Tracking individuals in areas such as dense urban environments and building interiors is desirable for numerous critical applications, but has been problematic mainly because of the unreliability or unavailability of GPS in many locations of interest. To date, tracking applications that utilize inertial sensors within smart devices have had varied degrees of success: accuracy typically dips below that of standard GPS within minutes and depends strongly on the quality of the sensors in the device, as well as the location that the device is carried on the body. In this paper we present a sensor module that interfaces with modern smart devices and which utilizes a low-cost, commercial-off-the-shelf, 9-axis IMU and pressure sensor to provide an advanced pedestrian dead reckoning solution. The sensor module is designed to communicate with the smart device (e.g., iOS, Android or Windows) via the audio jack and is intended for use as a beltmounted pedestrian tracker. In addition to describing the device hardware and functionality, we present our approach to processing the sensor module data streams to determine a user’s position. Results using the prototype sensor module in operationally relevant scenarios is presented and discussed.
Crude oil spills in the marine environment result in spatially variable slicks, with up to 90% of the oil contained in less than 10% of the slick area. Rapid slick containment and cleanup is in the interest of all stakeholders and can be best accomplished by focusing efforts on the thickest regions of the slick. An instrument for estimating oil slick thickness would expedite the cleanup process and offers the potential to minimize a spills’ environmental impact. In this work, we have experimented using infrared (IR) spectroscopy and pattern recognition algorithms to discriminate thin and thick regions of an oil slick. Fourier transform-IR (FT-IR) spectra of five crude oils and one refined oil at varying thicknesses on water were collected at short standoff in a laboratory setting. The strong C-H stretching absorbances near 3000 cm<sup>-1</sup> and 1500 cm<sup>-1</sup> proved most useful for discriminating oil thickness. Several techniques for signal representation and discrimination were explored in attempt to classify spectra as thin or thick, where “thick” was defined as greater than a predetermined thickness threshold. Although a discrimination approach using Principal Component Analysis and artificial neural networks was most efficient, a template matching approach provided slightly better performance. Thick oil slicks were determined with 95% probability of detection (P<sub>d</sub>) and 5% probability of false alarm (P<sub>fa</sub>) when the oil was contained in the template matching database (88% P<sub>d</sub> with 15% P<sub>fa</sub> when the oil was not in the database). The system’s overall performance varied with the predetermined thickness threshold, with 100 μm producing the best results.
Conference Committee Involvement (1)
Geospatial Informatics, Fusion, and Motion Video Analytics VI
19 April 2016 | Baltimore, Maryland, United States