Passive multispectral sensor architecture for radar-EOIR sensor fusion for low SWAP UAS sense and avoid

Barott, William C.; Coyle, Eric; Dabrowski, Ted; Hockley, Christopher J.; Stansbury, Richard S.

doi:10.1109/plans.2014.6851491

Cited by 8 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are many methods for using RF data, including both active and passive usage. In [ 23 ], support vector machine (SVM) is used as a final method of classification to achieve sense-and-avoid for unmanned aircraft. The use of an autoencoder-based dynamic deep directional unit network [ 24 ] was capable of learning compact and abstract feature representations from high-dimensional spatiotemporal data of full motion video and I/Q data for the purposes of event behavior characterization.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The use of an autoencoder-based dynamic deep directional unit network [ 24 ] was capable of learning compact and abstract feature representations from high-dimensional spatiotemporal data of full motion video and I/Q data for the purposes of event behavior characterization. Other research into achieving EO/RF fusion for vehicle tracking and detection using Full Motion Video and P-RF includes joint manifold learning [ 25 ], a sheaf-based approach with its data [ 26 ], and SVM classifier [ 23 ]. In [ 25 , 26 ], simulation data were used as the primary method of training and testing, while in [ 23 ] real data were used.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Other research into achieving EO/RF fusion for vehicle tracking and detection using Full Motion Video and P-RF includes joint manifold learning [ 25 ], a sheaf-based approach with its data [ 26 ], and SVM classifier [ 23 ]. In [ 25 , 26 ], simulation data were used as the primary method of training and testing, while in [ 23 ] real data were used. In [ 25 ], a joint manifold learning fusion approach was used for mixed simulation data using DIRSIG-generated data.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Finding Explanations in AI Fusion of Electro-Optical/Passive Radio-Frequency Data

Vakil

Blasch

Ewing

et al. 2023

Sensors

View full text Add to dashboard Cite

In the Information Age, the widespread usage of blackbox algorithms makes it difficult to understand how data is used. The practice of sensor fusion to achieve results is widespread, as there are many tools to further improve the robustness and performance of a model. In this study, we demonstrate the utilization of a Long Short-Term Memory (LSTM-CCA) model for the fusion of Passive RF (P-RF) and Electro-Optical (EO) data in order to gain insights into how P-RF data are utilized. The P-RF data are constructed from the in-phase and quadrature component (I/Q) data processed via histograms, and are combined with enhanced EO data via dense optical flow (DOF). The preprocessed data are then used as training data with an LSTM-CCA model in order to achieve object detection and tracking. In order to determine the impact of the different data inputs, a greedy algorithm (explainX.ai) is implemented to determine the weight and impact of the canonical variates provided to the fusion model on a scenario-by-scenario basis. This research introduces an explainable LSTM-CCA framework for P-RF and EO sensor fusion, providing novel insights into the sensor fusion process that can assist in the detection and differentiation of targets and help decision-makers to determine the weights for each input.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Finding Explanations in AI Fusion of Electro-Optical/Passive Radio-Frequency Data

Vakil

Blasch

Ewing

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Ground-based receivers exploiting the illumination offered by transmitters of opportunity can be employed as a gap-fill/back-up technology on specific airspaces and in particular scenarios (e.g., low altitude surveillance) [32,33]. Moreover, the idea of applying passive radar on board aircraft has been investigated, to protect the platform from collisions and other threats [34,35]. Most studies have considered the use of ground-based emitters, such as radio/TV broadcasting, [36,37].…”

Section: Passive Radar Concept For Air Surveillancementioning

confidence: 99%

Parasitic Surveillance Potentialities Based on a GEO-SAR Illuminator

et al. 2021

View full text Add to dashboard Cite

Synthetic aperture radar systems operating with satellites in geosynchronous orbits (GEO-SAR) can provide a permanent coverage of wide specific areas of the Earth’s surface. As well as for primary applications in remote sensing areas such as soil moisture and deformation monitoring, the wide availability of the signal emitted by a GEO-SAR on a regional scale makes it an appealing illuminator of opportunity for bistatic radars. Different types of receiving-only devices located on or near the Earth could exploit the same signal source, noticeably already conceived for radar purposes, for applications in the framework of both military and civil surveillance. This paper provides an overview of possible parasitic applications enabled by a GEO-SAR illuminator in different operative scenarios, including aerial, ground and maritime surveillance. For each selected scenario, different receiver configurations are proposed, providing an assessment of the achievable performance with discussions about the expected potentialities and challenges. This research aims at serving as a roadmap for designing parasitic systems relying on GEO-SAR signals, and also aims at extending the net of potential users interested in investing in GEO-SAR missions.

show abstract

“…However these systems show some limitations due to the complexity of filtering the sounds generated by the UAV itself. Another passive approach with a lot of potential is passive radar, which uses the reflections of RF signals already existing in the environment to detect the presence of other flying objects [6]. Its main Fig .…”

Section: Introductionmentioning

confidence: 99%

SIGS: Synthetic Imagery Generating Software for the Development and Evaluation of Vision-based Sense-And-Avoid Systems

Carrio

Collumeau

et al. 2015

J Intell Robot Syst

View full text Add to dashboard Cite

Unmanned Aerial Systems (UASs) have recently become a versatile platform for many civilian applications including inspection, surveillance and mapping. Sense-and-Avoid systems are essential for the autonomous safe operation of these systems in non-segregated airspaces. Vision-based Sense-andAvoid systems are preferred to other alternatives as their price, physical dimensions and weight are more suitable for small and medium-sized UASs, but obtaining real flight imagery of potential collision scenarios is hard and dangerous, which complicates the development of Vision-based detection and tracking algorithms. For this purpose, user-friendly software for synthetic imagery generation has been developed, allowing to blend user-defined flight imagery of a simulated aircraft with real flight scenario images to produce realistic images with ground truth annotations. These are extremely useful for the development and benchmarking of Vision-based detection and tracking algorithms at a much lower cost and risk. An image processing algorithm has also been developed for automatic detection of the occlusions caused by certain parts of the UAV which carries the camera. The detected occlusions can later be used by our software to simulate the occlusions due to the UAV that would appear in a real flight with the same camera setup. Additionally this algorithm could be used to mask out pixels which do not contain relevant information of the scene for the visual detection, making the image search process more efficient. Finally an application example of the imagery obtained with our software for the benchmarking of a state-of-art visual tracker is presented.

show abstract

Passive multispectral sensor architecture for radar-EOIR sensor fusion for low SWAP UAS sense and avoid

Cited by 8 publications

References 18 publications

Finding Explanations in AI Fusion of Electro-Optical/Passive Radio-Frequency Data

Finding Explanations in AI Fusion of Electro-Optical/Passive Radio-Frequency Data

Parasitic Surveillance Potentialities Based on a GEO-SAR Illuminator

SIGS: Synthetic Imagery Generating Software for the Development and Evaluation of Vision-based Sense-And-Avoid Systems

Contact Info

Product

Resources

About