A Decentralized Parallel One-Pass Fixed-Interval Deconvolution Algorithm for Multisensor Systems with Multiplicative Noises

A decentralized parallel one-pass deconvolution algorithm for multisensor systems with multiplicative noises is proposed. Comparing with the conventional deconvolution algorithm, it avoids the computational overload and the high storage requirement. The algorithm is optimal in the sense of linear minimum-variance. The simulation results illustrate the validity of the proposed algorithm.

IMPLEMENTATION AND PERFORMANCE ANALYSIS OF ACCURATE LOCALIZATION OF SHORT-RANGE TARGETS BASED ON MULTISENSOR SYSTEMS

In this paper, the optimal estimate method is systematically investigated for estimating the position and velocity vectors of a short range target in space with a multisensor system TR n (one transmitting sensor and n receiving sensors). A suboptimal and realizable signal processing scheme is provided. The performance of the suboptimal procedure is analyzed theoretically in detail, and analytical expressions are obtained for the covariance matrix of the estimator error. Simulation results verify the theoretical prediction, which demonstrates the system is able to accurately locate a short range target.

THE RESEARCH OF GRADATION FUSION ALGORITHM BASED ON MULTISENSOR ASYNCHRONOUS SAMPLING SYSTEM

This letter explores the distributed multisensor dynamic system, which has uniform sampling velocity and asynchronous sampling data for different sensors, and puts forward a new gradation fusion algorithm of multisensor dynamic system. As the total forecasted increment value between the two adjacent moments is the forecasted estimate value of the corresponding state increment in the fusion center, the new algorithm models the state and the forecasted estimate value of every moment. Kalman filter and all measurements arriving sequentially in the fusion period are employed to update the evaluation of target state step by step, on the condition that the system has obtained the target state evaluation that is based on the overall information in the previous fusion period. Accordingly, in the present period, the fusion evaluation of the target state at each sampling point on the basis of the overall information can be obtained. This letter elaborates the form of this new algorithm. Computer simulation demonstrates that this new algorithm owns greater precision in estimating target state than the present asynchronous fusion algorithm calibrated in time does.

MULTISENSOR TRACKING SYSTEM WITH ATTITUDE MEASUREMENTS

A new 3 D fusion tracking system for an anti air missile homing system based on radar and imaging sensor is developed. The attitude measurements from the imaging sensor are used to improve the tracking performance. Computer simulation results show that the tracking system greatly reduces the tracking errors compared with trackers without attitude measurements, and achieves small miss distances even when the target has a big maneuver.

Multisensor Data Fusion for High Quality Data Analysis and Processing in Measurement and Instrumentation

Multisensor data fusion （MDF） is an emerging technology to fuse data from multiple sensors in order to make a more accurate estimation of the environment through measurement and detection. Applications of MDF cross a wide spectrum in military and civilian areas. With the rapid evolution of computers and the proliferation of micro-mechanical/electrical systems sensors, the utilization of MDF is being popularized in research and applications. This paper focuses on application of MDF for high quality data analysis and processing in measurement and instrumentation. A practical, general data fusion scheme was established on the basis of feature extraction and merge of data from multiple sensors. This scheme integrates artificial neural networks for high performance pattern recognition. A number of successful applications in areas of NDI （Non-Destructive Inspection） corrosion detection, food quality and safety characterization, and precision agriculture are described and discussed in order to motivate new applications in these or other areas. This paper gives an overall picture of using the MDF method to increase the accuracy of data analysis and processing in measurement and instrumentation in different areas of applications.

Research on Kalman-filter based multisensor data fusion

Multisensor data fusion has played a significant role in diverse areas ranging from local robot guidance to global military theatre defense etc. Various multisensor data fusion methods have been extensively investigated by researchers, of which Klaman filtering is one of the most important. Kalman filtering is the best-known recursive least mean-square algorithm to optimally estimate the unknown states of a dynamic system, which has found widespread application in many areas. The scope of the work is restricted to investigate the various data fusion and track fusion techniques based on the Kalman Filter methods, then a new method of state fusion is proposed. Finally the simulation results demonstrate the effectiveness of the introduced method.

A Polynomial Prediction Filter Method for Estimating Multisensor Dynamically Varying Biases

The estimation of the sensor measurement biases in a multisensor system is vital for the sensor data fusion. A solution is provided for the estimation of dynamically varying multiple sensor biases without any knowledge of the dynamic bias model parameters. It is shown that the sensor bias pseudomeasurement can be dynamically obtained via a parity vector. This is accomplished by multiplying the sensor uncalibrated measurement equations by a projection matrix so that the measured variable is eliminated from the equations. Once the state equations of the dynamically varying sensor biases are modeled by a polynomial prediction filter, the dynamically varying multisensor biases can be obtained by Kalman filter. Simulation results validate that the proposed method can estimate the constant biases and dynamic biases of multisensors and outperforms the methods reported in literature.

