Sensor modeling and demonstration of a multi-object spectrometer for performance-driven sensing

Kerekes, John P.; Presnar, Michael D.; Fourspring, Kenneth D.; Ninkov, Zoran; Pogorzala, David; Raisanen, A.; Rice, Andrew; Vasquez, Juan R.; Patel, Jeffrey P.; MacIntyre, Robert T.; Brown, Scott

doi:10.1117/12.819265

Cited by 12 publications

(7 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A sensor that can adaptively collect spatial and spectral data is required to perfom high rate tracking of objects of interest [12]. RITMOS is considered for use as an adaptive dynamic data driven sensor.…”

Section: A Ritmos Sensormentioning

confidence: 99%

Feature Matching With an Adaptive Optical Sensor in a Ground Target Tracking System

et al. 2015

View full text Add to dashboard Cite

We consider methods to address the optical featureaided remote sensing tracking problem for vehicles in a challenging environment. Our approach is to apply the dynamic data driven application systems computing paradigm to implement control of an adaptive sensor. This adaptive sensor acquires a panchromatic image while simultaneously allowing the collection of visible-near infrared spectral data at specified pixels. This sensor holds the promise of delivering the increased accuracy of targeted spectral sensing without the enormous data volume of full spectral images. The target of interest is optimally imaged by the sensor based on the target's forecasted location and motion relative to the extracted content of the background. Background context is both extracted from the image and created from the OpenStreetMap road network. We describe the implementation of the tracking framework and testing of some of the components using simulated imagery created with the digital imaging and remote sensing image generation model. The Gaussian sum filter is employed to solve the data assimilation problem by forming a multimodel forecasting set that is used to increase the robustness and flexibility of tracking. For feature matching, we create an efficient sampling strategy that is informed by the viewing conditions to adaptively determine which pixels to measure spectrally in order to distinguish between different targets using a spectral distance measure.Index Terms-Adaptive sensing, DDDAS, hyperspectral imaging, optical sensor, target tracking.

show abstract

Section: A Ritmos Sensormentioning

confidence: 99%

Feature Matching With an Adaptive Optical Sensor in a Ground Target Tracking System

et al. 2015

View full text Add to dashboard Cite

show abstract

“…urban) systems in general. Agent-based simulators allow the user to select the time and spatial scales of the model [18]; therefore, these tools have been used to study intelligent traffic control systems, mobile applications and other similar systems [3], [19], [31], [32]. While agent-based models can typically scale with respect to the size of the system (e.g., number of network nodes, surface of the geographical area), they do not allow to easily select the level of details of different parts of the simulation, forcing the user to over-simplify the model.…”

Section: Related Workmentioning

confidence: 99%

The quest for scalability and accuracy: Multi-level simulation of the Internet of Things

Ferretti¹,

D’Angelo²,

Ghini³

et al. 2017

2017 IEEE/ACM 21st International Symposium on Distributed Simulation and Real Time Applications (DS-RT)

View full text Add to dashboard Cite

This paper presents a methodology for simulating the Internet of Things (IoT) using multi-level simulation models. With respect to conventional simulators, this approach allows us to tune the level of detail of different parts of the model without compromising the scalability of the simulation. As a use case, we have developed a two-level simulator to study the deployment of smart services over rural territories. The higher level is base on a coarse grained, agent-based adaptive parallel and distributed simulator. When needed, this simulator spawns OMNeT++ model instances to evaluate in more detail the issues concerned with wireless communications in restricted areas of the simulated world. The performance evaluation confirms the viability of multi-level simulations for IoT environments.

show abstract

“…mobile users of vehicles) that can interact with static ones. These tools have been successfully exploited to study intelligent traffic control systems [7], [29], [48], [51], mobile applications that resort to crowdsensed data [44] and so on. The main problem of these approaches is that, due to their nature, they do not allow creating massive scenarios, with many interconnections.…”

Section: B Internet Of Things and Smart-territoriesmentioning

confidence: 99%

Simulation of the Internet of Things

D’Angelo

Ferretti

Ghini

2016

2016 International Conference on High Performance Computing &Amp; Simulation (HPCS)

View full text Add to dashboard Cite

This paper presents main concepts and issues concerned with the simulation of Internet of Things (IoT). The heterogeneity of possible scenarios, arising from the massive deployment of an enormous amount of sensors and devices, imposes the use of sophisticated modeling and simulation techniques. In fact, the simulation of IoT introduces several issues from both quantitative and qualitative aspects. We discuss novel simulation techniques to enhance scalability and to permit the real-time execution of massively populated IoT environments (e.g., large-scale smart cities). In particular, we claim that agent-based, adaptive Parallel and Distributed Simulation (PADS) approaches are needed, together with multi-level simulation, which provide means to perform highly detailed simulations, on demand. We present a use case concerned with the simulation of smart territories.Comment: Proceedings of the IEEE 2016 International Conference on High Performance Computing and Simulation (HPCS 2016

show abstract

Sensor modeling and demonstration of a multi-object spectrometer for performance-driven sensing

Cited by 12 publications

References 17 publications

Feature Matching With an Adaptive Optical Sensor in a Ground Target Tracking System

Feature Matching With an Adaptive Optical Sensor in a Ground Target Tracking System

The quest for scalability and accuracy: Multi-level simulation of the Internet of Things

Simulation of the Internet of Things

Contact Info

Product

Resources

About