Multiple Sensor Platforms Research Articles

Advancing Sustainable Mobility: A Data Acquisition System for Light Vehicles and Active Mobility

Active mobility and light vehicles, such as e-bikes, are gaining increasing attention as sustainable transportation alternatives to internal combustion solutions. In this context, collecting comprehensive data on environmental conditions, vehicle performance, and user interaction is crucial for improving system efficiency and user experience. This paper presents a data acquisition system designed to collect data from multiple sensor platforms. The architecture is optimized to maintain low power consumption and operate within limited computational resources, making it suitable for real-time data acquisition on light vehicles. To achieve this, a data acquisition module was developed using a single-board computer integrated with a custom shield, which also captures data related to the assistance of an e-bike motor through a wireless interface. The paper provides an in-depth discussion of the architecture and software development, along with a detailed overview of the sensors used. A demonstrator was created to verify the system architecture idea and prove the potentialities of the system overall. The demonstrator has been qualified by professional and semi-professional riders in the framework of the Giro-E, a cyclist event which took place in May 2024, on the same roads of the Giro d’Italia. Finally, some preliminary analyses on the data acquired are provided to show the performance of the system, particularly in reconstructing the user behavior, the environmental parameters, and the type of road.

Microscale Temperature-Humidity Index (THI) Distribution Estimated at the City Scale: A Case Study in Maebashi City, Gunma Prefecture, Japan

Global warming and climate change are significantly impacting local climates, causing more intense heat during the summer season, which poses risks to individuals with pre-existing health conditions and negatively affects overall human health. While various studies have examined the Surface Urban Heat Island (SUHI) phenomenon, these studies often focus on small to large geographic regions using low-to-moderate-resolution data, highlighting general thermal trends across large administrative areas. However, there is a growing need for methods that can detect microscale thermal patterns in environments familiar to urban residents, such as streets and alleys. The temperature-humidity index (THI), which incorporates both temperature and humidity data, serves as a critical measure of human-perceived heat. However, few studies have explored microscale THI variations within urban settings and identified potential THI hotspots at a local level where SUHI effects are pronounced. This research aims to address this gap by estimating THI at a finer resolution scale using data from multiple sensor platforms. We developed a model with the random forest algorithm to assess THI trends at a resolution of 0.5 m, utilizing various variables from different sources, including Landsat 8 land surface temperature (LST), unmanned aerial system (UAS)-derived LST, Sentinel-2 NDVI and NDMI, a wind exposure index, solar radiation modeled from aircraft and UAS-derived Digital Surface Models, and vehicle density and building floor area from social big data. Two models were constructed with different variables: Modelnatural, which includes variables related to only natural factors, and Modelmix, which includes all variables, including anthropogenic factors. The two models were compared to reveal how each source contributes to the model development and SUHI effects. The results show significant improvements, as Modelnatural had a fitting R2 = 0.5846, a root mean square error (RMSE) = 0.5936 and a mean absolute error (MAE) = 0.4294. Moreover, when anthropogenic factors were introduced, Modelmix performed even better, with R2 = 0.9638, RMSE = 0.1751, and MAE = 0.1065 (n = 923). This study contributes to the future of microscale SUHI analysis and offers important insights into urban planning and smart city development.

Turning Night into Day: The Creation and Validation of Synthetic Nighttime Visible Imagery Using the Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) and Machine Learning

