: We present a framework for forensic identification of RF devices using specially designed probe signals. This framework applies to a broad range of devices and models. Probe signals, device models, feature selection, and classifier design are described. For the device model, we introduce a method for determining a nonlinearity based on a known diode model. Experimental results are given to verify our approach.

Remote detection and characterization of wireless devices in an environment is a topic of growing importance. Characterization of a wireless device is useful in many applications. An example of this is in the testing of FCC Part 15 devices. These devices must adhere to strict guidelines in regards to RF interference. Compliance can be verified by using forensic techniques to classify and characterize the returned signal. We present a framework for remote detection and forensic characterization of RF devices using specially designed probe signals. This framework can be applied to a broad range of devices and models. Probe signals, device models, feature selection, classifier design are described. For the device model we introduce a method for simulating a non-linearity in the RF system based on a known diode model. Experimental results are given to verify our approach.

Many emergency response units are currently faced with restrictive budgets that prohibit their use of advanced technology-based training solutions. Our work focuses on creating an affordable, mobile, state-of-the-art emergency response training solution through the integration of low-cost, commercially available products. The system we have developed consists of tracking, audio, and video capability, coupled with other sensors that can all be viewed through a unified visualization system. In this paper we focus on the video sub-system which helps provide real time tracking and video feeds from the training environment through a system of wearable and stationary cameras. These two camera systems interface with a management system that handles storage and indexing of the video during and after training exercises. The wearable systems enable the command center to have live video and tracking information for each trainee in the exercise. The stationary camera systems provide a fixed point of reference for viewing action during the exercise and consist of a small Linux based portable computer and mountable camera. The video management system consists of a server and database which work in tandem with a visualization application to provide real-time and after action review capability to the training system.

We present a framework for forensic identification of RF devices using specially designed probe signals. This framework applies to a broad range of devices. Probe signals, device models, feature selection and classifier design are described, and experimental results are given to verify our approach.

Given the wide use of Radio Frequency (RF) devices for applications ranging from data networks to wireless sensors, it is of interest to be able to characterize individual devices to verify compliance with FCC Part 15 rules. In an effort to characterize these types of devices we have developed a system that utilizes specially designed probe signals to elicit a response from the device from which unique characteristics can be extracted. The features that uniquely characterize a device are referred to as device signatures or device fingerprints. We apply this approach to RF devices which employ different bandpass filters, and construct training based classifiers which are highly accurate. We also introduce a model-based framework for optimal detection that can be employed to obtain performance limits, and to study model mismatch and probe optimization.

Many emergency response units are currently faced with restrictive budgets which prohibit their use of technology both in training and in real-world situations. Our work focuses on creating an affordable, mobile, state-of-the-art emergency response test-bed through the integration of low-cost, commercially available products. We have developed a command, control, communications, surveillance and reconnaissance system that will allow small-unit exercises to be tracked and recorded for evaluation purposes. Our system can be used for military and first responder training providing the nexus for decision making through the use of computational models, advanced technology, situational awareness and command and control. During a training session, data is streamed back to a central repository allowing commanders to evaluate their squads in a live action setting and assess their effectiveness in an after-action review. In order to effectively analyze this data, an interactive visualization system has been designed in which commanders can track personnel movement, view surveillance feeds, listen to radio traffic, and fast-forward/rewind event sequences. This system provides both 2-D and 3-D views of the environment while showing previously traveled paths, responder orientation and activity level. Both stationary and personnel-worn mobile camera video feeds may be displayed, as well as the associated radio traffic.