In environmental science, where information from sensor devices are sparse, data fusion for mapping purposes is often based on geostatistical approaches. We propose a methodology called adaptive distance attention that enables us to fuse sparse, heterogeneous, and mobile sensor devices and predict values at locations with no previous measurement. The approach allows for automatically weighting the measurements according to a priori quality information about the sensor device without using complex and resource-demanding data assimilation techniques. Both ordinary kriging and the general regression neural network (GRNN) are integrated into this attention with their learnable parameters based on deep learning architectures. We evaluate this method using three static phenomena with different complexities: a case related to a simplistic phenomenon, topography over an area of 196 $ {km}^2 $ and to the annual hourly $ {NO}_2 $ concentration in 2019 over the Oslo metropolitan region (1026 $ {km}^2 $). We simulate networks of 100 synthetic sensor devices with six characteristics related to measurement quality and measurement spatial resolution. Generally, outcomes are promising: we significantly improve the metrics from baseline geostatistical models. Besides, distance attention using the Nadaraya–Watson kernel provides as good metrics as the attention based on the kriging system enabling the possibility to alleviate the processing cost for fusion of sparse data. The encouraging results motivate us in keeping adapting distance attention to space-time phenomena evolving in complex and isolated areas.

This paper presents a methodology designed to leverage multitemporal sequences of synthetic aperture radar (SAR) and multispectral data and automatically extract urban changes. The approach compares results using different radar and optical sensors, describing the advantages and drawbacks of using SAR data from the COnstellation of small Satellites for the Mediterranean basin Observation (COSMO)/SkyMed, SAtélite Argentino de Observación COn Microondas (SAOCOM), and Sentinel-1 constellations, as well as nighttime light data or Sentinel-2 images. Multiple indexes obtained from multispectral data are compared, too, and results obtained by an unsupervised clustering procedure are analyzed. The results show that using different datasets it is possible to obtain consistent results about different types of changes in urban areas (e.g., demolition, development, and densification) with different levels of spatial details.

The research focuses on the design space optimisation of National Advisory Committee for Aeronautics (NACA) submerged inlets through the formulation of a hybrid data fusion methodology. Submerged inlets have drawn considerable attention owing to their potential for good on-design performance, for example during cruise flight conditions. However, complexities due to the geometrical topology and interactions among various design variables remain a challenge. This research enhances the current design knowledge of submerged inlets through the utilisation of data mining and Computational Fluid Dynamics (CFD) methodologies, focusing on design space optimisation. A two-pronged approach is employed where the first step encompasses a low-fidelity model through data mining and surrogate modelling to predict and optimise the design parameters, while the second step uses the Design of Experiments (DOE) approach based on the CFD results for the candidate design geometry to construct a surrogate model with high fidelity for design refinement. The feasibility of the proposed methodology is demonstrated for the optimisation of the total pressure recovery of a NACA submerged inlet for the subsonic flight regime. The proposed methodology is found to provide good agreement between the surrogate and CFD-based model and reduce the optimisation processing time by half in comparison with conventional (global-based) CFD optimisation approaches.

Self-localization in highly dynamic environments is still a challenging problem for humanoid robots with limited computation resource. In this paper, we propose a dual-channel unscented particle filter (DC-UPF)-based localization method to address it. A key novelty of this approach is that it employs a dual-channel switch mechanism in measurement updating procedure of particle filter, solving for sparse vision feature in motion, and it leverages data from a camera, a walking odometer, and an inertial measurement unit. Extensive experiments with an NAO robot demonstrate that DC-UPF outperforms UPF and Monte–Carlo localization with regard to accuracy.

Our understanding of Roman urbanism relies on evidence from a few extensively investigated sites, such as Pompeii and Ostia, which are unrepresentative of the full variety of Roman towns. This article presents the results of the first high-resolution GPR survey of a complete Roman town—Falerii Novi, in Lazio, Italy. The authors review the methods deployed and provide an overview of the results, including discussion of a case-study area within the town. They demonstrate how this type of survey has the potential to revolutionise archaeological studies of urban sites, while also challenging current methods of analysing and publishing large-scale GPR datasets.

Superimposing Electronic Navigational Chart (ENC) data on marine radar images can enrich information for navigation. However, direct image superposition is affected by the performance of various instruments such as Global Navigation Satellite Systems (GNSS) and compasses and may undermine the effectiveness of the resulting information. We propose a data fusion algorithm based on deep learning to extract robust features from radar images. By deep learning in this context we mean employing a class of machine learning algorithms, including artificial neural networks, that use multiple layers to progressively extract higher level features from raw input. We first exploit the ability of deep learning to perform target detection for the identification of marine radar targets. Then, image processing is performed on the identified targets to determine reference points for consistent data fusion of ENC and marine radar information. Finally, a more intelligent fusion algorithm is built to merge the marine radar and electronic chart data according to the determined reference points. The proposed fusion is verified through simulations using ENC data and marine radar images from real ships in narrow waters over a continuous period. The results suggest a suitable performance for edge matching of the shoreline and real-time applicability. The fused image can provide comprehensive information to support navigation, thus enhancing important aspects such as safety.

To evaluate the aptness of a navigation system in a particular application, the designer needs to assess its performance over typical trajectories travelled by the vehicle itself. Moreover, he or she may be required to judge which components of the kinematics state may be better estimated (and which will not). The main contributions of this work are two novel and complementary performance measures that, in concert, allow for the assessment of a navigation system within the actual context of its application over specific trajectories. For a given on board instrumental configuration, the “excitability metric” permits the isolation of the contribution of the information conveyed by the vehicle's motion itself, while, the “sensability metric” measures the resultant overall quality of the kinematics state estimation. The same tools could help the designer planning appropriate vehicles manoeuvres in order to obtain a required precision for each estimated component. While emphasis is given on the mathematical justification of those metrics, their use is also illustrated with real flight data recorded from a sounding rocket.

