Performance evaluation of neural network algorithms for multisensor data fusion in an airborne track while scan radar

This paper deals with the solution to the problem of multisensor data fusion for a single target scenario as detected by an airborne track-while-scan radar. The details of a neural network implementation, various training algorithms based on standard backpropagation, and the results of training and testing the neural network are presented. The promising capabilities of RPROP algorithm for multisensor data fusion for various parameters are shown in comparison to other adaptive techniques

Data Fusion of Dual Foot-Mounted INS to Reduce the Systematic Heading Drift

In this report, we investigate the problem of applying a range constraint in order to reduce the systematic heading drift in a foot-mounted inertial navigation system (INS) (motion-tracking). We make use of two foot-mounted INS, one on each foot, which are aided with zero-velocity detectors. A novel algorithm is proposed in order to reduce the systematic heading drift. The proposed algorithm is based on the idea that the separation between the two feet at any given instance must always lie within a sphere of radius equal to the maximum possible spatial separation between the two feet. A Kalman filter, getting one measurement update and two observation updates is used in this algorithm.

Data fusion approaches and applications for construction engineering

Data fusion can be defined as the process of combining data or information for estimating the state of an entity. Data fusion is a multidisciplinary field that has several benefits, such as enhancing the confidence, improving reliability, and reducing ambiguity of measurements for estimating the state of entities in engineering systems. It can also enhance completeness of fused data that may be required for estimating the state of engineering systems. Data fusion has been applied to different fields, such as robotics, automation, and intelligent systems. This paper reviews some examples of recent applications of data fusion in civil engineering and presents some of the potential benefits of using data fusion in civil engineering.

Sensor data scheduling for linear quadratic Gaussian control with full state feedback

Networked control systems (NCSs) have attracted much attention in the past decade due to their many advantages and growing number of applications. Different than classic control systems, resources in NCSs, such as network bandwidth and communication energy, are often limited, which degrade the closed-loop system performance and may even cause the system to become unstable. Seeking a desired trade-off between the closed-loop system performance and the limited resources is thus one heated area of research. In this paper, we analyze the trade-off between the sensor-to-controller communication rate and the closed-loop system performance indexed by the conventional LQG control cost. We present and compare several sensor data schedules, and demonstrate that two event-based sensor data schedules provide better trade-off than an optimal offline schedule. Simulation examples are provided to illustrate the theories developed in the paper. © 2012 AACC American Automatic Control Council).

Assessing the potential of VEGETATION sensor data for mapping snow and ice cover: a Normalized Difference Snow and Ice Index

The VEGETATION (VGT) sensor in SPOT 4 has four spectral bands that are equivalent to Landsat Thematic Mapper (TM) bands (blue, red, near-infrared and mid-infrared spectral bands) and provides daily images of the global land surface at a 1-km spatial resolution. We propose a new index for identifying and mapping of snow ice cover, namely the Normalized Difference Snow/Ice Index (NDSII), which uses reflectance values of red and mid-infrared spectral bands of Landsat TM and VGT. For Landsat TM data, NDSII is calculated as NDSIITM =(TM3 -TM5)/(TM3 +TM5); for VGT data, NDSII is calculated as NDSIIVGT =(B2- MIR)/(B2 + MIR). As a case study we used a Landsat TM image that covers the eastern part of the Qilian mountain range in the Qinghai-Xizang (Tibetan) plateau of China. NDSIITM gave similar estimates of the area and spatial distribution of snow/ice cover to the Normalized Difference Snow Index (NDSI=(TM2-TM5)/(TM2+TM5)) which has been proposed by Hall et al. The results indicated that the VGT sensor might have the potential for operational monitoring and mapping of snow/ice cover from regional to global scales, when using NDSIIVGT.

An Obstacle Cluster Based Data Fusion Method for Mobile Robot

首先给出了一种通过融合多个超声波传感器和一台激光全局定位系统的数据建立机器人环境地图的方法 ,并在此基础上 ,首次提出了机器人在非结构环境下识别障碍物的一种新方法 ,即基于障碍物群的方法 .该方法的最大特点在于它可以更加简洁、有效地提取和描述机器人的环境特征 ,这对于较好地实现机器人的导航、避障 ,提高系统的自主性和实时性是至关重要的 .大量的实验结果表明了该方法的有效性 .

Fusion ARTMAP: An Adaptive Fuzzy Network for Multi-Channel Classification

Fusion ARTMAP is a self-organizing neural network architecture for multi-channel, or multi-sensor, data fusion. Fusion ARTMAP generalizes the fuzzy ARTMAP architecture in order to adaptively classify multi-channel data. The network has a symmetric organization such that each channel can be dynamically configured to serve as either a data input or a teaching input to the system. An ART module forms a compressed recognition code within each channel. These codes, in turn, beco1ne inputs to a single ART system that organizes the global recognition code. When a predictive error occurs, a process called parallel match tracking simultaneously raises vigilances in multiple ART modules until reset is triggered in one of thmn. Parallel match tracking hereby resets only that portion of the recognition code with the poorest match, or minimum predictive confidence. This internally controlled selective reset process is a type of credit assignment that creates a parsimoniously connected learned network.

Acoustic-oceanographic data fusion for prediction of oceanic acoustic elds

Tese de doutoramento, Engenharia Electrónica e Telecomunicações (Processamento de Sinal), Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2014

Person Localization Using Sensor Information Fusion

Nowadays the incredible grow of mobile devices market led to the need for location-aware applications. However, sometimes person location is difficult to obtain, since most of these devices only have a GPS (Global Positioning System) chip to retrieve location. In order to suppress this limitation and to provide location everywhere (even where a structured environment doesn’t exist) a wearable inertial navigation system is proposed, which is a convenient way to track people in situations where other localization systems fail. The system combines pedestrian dead reckoning with GPS, using widely available, low-cost and low-power hardware components. The system innovation is the information fusion and the use of probabilistic methods to learn persons gait behavior to correct, in real-time, the drift errors given by the sensors.

Combining WLAN fingerprint-based localization with sensor data for indoor navigation using mobile devices

This project proposes an approach for supporting Indoor Navigation Systems using Pedestrian Dead Reckoning-based methods and by analyzing motion sensor data available in most modern smartphones. Processes suggested in this investigation are able to calculate the distance traveled by a user while he or she is walking. WLAN fingerprint- based navigation systems benefit from the processes followed in this research and results achieved to reduce its workload and improve its positioning estimations.

An online platform for real-time sensor data collection, visualization, and sharing

Sharing sensor data between multiple devices and users can be^challenging for naive users, and requires knowledge of programming and use of different communication channels and/or development tools, leading to non uniform solutions. This thesis proposes a system that allows users to access sensors, share sensor data and manage sensors. With this system we intent to manage devices, share sensor data, compare sensor data, and set policies to act based on rules. This thesis presents the design and implementation of the system, as well as three case studies of its use.