融合多模传感器的室内实时高精度轨迹生成

提出了一种融合多模传感器的室内实时高精度轨迹生成方法,亦即将室内Wi-Fi定位与传感器定位结合起来,生成用户在室内移动的实时轨迹。首先由Wi-Fi定位出用户的初始位置,然后结合Wi-Fi定位的结果以及多个传感器的数据,得到用户的运动速度以及方向,通过航迹推算算法得到用户下一时刻的位置,最后对得出的位置坐标进行卡尔曼滤波处理,得到用户的位置坐标,最终生成用户移动的实时轨迹。实验结果表明,该方法可以得到比Wi-Fi定位更为平滑稠密的移动轨迹,且精确度比其他同类方法更高。

基于金属涂覆多模光纤传感器对矿灯充电电源电流的测量

在矿灯用充电电源的电流检测领域中,特别是在电源处在给矿灯充电状态下,电流的监控非常重要。为了使检测安全、结构简单、成本低,使用金属涂覆多模光纤传感器检测是一种不错的选择。这种传感器是利用多模光纤的微弯效应设计一种强度调制型小电流光纤传感器,通过检测光纤末端输出光束的光能量的变化来实现小电流的检测。实验表明,该设计在一定的范围内能保证测量精度和速度,操作简单、设计灵活,有很好的应用价值。

多模光纤弯曲损耗特性的测量与分析

分析了多模光纤强度型微弯传感器中多模光纤的弯曲损耗。提供了弯曲半径为1mm^8mm和10mm^26mm,光源波长为0.633μm、0.780μm、0.830μm G raded Index多模光纤的弯曲损耗特性的测试结果。首次观察到了多模光纤弯曲损耗随着弯曲半径的减小而增大的趋势,以及在弯曲半径为10mm^26mm范围内损耗特性的不平滑性,并利用传播常数和W G模理论对此现象进行了解释。

基于多模光纤束传感器结构的激光诱导荧光检测系统的光路设计

基于反射型激光诱导荧光检测技术,对激光诱导荧光光纤束传感器检测系统的光路进行了设计与分析,重点探讨了光纤探头中光线轨迹问题.该检测系统利用多模光纤束传感器将激光垂直照射到待测样品上,再把样品发出的荧光信号采集到光电倍增管,将光信号转换为电信号,再通过光子计数器和A/D转换后供计算机实时采样.以荧光素钠为试剂,作为检测物质对该检测系统的灵敏度进行评价,在检测池尺寸为35 mm×15 mm时,最小检出限达到3×10-12mol/L,同时得到检测物质的荧光信号强度与溶液浓度成线性关系.由此认为这是一种性能比较优越的检测系统,有利于提高抗干扰能力和检测灵敏度.

复合制导技术的进展

复合导引头技术是实现精确制导的一种新的技术途径 ,它比单一体制的导引头具有更高的精度和抗干扰能力。近年来 ,随着双模复合导引头技术的发展 ,多模复合导引头也已经在地空导弹、地地导弹、反舰导弹和空地与反辐射导弹上得到广泛应用。叙述了近年来国外飞航导弹、空地导弹和反辐射导弹复合制导技术的发展状况。

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.

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.

Modeling of the Multi-Target Locating and Tracking in the Field Artillery System

A method for the multi target locating and tracking with the multi sensor in a field artillery system is studied. A general modeling structure of the system is established. Based on concepts of cluster and closed ball, an algorithm is put forward for multi sensor multi target data fusion and an optimal solution for state estimation is presented. The simulation results prove the algorithm works well for the multi stationary target locating and the multi moving target tracking under the condition of the sparse target environment. Therefore, this method can be directly applied to the field artillery C 3I system.

A Multi-model Approach for Soft Sensor Development Based on Feature Extraction Using Weighted Kernel Fisher Criterion

Multi-model approach can significantly improve the prediction performance of soft sensors in the process with multiple operational conditions.However,traditional clustering algorithms may result in overlapping phenomenon in subclasses,so that edge classes and outliers cannot be effectively dealt with and the modeling result is not satisfactory.In order to solve these problems,a new feature extraction method based on weighted kernel Fisher criterion is presented to improve the clustering accuracy,in which feature mapping is adopted to bring the edge classes and outliers closer to other normal subclasses.Furthermore,the classified data are used to develop a multiple model based on support vector machine.The proposed method is applied to a bisphenol A production process for prediction of the quality index.The simulation results demonstrate its ability in improving the data classification and the prediction performance of the soft sensor.

A novel fuzzy sensor fusion algorithm

A novel fusion algorithm was given based on fuzzy similarity and fuzzy integral theory. First, it calculated the fuzzy similarity among a certain sensor＇s measurement values and the multiple sensors＇ objective prediction values to determine the importance weight of each sensor and realize multi-sensor data fusion. Then according to the determined importance weight, an intelligent fusion system based on fuzzy integral theory was given, which can solve FEI-DEO and DEI-DEO fusion problems and realize the decision fusion. Simulation results were proved that fuzzy integral algorithm has enhanced the capability of handling the uncertain information and improved the intelligence degrees

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.