Decoupled Wiener state fuser for descriptor systems

By the modem time series analysis method, based on the autoregressive moving average （ARMA） innovation models and white noise estimation theory, using the optimal fusion rule weighted by diagonal matrices, a distributed descriptor Wiener state fuser is presented by weighting the local Wiener state estimators for the linear discrete stochastic descriptor systems with multisensor. It realizes a decoupled fusion estimation for state components. In order to compute the optimal weights, the formulas of computing the cross-covariances among local estimation errors are presented based on cross-covariances among the local innovation processes, input white noise, and measurement white noises. It can handle the fused filtering, smoothing, and prediction problems in a unified framework. Its accuracy is higher than that of each local estimator. A Monte Carlo simulation example shows its effectiveness and correctness.

Multisensor image fusion algorithm using nonseparable wavelet frame transform

A muitisensor image fusion algorithm is described using 2-dimensional nonseparable wavelet frame （NWF） transform. The source muitisensor images are first decomposed by the NWF transform. Then, the NWF transform coefficients of the source images are combined into the composite NWF transform coefficients. Inverse NWF transform is performed on the composite NWF transform coefficients in order to obtain the intermediate fused image. Finally, intensity adjustment is applied to the intermediate fused image in order to maintain the dynamic intensity range. Experiment resuits using real data show that the proposed algorithm works well in muitisensor image fusion.

Research on Obstacle Detection Method of Urban Rail Transit Based on Multisensor Technology

With the rapid development of urban rail transit,passenger traffic is increasing,and obstacle violations are more frequent,and the safety of train operation under high-density traffic conditions is becoming more and more thought provoking.In order to monitor the train operating environment in real time,this paper first adopts multisensing technology based on machine vision and lidar,which is used to collect video images and ranging data of the track area in real time,and then it performs image preprocessing and division of regions of interest on the collected video.Then,the obstacles in the region of interest are detected to obtain the geometric characteristics and position information of the obstacles.Finally,according to the danger degree of obstacles,determine the degree of impact on the train operation,and use the signal system automatic response ormanual response mode to transmit the detection results to the corresponding train,so as to control the train operation.Through simulation analysis and experimental verification,the detection accuracy and control performance of the detection method are confirmed,which provides safety guarantee for the train operation.

THEORETICAL ANALYSIS OF IMPROVEMENT OF TRACK LOSS IN CLUTTER WITH MULTISENSOR DATA FUSION

The paper analyses the improvement of track loss in clutter with multisensor data fusion.By a determination of the transition probability density function for the fusion prediction error, one can study the mechanism of track loss analytically. With nearest-neighbor association algorithm. The paper we studies the fused tracking performance parameters, such as mean time to lose fused track and the cumulative probability of lost fused track versus the normalized clutter density, for track continuation and track initiation, respectively. A comparison of the results obtained with the case of a single sensor is presented. These results show that the fused tracks of multisensor reduce the possibility of track loss and improve the tracking performance. The analysis is of great importance for further understanding the action of data fusion.

Fuzzy Stochastic Approach for Multisensor Fusion

The problem of multisensor fuzzy stochastic fusion is probed in the paper. The concept of fuzzy stochastic fusion entropy is defined, the character of fusion entropy is discussed and the entropy rule of optimal decision in multisensor system is deduced first. The criterion of multisensor fuzzy stochastic data fusion is presented, and the adaptive algorithms of multisensor fuzzy random data fusuion under the criterion is set up second. The effectiveness of the decision fusion and data fusion method has been demonstrated through the computer simulation last.

Express Screening of Biological Objects Using Multisensor Stripping Voltamperometry with Pattern Recognition

Express diagnostics of biological objects is necessary for operational preliminary assessment of the condition of the patient. A method of recognition of differences between the norm and pathology is based on analysis of multidimensional patterns of the voltamperogram electrochemical test systems in Electronic formats “language”, “electronic nose”. The basis of such systems is the use of a set (matrix) sensor with completely different characteristics. A. N. Frumkin Institute of Physical Chemistry and Electrochemistry RAS (IPCE) developed a method for multidimensional stripping voltammetry, which allowed you to provide information on biological matter being investigated not as a number, as a response to a single dimension, and in the form of N-dimensional image. Formats are implemented in the process of electrochemical studies of liquid or gaseous phase. Evaluating the closeness of the resulting image object under test with known samples is collected in a database. Examples of express diagnostics of glaucoma are with accordance of the results of the electrochemical research of blood serum.

