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The creation of accurate elevation models (topography) from stereo images are critical for a large variety of geospatial activities, including the production of digital orthomosaics, change detection, landing site analysis, geologic mapping, rover traverse planning, and spectral analysis. The United Stated Geological Survey, Astrogeology Science Center, continues to transition the supported planetary sensor models to the Community Sensor Model (CSM) standard. This paper describes the current state of use for this photogrammetric standard, supported sensor model types, and qualitatively compares derived topography between SOCET SET and SOCET GXP (®BAE Systems) using HiRISE stereo images of Mars. Our transition to the CSM standard will ensure an uninterrupted capability to make these valuable products for Mars and many other extraterrestrial planets and moons. [ABSTRACT FROM AUTHOR]

New advanced driver assistance system/automated driving (ADAS/AD) functions have the potential to significantly enhance the safety of vehicle passengers and road users, while also enabling new transportation applications and potentially reducing CO2 emissions. To achieve the next level of driving automation, i.e., SAE Level-3, physical test drives need to be supplemented by simulations in virtual test environments. A major challenge for today’s virtual test environments is to provide a realistic representation of the vehicle’s perception system (camera, lidar, radar). Therefore, new and improved sensor models are required to perform representative virtual tests that can supplement physical test drives. In this article, we present a computationally efficient, mathematically complete, and geometrically exact generic sensor modeling approach that solves the FOV (field of view) and occlusion task. We also discuss potential extensions, such as bounding-box cropping and sensor-specific, weather-dependent FOV-reduction approaches for camera, lidar, and radar. The performance of the new modeling approach is demonstrated using camera measurements from a test campaign conducted in Hungary in 2020 plus three artificial scenarios (a multi-target scenario with an adjacent truck occluding other road users and two traffic jam situations in which the ego vehicle is either a car or a truck). These scenarios are benchmarked against existing sensor modeling approaches that only exclude objects that are outside the sensor’s maximum detection range or angle. The modeling approach presented can be used as is or provide the basis for a more complex sensor model, as it reduces the number of potentially detectable targets and therefore improves the performance of subsequent simulation steps.

"Blind holes" are the main reasons for the reduced performance of microgas sensor carriers. To improve the "blind hole" of catalytic combustion methane sensors and therefore, their thermal stability, this study presents a numerical simulation of the catalytic combustion in an Al2O3− oriented ceramic array involving porous microthermal plates. A three-visualization model of the sensor is established using the FLUENT software, and the simulation results are systematically analyzed based on the dynamics and thermodynamic mechanism of the microgas sensor. The results show that the regularity of the surface reaction presents a circular distribution, with the center line of the channel serving as the axis symmetry. The total reaction velocity in the array hole increases gradually from the inlet to the outlet. The flow velocity at the inlet should be controlled at more than 1 × 10−8 m/s, which is more accurate compared with the concept of "uniform velocity" in previous studies. The optimum pore size at the inlet should be 150 nm, and the inner pore size of the wall should be slightly higher than 300 nm, which is a more careful division compared with previous pore-size studies. The efficient reaction position is from the inlet to the quarter of the hole. The simulation results make up for the deficiencies in the analysis of the process parameters of the methane sensor carrier array hole and the internal reaction change process, as well as provide innovative comments on the sensor structure design. Through digital simulations, the limitations associated with the experiments can be avoided, the theoretical study can be improved, theoretical support can be provided for experiments related to the improvement of thermal stability, the predictability of experiments can be improved, and the feasibility of the research proposal can be verified. These steps are important for the improvement of the "blind hole" problem of catalytic combustion methane sensors. [ABSTRACT FROM AUTHOR]

The creation of accurate elevation models (topography) from stereo images are critical for a large variety of geospatial activities, including the production of digital orthomosaics, change detection, landing site analysis, geologic mapping, rover traverse planning, and spectral analysis. The United Stated Geological Survey, Astrogeology Science Center, continues to transition the supported planetary sensor models to the Community Sensor Model (CSM) standard. This paper describes the current state of use for this photogrammetric standard, supported sensor model types, and qualitatively compares derived topography between SOCET SET and SOCET GXP (®BAE Systems) using HiRISE stereo images of Mars. Our transition to the CSM standard will ensure an uninterrupted capability to make these valuable products for Mars and many other extraterrestrial planets and moons.