Federal extended Kalman filter based on reconstructed observation in incomplete observations

In the estimation and identification of nonlinear system state,aiming at the adverse effect of observation missing randomly caused by detection probability of used sensor which is less than 1,a novel federal extended Kalman filter( FEKF) based on reconstructed observation in incomplete observations( ROIO) is proposed in this paper. On the basis of multi-sensor observation sets,the observation is exchanged at different times to construct a new observation set. Based on each observation set,an extended Kalman filter algorithm is used to estimate the state of the target,and then the federal filtering algorithm is used to solve the state estimation based on the multi-sensor observation data. The effect of the sensor probing probability on the filtering result and the effect of the number of sensors on the filtering result are obtained by the simulation experiment,respectively. The simulation results demonstrate effectiveness of the proposed algorithm.

FBS Effect and Temperature Dependence in Trench-Assisted Multimode Fiber

We propose the trench-assisted multimode fiber(TA-OM4)as a novel sensing fiber in forward Brillouin scattering(FBS)-based temperature sensor,due to its higher temperature sensitivity,better bending resistance and lower propagation loss,compared with the single mode fiber(SMF)and other sensing fibers.The FBS effect and acousto-optic interaction in TA-OM4 are the first time to be demonstrated and characterized at 1550 nm theoretically and experimentally.A 2.0 km long TA-OM4 is put into an oven to measure its temperature sensitivity,which can reach up to 80.3 kHz/℃,exceeding 53%of SMF(52.4 kHz/℃).The simulated and experimental results verify that the TA-OM4 may be a good candidate as the sensing fiber for the FBS-based temperature sensor.

The Design ＆ Implementation of Multimedia Sensor Node and the Building of Multimedia Network in Smart Home

With the requirements of multimedia service increasing in people＇ s life, sensor modules such as microphone, camera are added in the smart home＇ s sensor network, and the acquisition and processing of a large amount of information media such as audio, image and video is becoming a significant characteristics of smart home. The paper focuses on solving the following technical problems： the building of Zigbee multimedia network, the Design and selection of multimedia sensor node. These provide the basic network platform and the core technical support for the building of smart home.

Multimode Interference Refractive Index Sensor Based on Coreless Fiber

A multimode interference refractive index （KI） sensor based on the coreless fiber was numerically and experimentally demonstrated. Two identical single mode fibers （SMF） are spliced at both ends of a section of the coreless fiber which can he considered as the equivalent weakly guiding multimode fiber （MMF） with a step-index profile when the surrounding refractive index （SKI） is lower than that of the coreless fiber. Thus, it becomes the conventional single-mode multimode single-mode （SMS） fiber structure but with a larger core size. The output spectra will shift along with the changes in the SKI owing to the direct exposure of the coreless fiber. The output spectra under different SKIs were numerically studied, as well as the sensitivities with different lengths and diameters of the coreless fiber. The predication and calculation showed the good agreement with the experimental results. The proposed RI sensor proved to be feasible by verification experiments, and the relative error was merely 0.1% which occupied preferable sensing performance and practicability.

Refractive index sensors based on the fused tapered special multi-mode fiber

In this paper,a novel refractive index(RI) sensor is proposed based on the fused tapered special multi-mode fiber(SMMF).Firstly,a section of SMMF is spliced between two single-mode fibers(SMFs).Then,the SMMF is processed by a fused tapering machine,and a tapered fiber structure is fabricated.Finally,a fused tapered SMMF sensor is obtained for measuring external RI.The RI sensing mechanism of tapered SMMF sensor is analyzed in detail.For different fused tapering lengths,the experimental results show that the RI sensitivity can be up to 444.517 81 nm/RIU in the RI range of 1.334 9—1.347 0.The RI sensitivity is increased with the increase of fused tapering length.Moreover,it has many advantages,including high sensitivity,compact structure,fast response and wide application range.So it can be used to measure the solution concentration in the fields of biochemistry,health care and food processing.

Magnetic field sensor based on peanut-shape structure and multimode fiber

An all fiber magnetic field sensor with peanut-shape structure based on multimode fiber(MMF) is proposed and experimentally demonstrated. The sensing structure and magnetic fluid(MF) are both encapsulated in capillary, and the effective refractive index of MF is affected by surrounding magnetic field strength. The measurement of magnetic field is realized by observing the wavelength drift of interference peak. The transmission spectrum generated by Mach-Zehnder interferometer(MZI) includes core-core mode interference and core-cladding mode interference. Experimental results demonstrate that the core-cladding mode interference is sensitive to magnetic field, and the magnetic field sensitivity is 0.047 8 nm/mT. In addition, two kinds of interference dips are sensitive to temperature, and the sensitivities are 0.060 0 nm/°C and 0.052 6 nm/°C, respectively. So the simultaneous measurement of magnetic field strength and temperature can be achieved based on sensitivity matrix.