Abstract Meteorological satellite imagery is a critical asset for observing and forecasting weather phenomena. The Joint Polar Satellite System (JPSS) Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) sensor collects measurements from moonlight, airglow, and artificial lights. DNB radiances are then manipulated and scaled with a focus on digital display. DNB imagery performance is tied to the lunar cycle, with the best performance during the full moon and the worst with the new moon. We propose using feed-forward neural network models to transform brightness temperatures and wavelength differences in the infrared spectrum to a pseudo-lunar reflectance value based on lunar reflectance values derived from observed DNB radiances. JPSS NOAA-20 and Suomi National Polar-Orbiting Partnership (SNPP) satellite data over the North Pacific Ocean at night for full moon periods from December 2018 to November 2020 were used to design the models. The pseudo-lunar reflectance values are quantitatively compared to DNB lunar reflectance, providing the first-ever lunar reflectance baseline metrics. The resulting imagery product, Machine Learning Nighttime Visible Imagery (ML-NVI), is qualitatively compared to DNB lunar reflectance and infrared imagery across the lunar cycle. The imagery goal is not only to improve upon the consistent performance of DNB imagery products across the lunar cycle, but ultimately to lay the foundation for transitioning the algorithm to geostationary sensors, making global continuous nighttime imagery possible. ML-NVI demonstrates its ability to provide DNB-derived imagery with consistent contrast and representation of clouds across the full lunar cycle for nighttime cloud detection. Significance Statement This study explores the creation and evaluation of a feed-forward neural network to generate synthetic lunar reflectance values and imagery from VIIRS infrared channels. The model creates lunar reflectance values typical of full moon scenes, enabling quantifiable comparisons for nighttime imagery evaluations. Additionally, it creates imagery that highlights low clouds better than its infrared counterparts. Results indicate the ability to create visually consistent nighttime visible imagery across the full lunar cycle for the improved nighttime detection of low clouds. Wavelengths chosen are available on both polar and geostationary satellite sensors to support the utilization of the algorithm on multiple sensor platforms for improved temporal resolution and greater simultaneous geographic coverage over polar orbiters alone.

A Network-Group Target State and Network Topology Estimation Method Based on Signals Containing Delay, Doppler and Address

Network-group targets are a set of objectives that adhere to a shared communication protocol, perform common tasks, and exhibit relatively coordinated movements. Typically, network-group targets emit radar and communication signals. However, they often employ a silent mode to evade our perception. Despite this, they continue to exchange data through their communication networks. By intercepting the communication signals of these targets, this paper proposes a method for estimating the state and network topology of network-group targets based on random finite set (RFS) theory. This method is based on the modeling of network-group targets using a labeled multi-Bernoulli (LMB) RFS. In state estimation, the usual method involves extracting the localization parameters from the signals in the first step and use these parameters for target tracking in the second step. This study focused on estimating the kinematic states of network-group targets using communication signals containing delay and Doppler information received by multiple mobile sensor platforms. The proposed method considers the coherency between frequency-hopping pulses in the signals, resulting in an improved estimation performance. In addition, considering that network-group targets require network access for information exchange, graph theory concepts were utilized to estimate the network topology of network-group targets by address measurement. The simulation results validated the effectiveness of the proposed method and demonstrated its superior performance.

Livestock Informatics Toolkit: A Case Study in Visually Characterizing Complex Behavioral Patterns across Multiple Sensor Platforms, Using Novel Unsupervised Machine Learning and Information Theoretic Approaches.

Large and densely sampled sensor datasets can contain a range of complex stochastic structures that are difficult to accommodate in conventional linear models. This can confound attempts to build a more complete picture of an animal’s behavior by aggregating information across multiple asynchronous sensor platforms. The Livestock Informatics Toolkit (LIT) has been developed in R to better facilitate knowledge discovery of complex behavioral patterns across Precision Livestock Farming (PLF) data streams using novel unsupervised machine learning and information theoretic approaches. The utility of this analytical pipeline is demonstrated using data from a 6-month feed trial conducted on a closed herd of 185 mix-parity organic dairy cows. Insights into the tradeoffs between behaviors in time budgets acquired from ear tag accelerometer records were improved by augmenting conventional hierarchical clustering techniques with a novel simulation-based approach designed to mimic the complex error structures of sensor data. These simulations were then repurposed to compress the information in this data stream into robust empirically-determined encodings using a novel pruning algorithm. Nonparametric and semiparametric tests using mutual and pointwise information subsequently revealed complex nonlinear associations between encodings of overall time budgets and the order that cows entered the parlor to be milked.

