With recent advances in both algorithm and component technologies, THrough Wall At RF (THWARF) is emerging as an affordable sensor technology supporting a variety of applications, such as emergency rescue and firefighting. Due to the complexities associated with the development of successful through-wall systems, commercial offerings of through-wall technology have been limited to date. THWARF sensing presents numerous technical challenges including a less-than-cooperative propagation environment, (often) ad hoc antenna deployments, and inhomogeneities and non-stationarities in both the background environment and target set, thereby rendering through-wall imaging and sensing a difficult proposition.
While through-wall addresses a number of practical problems, it is dual-use with obvious military applications. This, combined with intellectual property issues, tends to reduce the incentive to collaborate and socialize ideas, and generally stifles innovation in the field. It is our contention that system development suffers and sub-optimal solutions emerge from less-than-systematic development and evaluation processes. Although many of the institutional problems will likely never be overcome, development efforts can be improved by standardizing at some levels. At present, it is difficult to compare and evaluate systems in a consistent manner. The community lacks agreed upon performance metrics and standardized test scenes. In addition, unlike other communities such as speech, adaptive array processing, and automatic target recognition, the through-wall community lacks benchmark datasets. Benchmark datasets could facilitate the standardization of performance metrics and the development and comparison of algorithms and system concepts.
CACs Through-The-Wall Imaging Experiments
In an attempt to increase the coherence of algorithm and technology development and evaluation relevant to TWARF sensing, the Center for Advanced Communications (CAC) at Villanova University has conducted several preliminary through-the-wall imaging experiments and collected datasets under the supervision of Defense Advanced Research Project Agency (DARPA) and in collaboration with the Air Force Research Laboratory (AFRL). The datasets were collected in general engineering work space at the University that has been lined with radar absorbing material. Data is collected with largely off-the-shelf equipment including an Agilent network analyzer, Model ENA 5071B.
The datasets include free-space and through-wall collections for three different arrangements of the room's contents: empty scene, calibration scene, and populated scene. The empty scene allows measurement of the noise/clutter background and supports coherent subtraction with the other two scenes. The calibration scene contains isolated reflectors that may be used to determine a fully-polarimetric radiometric calibration solution for the experimental system. The populated scene contains a number of common objects such as a phone, computer, tables, chair and filing cabinet. In addition, a jug of saline solution has been added to crudely approximate a human.
The wall is composed of plywood and gypsum board on a wooden frame. Two horn antennas are mounted on a 2D scanner that moves the antennas along and adjacent to the wall and is controlled by the network analyzer. Two additional antennas are fixed to the scanner frame and act as bistatic receivers. Other attributes of the data include a 1 GHz bandwidth stepped-frequency waveform centered at 2.5 GHz and a two-dimensional synthetic aperture, 49" on a side, with a sample spacing of 0.875" on a square grid. For more information, please view the detailed description of the RF system and experimental conditions.
The datasets are admittedly sterile and somewhat naïve as they relate to problems encountered in real-world applications. The datasets represent a necessary compromise given the potential military utility of the technology. We hope that others will consider both natural and novel extensions to our work.