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Chemical sensing of explosives may allow differentiation between mines and other mine-like objects, especially if close proximity to the targets can be achieved. Robotic crawlers are well suited to achieve the required proximity and have added advantages, including stability on the bottom and station-keeping. We have performed initial tests with an explosive sensor mounted on crawlers and two types of targets containing real explosives. In the tests, the robot successfully detected both targets at significant distances. For the initial test, the crawler approached the target from a direction upcurrent of the target so that any chemical signature emanating from the target would be transported away from the sensor. No sensor response was noted in this case. The robot was then repositioned by executing a number of turns placing the robot and sensor downcurrent from the target. Shortly after arriving in this position, intermittent sensor responses were observed. These responses were similar to what is observed in the laboratory. The response to the targets was rapid and reversible. In order to gain insight into how most effectively to sample the area around mine-like objects, we are also simulating chemical orientation using spatial modeling and analysis tools.

Time frequency representations characterize signals in the time-frequency plane and can aid in the physical interpretation of backscattered chirps. However, these 2-D representations force trade-offs in resolution, computational efficiency, noise reduction, and cross-term generation. We are mainly concerned with the ability of the transform to reject noise, but we are also concerned with the effect of the cross-term artifacts that do not represent real phenomena on the ability to classify targets using machine learning approaches. First, strong backscatter returns available for a particular target were identified. In the cluttered field under study, this was accomplished from a synthetic aperture image of the field. The processing reported herein used these returns from each studied target to produce a partition of the time frequency plane that contained disjoint sets of time-frequency regions. Each target was represented by a subset of these regions. To test the separability of the targets based on these subsets, random pings were selected from each target’s high backscatter region. These pings were then compared against the disjoint sets. This procedure was carried out with several different time-frequency methods in order to determine the importance of the method’s resolution, noise reduction quality, and cross-term artifacts, in producing robust time-frequency plane partitions that may be used to classify targets. [This work was sponsored by the SERDP.]

There is growing concern over the impact of human intrusion into the habitat of certain wild animal species. A major part of this intrusion is in the form of noise from moving vehicles. The level on the ground or underwater caused by moving noise sources has been dealt with as single-event intrusions that may cause startle and associated physiological responses, and as cumulative noise exposures. The later approach allows correlation between the cumulative noise exposure of the whole animal population, and the change in population numbers and overall health. Currently, the most difficult part of this analysis lies in determining the sound exposure of the population since both the animals and the noise sources are spatially and temporally varying. There is a certain amount of knowledge about the movement of both the noise sources and the population; this knowledge can be used to create a kinematic simulation of the motions of both entities. Such a simulation has been used to yield long-term spatial probability distributions of noise sources that can then be superimposed over similarly obtained distributions of the population. This superimposition yields the required estimates of the total noise exposure of the population.

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