Software tools will be developed that define the underlying phenomenology of greatest importance to radar CID performance, allowing a trace back to the responsible target scattering physics. The proposed research entails development of algorithms that identify the target-sensor orientations (poses)… More

Information-exploitation algorithms are proposed for Air Force sensing and weapons systems, motivated by the inevitable variability and differences seen between conventional training and testing data. Adaptive feedback is proposed, yielding an active-learning framework, wherein the algorithm… More

Signal Innovations Group (SIG) and its Fast Track partner, Integrian (www.integrian.com), propose development of an adaptive video sensing system, with a focus on intelligence, surveillance, and reconnaissance (ISR). The proposed adaptive system is applicable for ISR in general environments,… More

State-of-the-art radar combat ID (CID) must address target identification in complex environments, often with very limited measured training data. Consequently, the algorithms frequently rely on data computed via numerical models, as a function of required sensor conditions (bandwidth,… More

We seek to address limitations of conventional 2D SAR imaging by investigating a new class of 3D imaging techniques that exploit the complicated non-planar relative rotation between a target and radar system. The proposed technique builds upon fundamental work on invariants from range-dependent… More

There are numerous targets of interest to the Air Force that may be manifested in many different forms. For example, many ground targets may be configured with or without particular subcomponents, and such subcomponents may also be situated in different locations on a given target. Moreover, the… More

A research program is proposed on the integration of signal processing and electromagnetic modeling, to address the problem of performing ATR with targets possessing a high degree of variability. Sparseness is employed from two perspectives. First, in the signal processing component, sparse… More

We propose a principled in situ learning framework that is appropriate for a Bayesian classifier implemented with semi-supervised and multi-task learning. We will investigate several different forms of in situ learning, and will perform testing on measured data to help define which is most… More

The proliferation of landmines continues to be a problem of worldwide humanitarian urgency. While airborne sensors have demonstrated significant utility in covering a wide area at a high stand-off distance, the variety of deployment methods, environments, mine types, and operating conditions… More

Automatic target recognition(ATR) for tactical military targets is a very challenging problem, particularly when the number of observed aspects of a target is subject to the operational constraints of the sensor platform. When the target is situated in the presence of a complicated background… More

In the proposed Phase II program, the methods developed and implemented during Phase I research will be fully integrated within a common Bayesian in situ learning framework. We have developed several Bayesian classifiers, to which we will apply label acquisition and label confidence techniques.… More

A probabilistic ATR framework is proposed to exploit coincident multi-aspect radar and EO video data for target detection, tracking, and classification/identification. The mathematical framework is constituted by four principal components with a particular focus on exploitation of 3D information… More

The proposed Phase I program will develop a Bayesian framework for modeling and predicting track uncertainty and performance as a function of OCs and integrate this framework with algorithms for tracking vehicles in WAMI. The proposed Performance and Uncertainty Modeling and Prediction for Tracking… More

Situational awareness is paramount when conducting operations in potentially hostile terrain. Vehicle operators must focus on multiple tasks simultaneously: navigation, collision avoidance, location of possible IED threats, and identification of dismount threats. Fatigue and distractions affect… More

Fully automated approaches to target detection are efficient at processing large amounts of data but often rely heavily on the training data and the model employed. Training data and modeling assumptions can be violated in the operational environments where the algorithms are applied. Alternatively,… More

ABSTRACT: The Air Force has invested considerable resources into collecting and synthesizing radar data for training and testing ATC/R systems. Significant cost savings may be realized if these existing datasets may be leveraged for training new sensors and modalities. Signal Innovations Group… More