A collaborative engagement system comprises: at least two unmanned vehicles comprising an unmanned air vehicle including sensors configured to locate a target and an unmanned ground vehicle including sensors configured to locate and track a target; and a controller facilitating control of, and communication and exchange of data to and among the unmanned vehicles, the controller facilitating data exchange via a common protocol. The collaborative engagement system controls the unmanned vehicles to maintain line-of-sight between a predetermined target and at least one of the unmanned vehicles.

Collaborative Engagement for Target Identification and Tracking

In this paper, several methods to register and stabilize a motion imagery video sequence under the layered sensing concept are evaluated Utilizing the layered sensing paradigm, an area is surveyed by a multitude of sensors at many different altitudes and operating across many modalities Utilizing a combination of sensors provides better insight into a situation than could ever be achieved with a single sensor A fundamental requirement in layered sensing is to first register, stabilize, and normalize the data from each of the individual sensors This paper extends our previous work [1] to include experimental analysis The paper contribution provides an evaluation of four registration algorithms now including the (1) Lucas-Kanade (LK) algorithm, (2) the Ohio State University (OSU)1 correlation-based method, (3) robust data alignment (RDA), and (4) Scale Invariant Feature Transform (SIFT) Results demonstrate that registration accuracy and robustness were achieved with the LK and correlation-based methods over the others for image-to-image registration, restricted adaptive tuning, and stabilization over warped images; while the SIFT outperformed the others for partial image overlap

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Video image registration evaluation for a layered sensing environment

New technology presents opportunities for enhancing the performance of human systems that are tasked to meet multiple, often competing demands. Yet, mistakes in designing and deploying these technologies can create complexities that make human systems more brittle. To many stakeholders, the answer to this challenge is increase situation awareness. But what these advocates refer to when they talk about enhancing situation awareness varies tremendously. Over 15 years ago, the authors commented on how the label was ill defined. Today, the label is more popular than ever but the range of situations and the kinds of awareness are now so diverse that the label is better referred to as multiply defined. This paper returns to basic concepts and findings about human perception and the control of attention and the critical role that these processes play in individual as well as joint and distributed activity–how people know where to focus next in changing situations. This paper also briefly reviews recent studies o...

Capturing the dynamics of attention control from individual to distributed systems: the shape of models to come

: Radar systems provide an important remote sensing capability, and are crucial to the layered sensing vision; a concept of operation that aims to apply the right number of the right types of sensors, in the right places, at the right times for superior battle space situational awareness. The layered sensing vision poses a range of technical challenges, including radar, that are yet to be addressed. To address the radar-specific design challenges, the research community responded with waveform diversity; a relatively new field of study which aims reduce the cost of remote sensing while improving performance. Early work suggests that the frequency diverse array radar may be able to perform several remote sensing missions simultaneously without sacrificing performance. With few techniques available for modeling and characterizing the frequency diverse array, this research aims to specify, validate and characterize a waveform diverse signal model that can be used to model a variety of traditional and contemporary radar configurations, including frequency diverse array radars. To meet the aim of the research, a generalized radar array signal model is specified. A representative hardware system is built to generate the arbitrary radar signals, then the measured and simulated signals are compared to validate the model. Using the generalized model, expressions for the average transmit signal power, angular resolution, and the ambiguity function are also derived. The range, velocity and direction-of-arrival measurement accuracies for a set of signal configurations are evaluated to determine whether the configuration improves fundamental measurement accuracy.

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Frequency Diverse Array Radar: Signal Characterization and Measurement Accuracy

: A new theory and algorithm for scatterer classification in SAR imagery is presented. The automated classification process is operationally efficient compared to existing image segmentation methods requiring human supervision. The algorithm reconstructs coarse resolution subimages from subdomains of the SAR phase history. It analyzes local peaks in the subimages to determine locations and geometric shapes of scatterers in the scene. Scatterer locations are indicated by the presence of a stable peak in all subimages for a given subaperture, while scatterer shapes are indicated by changes in pixel intensity. A new multi-peak model is developed from physical models of electromagnetic scattering to predict how pixel intensities behave for different scatterer shapes. The algorithm uses a least squares classifier to match observed pixel behavior to the model. Classification accuracy improves with increasing fractional bandwidth and is subject to the high-frequency and wide-aperture approximations of the multi-peak model. For superior computational efficiency, an integrated fast SAR imaging technique is developed to combine the coarse resolution subimages into a final SAR image having fine resolution. Finally, classification results are overlaid on the SAR image so that analysts can deduce the significance of the scatterer shape information within the image context.

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Phase history decomposition for efficient scatterer classification in SAR imagery

: The changing face of warfare and it migration from large state-on-state conflicts to Irregular Warfare is dramatically changing the requirements laid down by the Department of Defense for acquiring and disseminating information. Creating "Situational Awareness" and information for all levels of decision makers from the President to the Marine on the ground will require significant changes. In response, The Air Force Research Laboratory has established an urgent and focused long-term strategy to improve Situational Awareness technologies. This document helps to outline that strategy by defining the context of a new technology construct called "Layered Sensing." This report fully defines the attributes and context of "Layered Sensing" and describes its implications for driving AFRL research. AFRL recognizes that this document represents a living, evolving construct. This document will be periodically reviewed, revised, and re-released as the concept evolves.

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Brian M. Kent

Papers

Layered Sensing: Its Definition, Attributes, and Guiding Principles for AFRL Strategic Technology Development