基于多传感器的ROS小车底层设计

从轮式移动机器人的底层结构和通信协议两个方向出发,设计一种基于机器人操纵系统(ROS)的自主轮式机器人。首先根据轮式机器人的任务需求,确定底层所需的各种传感器和硬件结构,测试各个传感器与底层芯片的互连与数据交互是否正常,完成底层的组装;然后安装所需的软件平台,Linux系统和ROS操作系统;最后在机器人操作系统的平台进行所需的测试,并针对在测试中遇到的各种问题提出解决方案。在平台的测试结果令人满意,移动机器人在遇到障碍或者突发情况可以及时自主地进行躲避和处理。基于多传感器的底层鲁棒性更强,受到动态环境的干扰更弱,室内的建图与定位工作能够稳定运行,能够为今后的工作提供指导。

信息融合理论的基本方法与进展(Ⅱ)

在军事技术、自动化、智能化等需求的牵引下,信息融合受到学术界和工业界的广泛关注,近年来取得了诸多新的理论与方法进展,因此有必要予以综述.本文首先分析了信息融合面临的问题与挑战,包括系统融合框架、信息不确定、多模态、高冲突、强相关、网络化以及非线性等;并以此为分类依据,在信息融合模型与系统设计、不确定信息融合、多模态信息融合、高冲突信息融合、相关信息融合及网络化信息融合等方面对近10年来的进展进行了综述.同时,探讨了信息一体化融合处理、以人为中心的信息融合、信息获取与融合的联合优化、复杂多传感器信息融合系统体系结构设计、信息融合系统仿真与性能评估、借助更多的数学理论方法等未来几个可能的研究发展方向.

信息融合理论的基本方法与进展

信息融合是现代信息技术与多学科交叉、综合、延拓产生的新的系统科学研究方向 ,由于其在军事和民用领域已经展现出的有效与广阔的理论和应用前景 ,而备受国内外学者和众多实际工程领域专家的高度关注 .文章对近几年来信息融合国际年会和近百种国际学术期刊进行了统计分析 ,对信息融合在军事和民用领域的应用分布情况、所采用的各种不同数学工具和研究方法所占的比例、融合系统建模方法、算法、发展动向。

基于协方差控制的集中式传感器分配算法研究

传感器管理是对一组传感器或测量设备进行自动化或半自动化控制的一种处理过程 ,它实现了探测性能的优化和资源的有效利用 .该文建立了传感器管理的一般最优化模型 ,研究了基于协方差控制策略的传感器分配问题 ,详细讨论了其实现方法 ,并给出三种基于不同矩阵度量的传感器分配算法 .仿真结果表明 ,使用基于协方差控制的传感器分配算法可以进行良好的传感器管理 。

多传感器集成与融合概述

多传感器集成与融合是目前智能机器与系统领域的研究热点 ,具有广阔的应用前景 .本文结合国内外多年的研究成果 ,对多传感器集成与融合进行了全面地介绍 ,详细介绍了其目前研究的主要问题以及解决的方法 .

一种基于分步式滤波的数据融合算法

本文提出了一种基于分步式滤波的多传感器动态系统数据融合算法 .在由多传感器组成的分布式动态系统中 ,当对目标状态的所有观测值到来时 ,首先基于系统先前信息对该时刻目标状态进行预测估计 ,利用Kalman滤波器和各局部观测值依次对该时刻目标状态的估计值进行更新 ,从而得到该时刻目标状态基于全局信息的融合估计值 .文中详细推证了融合算法的具体形式 ,并与传统的集中式数据融合算法在计算复杂度上进行了比较 ,计算机仿真表明该算法与传统的集中式算法对目标状态具有相同的估计精确度 .

基于有理数倍采样的异步数据融合算法研究

本文研究了一类具有不同采样率的分布式多传感器动态系统的数据融合问题,针对一类采样率呈有理数倍关系的动态系统,提出一种基于多源异步采样数据的新融合算法.新算法首先是将来自各个传感器的测量值在融合中心的坐标系中和时钟下进行映射统一;其次,以对目标状态下一时刻的预测值与目标在该时刻状态的估计值之差为基础,建立起描述该融合周期内各个观测点处的目标状态向量之间的动态模型;然后,以该时刻目标状态基于全局信息的估计值为条件,结合建立的新模型和传统的K a lm an滤波器,利用本周期内按序到达的各传感器观测值,依次对各个观测点处目标的状态进行估计和更新;最后,在顺序得到本周期内各个观测点处目标估计值的同时,也将获得下一时刻目标状态基于全局信息的估计值或预测估计值.文中在给出新算法基本思想的同时,也较为详细地对融合算法进行了推导,并通过计算机仿真的方法,将新算法与基于时间校准的算法在估计精确度上进行了比较,从而验证了新算法的有效性.