The influence of training data variability on a supervised machine learning classifier for Structure from Motion (SfM) point clouds of rock slopes

Supervised Machine Learning (ML) can be used to automatically interpret remote sensing data in engineering geology, with applications for rockfall and landslide characterization. However, supervised algorithms typically require very large training databases from which to learn predictive relationships, and there is little guidance on how to construct such a database in an earth science context with high temporal and spatial heterogeneity. This study builds a supervised classifier to perform basic rock slope characterization on Structure from Motion (SfM) point clouds collected under a variety of conditions. Eight datasets were collected in Colorado and Utah, USA, with multiple different sensor platforms (terrestrial and aerial), rock types, seasons, and lighting conditions, and each dataset was manually labeled to identify regions of vegetation, rock, soil, talus, and snow. A total of 2560 Random Forest models were built with different combinations of training datasets and combinations of geometric and color features. Most models were able to identify vegetation and rock with high accuracy (median F scores of 86% and 68% respectively), but performance for soil, talus, and snow was overall much poorer, and the median overall accuracy of generalized classifiers was 60%. Many characteristics of the training data were found to have significant effects on generalization accuracy, indicating that training datasets must be curated to be applicable to specific data collection parameters, seasons, lighting conditions, and geological settings. We conclude that high accuracy generalized results can be obtained, but the ML model must be carefully constructed, and its limitations acknowledged.

Active‐Matrix‐Based Flexible Optical Image Sensor

AbstractOwing to the demand for the development of Internet of things devices, multiple sensor platforms are of great interest to society and its future electronics. One of these emerging devices is a mechanically flexible sensor system that can attach and conform to nonplanar surfaces, unlike conventional inflexible sensor chips. To realize this concept, a variety of flexible sensor systems must be developed from materials to system integrations. This study proposes a flexible sensor system as one of the candidates: an active‐matrix‐based flexible optical image sensor that uses a stacked nanosheet film formed by a solution process and transistors. The results show that the sensor can be utilized as an optical imager because it detects enough optical stimuli. In addition to the sensitivity, the mechanical flexibility of the active‐matrix optical sensor is tested, and the results exhibit good electrical stability even after 1000 bending cycles and 0.85 cm bending radius. The mapping of light distribution is successfully demonstrated under both flat and curved conditions. This flexible optical sensor array shows the high possibility to image the light distribution for high‐resolution and low power mapping realized by active matrix circuit integration.

Hierarchically porous carbon nanofibers embedded with cobalt nanoparticles for efficient H2O2 detection on multiple sensor platforms

Hydrogen peroxide (H2O2) detection is essential in many industrial and biological processes. Current applications of H2O2 electrochemical sensors are often limited by the high cost of precious metal electrocatalysts and sophisticated instrumentation requirements. Here, we demonstrate a highly active non-precious metal cobalt/carbon nanocomposite electrocatalyst for efficient H2O2 sensing on multiple sensor platforms, including conventional glassy carbon electrodes (GCEs), free-standing film electrodes, and screen-printed electrodes (SPEs). This nanocomposite was synthesized by carbonation of electrospun polyacrylonitrile nanofibers containing zeolitic imidazole framework (i.e., ZIF-67) particles. The resulting hierarchically porous nitrogen-doped carbon nanofiber film contains hollow mesoporous carbon particles and exposed cobalt nanoparticles, which has sufficient mechanical strength as a self-standing film and is also electrically highly conductive. Consequently, when tested using GCEs, it exhibits an ultrahigh sensitivity of 300 μA/cm2 mM and a detection limit of 10 μM below the permissible H2O2 residual limit in food packaging permittable by the US FDA. When utilized as free-standing film electrodes, its linear detection range extends an order higher to 50 mM. It is also highly specific toward H2O2 sensing without responses to multiple interfering analytes. Further, it enables mobile H2O2 detection on SPEs when integrated with a portable potentiostat and mobile phone. This new nanocomposite electrocatalyst can open various new opportunities for practical H2O2 sensing applications.

A benchmarking measurement campaign in GNSS-denied/challenged indoor/outdoor and transitional environments