This article presents the application of a generic sensor model (GSM) used in the virtual validation of an advanced driver-assistance system. The aim is to demonstrate the feasibility and usefulness of such a model in a case study. This model transforms an incoming object list from a virtual simulation to a sensor-specific object list and applies to all perception sensors acting at the object level. The procedure for parameterization of the sensor model was explained and illustrated with examples of two algorithms: vision- and lidar-based. The validity of the model was confirmed using the case of a pedestrian crossing where incidents involving pedestrians occur. Based on simulation studies and the evaluation of the actual configuration of the pedestrian crossing, conclusions were drawn which may contribute to the increase of pedestrians’ safety, both on the passive and active sides. The obtained results allow for the improvement of the operation of the transportation system in the field of advanced driver-assistance system (ADAS) development. [ABSTRACT FROM AUTHOR]

Radar is a key sensor to achieve a reliable environment perception for advanced driver assistance system and automated driving (ADAS/AD) functions. Reducing the development efforts for ADAS functions and eventually enabling AD functions demands the extension of conventional physical test drives with simulations in virtual test environments. In such a virtual test environment, the physical radar unit is replaced by a virtual radar model. Driving datasets, such as the nuScenes dataset, containing large amounts of annotated sensor measurements, help understand sensor capabilities and play an important role in sensor modeling. This article includes a thorough analysis of the radar data available in the nuScenes dataset. Radar properties, such as detection thresholds, and detection probabilities depending on object, environment, and radar parameters, as well as object properties, such as reflection behavior depending on object type, are investigated quantitatively. The overall detection probability of the considered radar (Continental ARS-408-21) was found to be 27.81%. Four radar models on object level with different complexity levels and different parametrisation requirements are presented: a simple RCS-based radar model with an accuracy of 51%, a linear SVC model with an accuracy of 70%, a Random Forest model with an accuracy of 83%, and a Gradient Boost model with an accuracy of 86%. The feature importance analysis of the machine learning algorithms revealed that object class, object size, and object visibility are the most important parameters for the presented radar models. In contrast, daytime and weather conditions seem to have only minor influence on the modeling results. [ABSTRACT FROM AUTHOR]

The development and commercial use of virtualized sensing (smooth) in the manufacturing of Polyethylene Terephthalate (PET) and sewage treatment are both covered in this paper. The major purpose of using based on an input Artificial Neural Network (ANN) to give operations with real-time estimates of the PET permeability. Additionally, the redundancy viscosity measures provided by the ANN-based soft-sensor (ANNSES) were used to compare the outcomes of the procedure fully equipped. Everything was established that the proposed ANNSES could accurately infer the polymeric viscosity, allowing for the implementation of this soft sensor in real-time system integration. Multidisciplinary modeling, ANN, & a composite method that includes a primary clustering technique are used for data from industrial wastewater operations. The combined ANN approach decreases the overfitting problem with biological systems & improves forecasting accuracy. The results show that the hybrid ANN method can be used to both explain why difficult wastewater treatment processes are nonlinear & to obtain knowledge in imbalanced datasets.

“Blind holes” are the main reasons for the reduced performance of microgas sensor carriers. To improve the “blind hole” of catalytic combustion methane sensors and therefore, their thermal stability, this study presents a numerical simulation of the catalytic combustion in an Al2O3− oriented ceramic array involving porous microthermal plates. A three-visualization model of the sensor is established using the FLUENT software, and the simulation results are systematically analyzed based on the dynamics and thermodynamic mechanism of the microgas sensor. The results show that the regularity of the surface reaction presents a circular distribution, with the center line of the channel serving as the axis symmetry. The total reaction velocity in the array hole increases gradually from the inlet to the outlet. The flow velocity at the inlet should be controlled at more than 1 × 10−8 m/s, which is more accurate compared with the concept of “uniform velocity” in previous studies. The optimum pore size at the inlet should be 150 nm, and the inner pore size of the wall should be slightly higher than 300 nm, which is a more careful division compared with previous pore-size studies. The efficient reaction position is from the inlet to the quarter of the hole. The simulation results make up for the deficiencies in the analysis of the process parameters of the methane sensor carrier array hole and the internal reaction change process, as well as provide innovative comments on the sensor structure design. Through digital simulations, the limitations associated with the experiments can be avoided, the theoretical study can be improved, theoretical support can be provided for experiments related to the improvement of thermal stability, the predictability of experiments can be improved, and the feasibility of the research proposal can be verified. These steps are important for the improvement of the “blind hole” problem of catalytic combustion methane sensors.

