment can achieve the intelligent gas monitoring system. Apply with multilayer data fuse to design working tactics, and import the artificial neural networks to analyze detecting result. The wireless sensors system communicates with the control center through the optical fiber cable. All the gas sensor nodes distributed in coal mine are combined into an intelligent, flexible structure wireless network system, forming coal mine gas monitoring system based on wireless sensor network.

Online residual useful life prediction of large-size slewing bearings A data fusion method

To decrease breakdown time and improve machine operation reliability,accurate residual useful life(RUL) prediction has been playing a critical role in condition based monitoring.A data fusion method was proposed to achieve online RUL prediction of slewing bearings,which consisted of a reliability based RUL prediction model and a data driven failure rate(FR) estimation model.Firstly,an RUL prediction model was developed based on modified Weibull distribution to build the relationship between RUL and FR.Secondly,principal component analysis(PCA) was introduced to process multi-dimensional life-cycle vibration signals,and continuous squared prediction error(CSPE) and its time-domain features were employed as equipment performance degradation features.Afterwards,an FR estimation model was established on basis of the degradation features and relevant FRs using simplified fuzzy adaptive resonance theory map(SFAM) neural network.Consequently,real-time FR of equipment can be obtained through FR estimation model,and then accurate RUL can be calculated through the RUL prediction model.Results of a slewing bearing life test show that CSPE is an effective indicator of performance degradation process of slewing bearings,and that by combining actual load condition and real-time monitored data,the calculation time is reduced by 87.3%and the accuracy is increased by 0.11%,which provides a potential for online RUL prediction of slewing bearings and other various machineries.

Combination of Multi-class Probability Support Vector Machines for Fault Diagnosis

To deal with multi-source multi-class classification problems, the method of combining multiple multi-class probability support vector machines (MPSVMs) using Bayesian theory is proposed in this paper. The MPSVMs are designed by mapping the output of standard support vector machines into a calibrated posterior probability by using a learned sigmoid function and then combining these learned binary-class probability SVMs. Two Bayes based methods for combining multiple MPSVMs are applied to improve the performance of classification. Our proposed methods are applied to fault diagnosis of a diesel engine. The experimental results show that the new methods can improve the accuracy and robustness of fault diagnosis.

Data fusion for fault diagnosis using multi-class Support Vector Machines

Multi-source multi-class classification methods based on multi-class Support Vector Machines and data fusion strategies are proposed in this paper. The centralized and distributed fusion schemes are applied to combine information from several data sources. In the centralized scheme, all information from several data sources is centralized to construct an input space. Then a multi-class Support Vector Machine classifier is trained. In the distributed schemes, the individual data sources are proc-essed separately and modelled by using the multi-class Support Vector Machine. Then new data fusion strategies are proposed to combine the information from the individual multi-class Support Vector Machine models. Our proposed fusion strategies take into account that an Support Vector Machine (SVM) classifier achieves classification by finding the optimal classification hyperplane with maximal margin. The proposed methods are applied for fault diagnosis of a diesel engine. The experimental results showed that almost all the proposed approaches can largely improve the diagnostic accuracy. The robustness of diagnosis is also improved because of the implementation of data fusion strategies. The proposed methods can also be applied in other fields.