Abstract Localization in GNSS-denied/challenged indoor/outdoor and transitional environments represents a challenging research problem. This paper reports about a sequence of extensive experiments, conducted at The Ohio State University (OSU) as part of the joint effort of the FIG/IAG WG on Multi-sensor Systems. Their overall aim is to assess the feasibility of achieving GNSS-like performance for ubiquitous positioning in terms of autonomous, global, preferably infrastructure-free positioning of portable platforms at affordable cost efficiency. In the data acquisition campaign, multiple sensor platforms, including vehicles, bicyclists and pedestrians were used whereby cooperative positioning (CP) is the major focus to achieve a joint navigation solution. The GPSVan of The Ohio State University was used as the main reference vehicle and for pedestrians, a specially designed helmet was developed. The employed/tested positioning techniques are based on using sensor data from GNSS, Ultra-wide Band (UWB), Wireless Fidelity (Wi-Fi), vison-based positioning with cameras and Light Detection and Ranging (LiDAR) as well as inertial sensors. The experimental and initial results include the preliminary data processing, UWB sensor calibration and Wi-Fi indoor positioning with room-level granularity and platform trajectory determination. The results demonstrate that CP techniques are extremely useful for positioning of platforms navigating in swarms or networks. A significant performance improvement in terms of positioning accuracy and reliability is achieved. Using UWB, decimeter-level positioning accuracy is achievable under typical conditions, such as normal walls, average complexity buildings, etc. Using Wi-Fi fingerprinting, success rates of approximately 97 % were obtained for correctly detecting the room-level location of the user.

A Novel Approach of IoT Services Orchestration Based on Multiple Sensor and Actuator Platforms Using Virtual Objects in Online IoT App-Store

The information and communication technology (ICT) is witnessing a revolutionary era with the advancements in the Internet of Things (IoT). An IoT network is a combination of sensor and actuator networks, connected and communicating in certain ways to design and provide IoT services to the end users. These IoT services are created by mapping physical-world objects into virtual-world objects. In this work, we propose a novel approach of IoT services orchestration based on multiple sensor and actuator platforms using virtual objects in online IoT application-store (app-store). In this work, we focused on combining the concepts of do-it-yourself (DIY) IoT marketplace, virtual objects (VOs), and virtual services. We built a fusion IoT services platform on a previously proposed IoT application store. The IoT application store enables the sharing and discovery of IoT VOs, along with micro-services associated with each VO uploaded into the application store. The fusion IoT services platform enables the user to fetch the desired or all VOs from the IoT app store and map the available VOs to form the fusion IoT services. The user can either select all the available VOs and see all the possible services’ combinations or select the desired (DIY) services and customize the virtual services scope. The performance of the proposed fusion IoT services platform was evaluated on the basis of the service connection times, service response times with varying load of VOs, virtual users, and active platforms. The proposed idea also offers a sustainable solution by proposing the reuse of existing resources and reducing duplicate deployments, which can lessen the total cost of the physical networks’ deployment and maintenance. To the best of our knowledge, the proposed work is the first of its kind.

An Imaging Sensor-Aided Vision Navigation Approach that Uses a Geo-Referenced Image Database.

In determining position and attitude, vision navigation via real-time image processing of data collected from imaging sensors is advanced without a high-performance global positioning system (GPS) and an inertial measurement unit (IMU). Vision navigation is widely used in indoor navigation, far space navigation, and multiple sensor-integrated mobile mapping. This paper proposes a novel vision navigation approach aided by imaging sensors and that uses a high-accuracy geo-referenced image database (GRID) for high-precision navigation of multiple sensor platforms in environments with poor GPS. First, the framework of GRID-aided vision navigation is developed with sequence images from land-based mobile mapping systems that integrate multiple sensors. Second, a highly efficient GRID storage management model is established based on the linear index of a road segment for fast image searches and retrieval. Third, a robust image matching algorithm is presented to search and match a real-time image with the GRID. Subsequently, the image matched with the real-time scene is considered to calculate the 3D navigation parameter of multiple sensor platforms. Experimental results show that the proposed approach retrieves images efficiently and has navigation accuracies of 1.2 m in a plane and 1.8 m in height under GPS loss in 5 min and within 1500 m.

Decentralized Dynamic Data-driven Monitoring of Atmospheric Dispersion Processes

Online state and parameter estimation of atmospheric dispersion processes using multiple mobile sensor platforms is a prominent example of Dynamic Data-Driven Application Systems (DDDAS). Based on repeated predictions of a partial differential equation (PDE) model and measurements of the sensor network, estimates are updated and sensor trajectories are adapted to obtain more informative measurements. While most of the monitoring strategies require a central supercomputer, a novel decentralized plume monitoring approach is proposed in this paper. It combines the benefits of distributed approaches like scalability and robustness with the prediction ability of PDE process models. The strategy comprises model order reduction to keep calculations computationally tractable and a joint Kalman Filter with Covariance Intersection for incorporating measurements and propagating state information in the sensor network. Moreover, a cooperative vehicle controller is employed to guide the sensor vehicles to dynamically updated target locations that are based on the current estimated error variance.

