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Decentralized and Distributed Information Filter for Autonomous Intelligent Multisensor Systems

机译:自主智能多传感器系统的分散分布式信息过滤器

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This research paper is on development of distributed and decentralized multisensor data estimation and fusion algorithm with linear information filter for fusing the information from these various sensors and embedded in the developed system. The estimation technique is modified Kalman filter that provides estimates of the information about a certain state. Consequently the developed system is Radar Tracking System (RTS); comprising of array of antennae, and GPS mounted on a Digital Rate Gyroscope (DRG), for accurate and effective Information Gathering and Processing (IGP). The system was simulated to determine the stability and drift error rate of the trajectory over a period of 300secs. The results are also derived under usual methods and proof procedure of some existing literature. The advantage of this algorithm is that the transmission of new information can be done asynchronously only when the amount of information, measure with its randomness, is larger than a fixed threshold level.
机译:这项研究是关于分布式和分散式多传感器数据估计和融合算法的开发,该算法具有线性信息滤波器,用于融合来自这些不同传感器的信息并嵌入到开发的系统中。估计技术是经过修改的卡尔曼滤波器,可提供有关特定状态信息的估计。因此,开发的系统是雷达跟踪系统(RTS);包括天线阵列和安装在数字速率陀螺仪(DRG)上的GPS,用于准确有效的信息收集和处理(IGP)。对系统进行了仿真,以确定在300秒内轨迹的稳定性和漂移误差率。该结果也是根据一些现有文献的常规方法和证明程序得出的。该算法的优势在于,仅当信息量(以其随机性衡量)大于固定阈值水平时,才能异步完成新信息的传输。

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