Block adjustment is a key technology for achieving large-scale accurate mapping of space via remote sensing. When block adjustment without ground control points (GCPs) is conducted, the adjustment model has large condition number of design matrix, and an ill-conditioned normal equation, which eventually leads to abnormal convergence of direct least squares solution. In this study, we investigated block adjustment without GCPs based on Rational Function Model (RFM), and applied image space affine model of RFM as the systematic error compensation model of single scene imagery. The value range of each parameter of the affine model was determined by calculating the inverse projection errors of tie points and the adjustment model parameters were solved by the optimization method with attached inequalities constraint. Finally, the abnormal convergence problem was solved by reducing the degree of freedom of convergence. Comparative tests of block adjustment without GCPs using various methods were conducted using multiple regional imagery from Ziyuan-3 (ZY-3), Tianhui-1 (TH-1), Luojia-1 (LJ-1) and Gaofen-3 (GF-3). The results show that the proposed method can achieve rapid and correct convergence, and has strong practicability. [ABSTRACT FROM AUTHOR]

Wireless positioning systems are a key enabler for a variety of applications. In indoor environments, Global Navigation Satellite Systems are not available, hence technological alternatives are required. Ultra-Wide Band (UWB) radios provide high-precision distance measurements, however are still prone to ranging errors due to the environment. For localization system design and testing, this leads to cost-intense field trials in which localization methods are validated. As an alternative, publicly available UWB datasets exist, which however are also limited to only certain environmental conditions. In order to provide a more general evaluation basis, we present and discuss a procedure which allows the synthetic generation of UWB distance measurements with respect to theoretical error sources and parameterization based on a conducted measurement campaign. This contribution builds an understanding of commonly present errors for wireless distance measurements and their effects on ranging residual distributions in the distance domain. In addition, we discuss the influences of non-line-of-sight and multipath conditions. Based on this work, distance measurements with respect to the multipath richness of the propagation environment can be generated, helping to flexibly evaluate positioning methods for various emerging applications for telematics systems in the consumer, industrial, or transportation sector. These application fields are ranging from unmanned aerial vehicles, in-house parking systems, and intelligent freight wagons to connected aircraft cabins. [ABSTRACT FROM AUTHOR]

All-source navigation has become increasingly relevant over the past decade with the development of viable alternative sensor technologies. However, as the number and type of sensors informing a system increases, so does the probability of corrupting the system with sensor modeling errors, signal interference, and undetected faults. Though the latter of these has been extensively researched, the majority of existing approaches have designed algorithms centered around the assumption of simultaneously redundant, synchronous sensors with well-understood measurement models, none of which are guaranteed for all-source systems. As part of an overall all-source assured or resilient navigation objective, this research contributes a key component—validation of sensors which have questionable sensor models, in a fault-agnostic and sensor-agnostic manner, and without compromising the ongoing navigation solution in the process. The proposed algorithm combines a residual-based test statistic with the partial update formulation of the Kalman–Schmidt filter to provide a reliable method for sensor model validation that protects the integrity of the navigation solution during the validation process, all using only a single existing filter. The performance of the proposed method is validated against traditional fault detection and exclusion methods (such as normalized solution separation and conventional residual sequence monitoring) using Monte–Carlo simulations in a 2D non-Global Positioning System navigation problem with a plug-and-play position sensor. [ABSTRACT FROM AUTHOR]

Polarization navigation has been an active research direction in navigation fields due to its autonomy and robustness, especially in global navigation satellite system-denied environment. Most existing bionic polarization sensing schemes employed the linear polarizer as the analyzer. However, the quadrature error between the two sets of mutually orthogonal polarizers is usually inevitable, which may cause the sensor output distortion and directly affect the performance of polarization navigation sensors. In this paper, inspired by the special polarization-sensitive structure of insect compound eyes, a polarizing beam splitter (PBS) based polarization navigation sensor is designed. Concerned with the optical path coupling and extinction ratio inconsistency problem between the transmitted beam and the reflected one of the PBS, the novel sensor model is established, where we introduce a coupling coefficient to solve the problems mentioned above. An algorithm of computing the polarization degree and the polarization azimuth angle is presented based on the model. The unscented Kalman filter calibration method is adopted and the calibration experiments are carried out. Finally, the outdoor static accuracy of the sensor is tested, where the results showed that the sensor designed in this paper achieved high accuracy and good stability for a long time.