In this research a multi-sensor and data fusion approach was developed to create variable depth tillage zones. Data collected with an electromagnetic sensor was fused with measurements taken with a hydraulic penetrometer and conventionally acquired soil bulk density (BD) and moisture content (MC) measurements. Packing density values were then calculated for eight soil layers to determine the need to cultivate or not. From the results 62% of the site required the deepest tillage at 38 cm, 16% required tillage at 33 cm and 22% required no tillage at all. The resultant maps of packing density were shown to be a useful approach to map layered soil compaction and guide VDT operations.

The paper proposes a geostatistical framework to solve the issues of heterogeneous support for spatial estimation. Apparent soil electrical conductivity (ECa) was measured in a field cropped with San Marzano tomato using a multiple frequency electromagnetic profiler with 6 operating frequencies. Mixed support kriging was used to estimate ECa taking into account the change of support. The method includes punctual kriging with the error being the dispersion variance associated with each frequency. The individual ECa maps were weighted by the dispersion variance to obtain a map which was used for field partition in management zones.

Aiming at improving the poor real-time performance of existing nonlinear filtering algorithms applied to spacecraft autonomous navigation based on Global Positioning System (GPS) measurements and simplifying the algorithm design of navigation algorithms, a spacecraft autonomous navigation algorithm based on polytopic linear differential inclusion is proposed in this paper. Firstly, it is demonstrated that the nonlinear estimation error system of spacecraft autonomous navigation can be modelled as a polytopic linear differential inclusion system model according to the idea of global linearization. Thus, the filtering of a nonlinear system simplified to a filtering of a polytopic linear system with coefficients. Secondly, Tensor-Product (TP) model transformation is applied to determine the polytopic linear differential inclusion system model. The model error introduced by global linearization is reduced and the compromise between computational complexity and modelling accuracy is realised. Finally, a spacecraft autonomous navigation algorithm based on polytopic linear differential inclusion is designed by combining multi-model Kalman filtering with data fusion. Compared with an Extended Kalman Filter (EKF), the proposed algorithm is simpler and easier to implement since it need not update the Jacobian matrices online. Simulation results demonstrate the same estimation accuracy of the proposed algorithm to that of EKF.

Calhoun VD, Wu L, Kiehl KA, Eichele T, Pearlson GD. Aberrant processing of deviant stimuli in schizophrenia revealed by fusion of fMRI and EEG data.

In order to enhance the independent viability of high-orbit satellites, an X-ray pulsar-based navigation (XNAV)/celestial navigation system (CNS) integrated navigation method is proposed. An improved kinematic and static filter is derived to fulfil data fusion that can obtain an optimal estimation for global use. In the filter, unscented transformation is used to reduce linearization error, and the technique of separate-bias is used to reduce the impacts of systematic errors in XNAV measurements. The results of simulations have shown that the proposed navigation system can reach a positioning accuracy of less than 100 m, an improved performance over separate XNAV and CNS.

The recent developments in applications that have been designed to increase road safety require reliable and trustworthy sensors. Keeping this in mind, the most up-to-date research in the field of automotive technologies has shown that LIDARs are a very reliable sensor family. In this paper, a new approach to road obstacle classification is proposed and tested. Two different LIDAR sensors are compared by focusing on their main characteristics with respect to road applications. The viability of these sensors in real applications has been tested, where the results of this analysis are presented.

This paper describes some key points of a new tool being currently developed by Eurocontrol for the assessment of air traffic control (ATC) multisensor trackers performance. It summarizes the algorithmic foundations of the high-accuracy trajectory reconstruction process used to obtain reference trajectories from recorded measures. These trajectories will serve as a reference for the evaluation of the accuracy of ATC data processing centers. The performance of the system is illustrated with some reconstruction experiments on synthetic and real data.

Strapdown inertial navigation systems (INS) often employ aiding sensors to increase accuracy. Nonlinear filtering algorithms are then needed to fuse the collected data from these aiding sensors with measurements of strapdown rate gyros. Aiding sensors usually have slower dynamics compared to gyros and therefore collect data at lower rates. Thus the system will be unobservable between aiding sensors' sampling instants, and the error covariance, which shows the uncertainty in the estimation, grows during the sampling period. This paper presents a divided difference filter (DDF)-based data fusion algorithm, which utilizes the complementary noise profile of rate gyros and gravimetric inclinometers to extend their limits and achieve more accurate attitude estimates. It is confirmed experimentally that DDF achieves better covariance estimates compared to the extended Kalman filter (EKF) because the uncertainty in the state estimate is taken care of in the DDF polynomial approximation formulation.

In recent years, cooperation of heterogeneous multiagents (HMAs) has achieved formidable results and gained an increasing attention by researchers. This paper presents an entertainment robot system based on the HMAs' cooperation. This robot is used to distinguish the basic information of a person from the audience and entertain him in the exhibition hall. The main agents include the Arbitration System (AS), the Face Recognition System (FRS), the Position Cognition System (PCS), and the Behavior Planner (BP), which are carved up according to their respective functions. Each agent completes its own task independently and the robot finishes the whole mission through their cooperation. The hybrid control architecture and the data-fusion algorithm based on Bayesian Belief Network are also discussed in this paper.

This paper reviews currently existing fault-tolerant navigation system architectures and data fusion methods used in the design and development of integrated aircraft navigation systems and also compares their advantages and disadvantages. Four fault-tolerant navigation system architectures are reviewed and the associated Kalman filter architectures and algorithms are discussed. These techniques have been used in most integrated aircraft navigation systems. The aim of this review paper is to provide a guide for navigation system designers to develop future aircraft multisensor navigation systems.