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.
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 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.