The virtual testing and validation of advanced driver assistance system and automated driving (ADAS/AD) functions require efficient and realistic perception sensor models. In particular, the limitations and measurement errors of real perception sensors need to be simulated realistically in order to generate useful sensor data for the ADAS/AD function under test. In this paper, a novel sensor modeling approach for automotive perception sensors is introduced. The novel approach combines kernel density estimation with regression modeling and puts the main focus on the position measurement errors. The modeling approach is designed for any automotive perception sensor that provides position estimations at the object level. To demonstrate and evaluate the new approach, a common state-of-the-art automotive camera (Mobileye 630) was considered. Both sensor measurements (Mobileye position estimations) and ground-truth data (DGPS positions of all attending vehicles) were collected during a large measurement campaign on a Hungarian highway to support the development and experimental validation of the new approach. The quality of the model was tested and compared to reference measurements, leading to a pointwise position error of 9.60% in the lateral and 1.57% in the longitudinal direction. Additionally, the modeling of the natural scattering of the sensor model output was satisfying. In particular, the deviations of the position measurements were well modeled with this approach.

Field spectroradiometers are often comprised of several spectral detectors to sample the full range of reflected solar irradiance. An example of such an instrument is the Analytical Spectral Devices (ASD) full-range spectroradiometer, featuring three spectral detectors to capture spectra between 350 and 2500 nm. The resulting spectra often exhibit radiometric steps at the joints of these detectors. This study investigates the influence of external temperature and humidity on the magnitude of these steps by experiments based on a climate chamber. Relative radiometric errors at the detector borders were found to reach up to 16% for the visible and near infrared and 21% for the shortwave infrared 2 (SWIR2), whereas relative reflectance errors are target dependent, typically ranging between 2% and 6%. The derived sensor model provides a physically based explanation of the changes in radiometry due to temperature and demonstrates that all spectral bands are affected to a higher or lesser degree. The model can be used to correct for the effect of temperature on the recorded radiances. Applying the model to ASD instruments that were not tested in the climate chamber still leads to reasonable correction results with RMSE values of 0.6%.

Atmospheric ice accretion is considered a severe safety issue for technical systems such as communication infrastructure, power lines, blades of wind turbines, and so on in cold climate regions. Among different sensing systems to detect and provide reliable information about the state of ice, capacitive sensing has been reported as a suitable technique. In capacitive sensing, measured capacitances are influenced due to ice accretion. Signal processing for ice sensors requires to estimate parameters for the state of ice from the data. In this paper, we present a model-based estimation approach for capacitive ice sensors. We analyze different electrode topologies for ice sensing and provide a statistical analysis of the sensor behavior. We hereby consider the random nature of natural ice accretion. From simulations, we show how a statistical model for the measurement process can be derived and demonstrate the construction of estimation algorithms within the Bayesian framework. We will demonstrate the capability of our approach for automated ice detection by means of long-term field data showing the ability to estimate ice layers with a precision better than 1 mm. [ABSTRACT FROM AUTHOR]

We examine the problem of target tracking whilst simultaneously preserving the target's privacy as epitomized by the robotic panda-tracking scenario, which O'Kane introduced at the 2008 Workshop on the Algorithmic Foundations of Robotics to elegantly illustrate the utility of ignorance. The present paper reconsiders his formulation and the tracking strategy he proposed, along with its completeness. We explore how the capabilities of the robot and panda affect the feasibility of tracking with a privacy stipulation, uncovering intrinsic limits, no matter the strategy employed. This paper begins with a one-dimensional setting and, putting the trivially infeasible problems aside, analyzes the strategy space as a function of problem parameters. We show that it is not possible to actively track the target as well as protect its privacy for every non-trivial pair of tracking and privacy stipulations. Secondly, feasibility can be sensitive, in several cases, to the information available to the robot initially. Quite naturally in the one-dimensional model, one may quantify sensing power by the number of perceptual (or output) classes available to the robot. The robot's power to achieve privacy-preserving tracking is bounded, converging asymptotically with increasing sensing power. We analyze the entire space of possible tracking problems, characterizing every instance as either achievable, constructively by giving a policy where one exists (some of which depend on the initial information), or proving that the instance is impossible. Finally, to relate some of the impossibility results in one dimension to their higher-dimensional counterparts, including the planar panda-tracking problem studied by O'Kane, we establish a connection between tracking dimensionality and the sensing power of a one-dimensional robot. [ABSTRACT FROM AUTHOR]

The effectiveness of sensor-based applications for smart homes and smart buildings is conditioned upon the deployment configuration of their underlying sensors. Real-world evaluation of alternative possible sensor-deployment configurations is labor-intensive, costly, and time-consuming, which implies the need for a simulation-based methodology. In this work, we report on such a methodology that supports the modeling of indoor spaces, the activities of their occupants, and the behaviors of different types of sensors. We argue that, in order for a simulation to be useful for the purpose of evaluating a sensor deployment configuration, it has to generate realistic event streams of individual sensors over time, as well as realistic compositions of sensor events within a time window. We have evaluated our simulator for smart indoor spaces, SIMsis toolkit, in the context of our Smart-Condo ambient-assisted living platform, supporting the observation and analysis of activities of daily living (ADLs). Our findings indicate that SIMsis produces realistic agent traces and sensor readings, and has the potential to support the process of developing and deploying sensor-based applications.