The integration of optical, topographic, and radar data for wetland mapping in northern Minnesota

Accurate and current wetland maps are critical tools for water resources management, however, many existing wetland maps were created by manual interpretation of one aerial image for each area of interest. As such, these maps do not inherently contain information about the intra- and interannual hydrologic cycles of wetlands, which is important for effective wetland mapping. In this paper, several sources of remotely sensed data will be integrated and evaluated for their suitability to map wetlands in a forested region of northern Minnesota. These data include: aerial photographs from two different times of a growing season, National Elevation Dataset and topographical derivatives such as slope and curvature, and multitemporal satellite-based synthetic aperture radar (SAR) imagery and polarimetric decompositions. We identified the variables that are most important to accurately classify wetland from upland areas and discriminate between wetland types for a forested region of northern Minnesota using the decision-tree classifier randomForest. The classifier was able to differentiate wetland from upland and water with 75% accuracy using optical, topographic, and SAR data combined, compared with 72% using optical and topographical data alone. Classifying wetland type proved to be more challenging; however, the results were significantly improved over the original National Wetland Inventory classification of only 49% compared with 63% using optical, topographic, and SAR data combined. This paper illustrates that integration of remotely sensed data from multiple sensor platforms and over multiple periods during a growing season improved wetland mapping and wetland type classification in northern Minnesota.

Cooperative Filters and Control for Cooperative Exploration

Autonomous mobile sensor networks are employed to measure large-scale environmental fields. Yet an optimal strategy for mission design addressing both the cooperative motion control and the cooperative sensing is still an open problem. We develop strategies for multiple sensor platforms to explore a noisy scalar field in the plane. Our method consists of three parts. First, we design provably convergent cooperative Kalman filters that apply to general cooperative exploration missions. Second, we present a novel method to determine the shape of the platform formation to minimize error in the estimates and design a cooperative formation control law to asymptotically achieve the optimal formation shape. Third, we use the cooperative filter estimates in a provably convergent motion control law that drives the center of the platform formation to move along level curves of the field. This control law can be replaced by control laws enabling other cooperative exploration motion, such as gradient climbing, without changing the cooperative filters and the cooperative formation control laws. Performance is demonstrated on simulated underwater platforms in simulated ocean fields.

Shifted Rayleigh filter: a new algorithm for bearings-only tracking

A new algorithm, the "shifted Rayleigh filter," is introduced for two- or three-dimensional bearings-only tracking problems. In common with other "moment matching" tracking algorithms such as the extended Kalman filter and its modern refinements, it approximates the prior conditional density of the target state by a normal density; the novel feature is that an exact calculation is then performed to update the conditional density in the light of the new measurement. The paper provides the theoretical justification of the algorithm. It also reports on simulations involving variants on two scenarios, which have been the basis of earlier comparative studies. The first is a "benign" scenario where the measurements are comparatively rich in range-related information; here the shifted Rayleigh filter is competitive with standard algorithms. The second is a more "extreme" scenario, involving multiple sensor platforms, high-dimensional models and noisy measurements; here the performance of the shifted Rayleigh filter matches the performance of a high-order bootstrap particle filter, while reducing the computational overhead by an order of magnitude.

Coordinated search for a lost target in a Bayesian world

This paper describes a decentralized Bayesian approach to the problem of coordinating multiple autonomous sensor platforms searching for a single non-evading target. In this architecture, each decision maker builds an equivalent representation of the probability density function (PDF) of the target state through a general decentralized Bayesian sensor network, enabling them to coordinate their actions without exchanging any information about their plans. The advantage of the approach is that a high degree of scalability and real-time adaptability can be achieved. The framework is implemented on a real-time high-fidelity multi-vehicle simulator system. The effectiveness of the method is demonstrated in different scenarios for a team of airborne search vehicles looking for both a stationary and a drifting target lost at sea.