The performance of camera-based polarization sensors largely depends on the estimated model parameters obtained through calibration. Limited by manufacturing processes, the low extinction ratio and inconsistency of the polarizer can reduce the measurement accuracy of the sensor. To account for the challenges, one extinction ratio coefficient was introduced into the calibration model to unify the light intensity of two orthogonal channels. Since the introduced extinction ratio coefficient is associated with degree of polarization (DOP), a new calibration method considering both azimuth of polarization (AOP) error and DOP error for the bionic camera-based polarization sensor was proposed to improve the accuracy of the calibration model parameter estimation. To evaluate the performance of the proposed camera-based polarization calibration model using the new calibration method, both indoor and outdoor calibration experiments were carried out. It was found that the new calibration method for the proposed calibration model could achieve desirable performance in terms of stability and robustness of the calculated AOP and DOP values.

Geolocation information is an important feature of remote sensing image data that is captured through a variety of passive or active observation sensors, such as push-broom electro-optical sensor, synthetic aperture radar (SAR), light detection and ranging (LIDAR) and sound navigation and ranging (SONAR). As a fundamental processing step to locate an image, geo-positioning is used to determine the ground coordinates of an object from image coordinates. A variety of sensor models have been created to describe geo-positioning process. In particular, Open Geospatial Consortium (OGC) has defined the Sensor Model Language (SensorML) specification in its Sensor Web Enablement (SWE) initiative to describe sensors including the geo-positioning process. It has been realized using syntax from the extensible markup language (XML). Besides, two standards defined by the International Organization for Standardization (ISO), ISO 19130-1 and ISO 19130-2, introduced a physical sensor model, a true replacement model, and a correspondence model for the geo-positioning process. However, a standardized encoding for geo-positioning sensor models is still missing for the remote sensing community. Thus, the interoperability of remote sensing data between application systems cannot be ensured. In this paper, a standardized encoding of remote sensing geo-positioning sensor models is introduced. It is semantically based on ISO 19130-1 and ISO 19130-2, and syntactically based on OGC SensorML. It defines a cross mapping of the sensor models defined in ISO 19130-1 and ISO 19130-2 to the SensorML, and then proposes a detailed encoding method to finalize the XML schema (an XML schema here is the structure to define an XML document), which will become a profile of OGC SensorML. It seamlessly unifies the sensor models defined in ISO 19130-1, ISO 19130-2, and OGC SensorML. By enabling a standardized description of sensor models used to produce remote sensing data, this standard is very promising in promoting data interoperability, mobility, and integration in the remote sensing domain.

We present a sensor model comprised of a Timoshenko beam coupled with a linear viscoelastic substrate via a distributed system of compliant elements. The system of governing equations includes the evolution of the kinematic descriptors of the Timoshenko beam and of the interface between the coupling elements and the viscoelastic substrate. This model is used to pose an inverse problem aimed at estimating the constitutive parameters of the substrate from deformation measurements of the beam. The sensing model is demonstrated by comparing its prediction with published experimentally obtained constitutive parameters identifying standard linear viscoelastic material models, showing good agreement between model estimations and experimental results.

Field spectroradiometers integrated in automated systems at Eddy Covariance (EC) sites are a powerful tool for monitoring and upscaling vegetation physiology and carbon and water fluxes. However, exposure to varying environmental conditions can affect the functioning of these sensors, especially if these cannot be completely insulated and stabilized. This can cause inaccuracy in the spectral measurements and hinder the comparison between data acquired at different sites. This paper describes the characterization of key sensor models in a double beam spectroradiometer necessary to calculate the Hemispherical-Conical Reflectance Factor (HCRF). Dark current, temperature dependence, non-linearity, spectral calibration and cosine receptor directional responses are modeled in the laboratory as a function of temperature, instrument settings, radiation measured or illumination angle. These models are used to correct the spectral measurements acquired continuously by the same instrument integrated outdoors in an automated system (AMSPEC-MED). Results suggest that part of the instrumental issues cancel out mutually or can be controlled by the instrument configuration, so that changes induced in HCFR reached about 0.05 at maximum. However, these corrections are necessary to ensure the inter-comparison of data with other ground or remote sensors and to discriminate instrumentally induced changes in HCRF from those related with vegetation physiology and directional effects.