The US Army Combat Capabilities Development Command Army Research Laboratory is developing a dual-band, full-polarization, side-looking synthetic aperture radar using an RF system-on-a-chip for the detection of landmi...
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ISBN:
(纸本)9781510650930;9781510650923
The US Army Combat Capabilities Development Command Army Research Laboratory is developing a dual-band, full-polarization, side-looking synthetic aperture radar using an RF system-on-a-chip for the detection of landmines. The system employs two separate front-ends to operate in the bands from 0.5 to 1.8 GHz and from 2.1 to 3.8 GHz. An antenna array is set up with two transmitters (one vertical and one horizontal) and two receivers (one vertical and one horizontal) to enable fully-polarimetric operation. A continuous wave stepped-frequency waveform is employed, and each combination of polarizations is simultaneously transmitted and received. This system was tested at a desert site. The targets that were tested were remote anti-armor mine system landmines, M20 metal landmines, and VS2.2 plastic landmines. The targets are imaged under a number of emplacement scenarios so that imaging results address targets made of various materials at different orientations and ranges. Furthermore, obscured targets and buried targets are also investigated. The effect of antenna coupling and techniques for reducing this effect are discussed. Then, the imaging results for each target scenario is shown and analyzed. Imaging results between data from the two frequency bands are compared and the success of detection for different emplacements is analyzed.
Many companies rely on user experience metrics, such as Net Promoter scores, to monitor changes in customer attitudes toward their products. This paper suggests that similar metrics can be used to assess the user expe...
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ISBN:
(数字)9781510617827
ISBN:
(纸本)9781510617827
Many companies rely on user experience metrics, such as Net Promoter scores, to monitor changes in customer attitudes toward their products. This paper suggests that similar metrics can be used to assess the user experience of the pilots and sensor operators who are tasked with using our radar, EO/IR, and other remote sensing technologies. As we have previously discussed, the problem of making our national security remote sensing systems useful, usable and adoptable is a human-system integration problem that does not get the sustained attention it deserves, particularly given the high-throughput, information-dense task environments common to military operations. In previous papers, we have demonstrated how engineering teams can adopt well-established human-computer interaction principles to fix significant usability problems in radar operational interfaces. In this paper, we describe how we are using a combination of Situation Awareness design methods, along with techniques from the consumer sector, to identify opportunities for improving human-system interactions. We explain why we believe that all stakeholders in remote sensing - including program managers, engineers, or operational users - can benefit from systematically incorporating some of these measures into the evaluation of our national security sensor systems. We will also provide examples of our own experience adapting consumer user experience metrics in operator-focused evaluation of currently deployed radar interfaces.
Systems designed to detect the threat posed by drones should be able to both locate a drone and ideally determine its type in order to better estimate the level of threat. Previously, drone types have been discriminat...
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ISBN:
(纸本)9781510650930;9781510650923
Systems designed to detect the threat posed by drones should be able to both locate a drone and ideally determine its type in order to better estimate the level of threat. Previously, drone types have been discriminated using millimeter-wave Continuous Wave (CW) radar, which produces high quality micro-Doppler signatures of the drone propeller blades with fully sampled Doppler spectra. However, this method is unable to locate the target as it cannot measure range. By contrast, Frequency Modulated Continuous Wave (FMCW) data typically undersamples the micro-Doppler signatures of the blades but can be used to locate the target. In this paper we investigate FMCW features of four drones and if they can be used to discriminate the models using machine learning techniques, enabling both the location and classification of the drone. Millimeter-wave radar data are used for better Doppler sensitivity and shorter integration time. Experimentally collected data from Ttree quadcopters (DJI Phantom Standard 3, DJI Inspire 1, and Joyance JT5L-404) and a hexacopter (DJI S900) have been. For classification, feature extraction based machine learning was used. Several algorithms were developed for automated extraction of micro-Doppler strength, bulk Doppler to micro-Doppler ratio, and HERM line spacing from spectrograms. These feature values were fed to classifiers for training. The four models were classified with 85.1% accuracy. Higher accuracies greater than 95% were achieved for training using fewer drone models. The results are promising, establishing the potential for using FMCW radar to discriminate drone types.
This paper will describe measurements of snow reflection using laser radar. There seems to be a rather limited number of publications on snow reflection related to laser radar, which is why we decided to investigate a...
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ISBN:
(纸本)0819457760
This paper will describe measurements of snow reflection using laser radar. There seems to be a rather limited number of publications on snow reflection related to laser radar, which is why we decided to investigate a little more details of snow reflection including that from different kinds of snow as well as the angular reflection properties. We will discuss reflectance information obtained by two commercial scanning laser radars using the wavelengths 0.9 mu m and 1.5 mu m. Data will mainly be presented at the eye safe wavelength 1.5 mu m but some measurements were also performed for the wavelength 0.9 mu m. We have measured snow reflection during a part of a winter season which gave us opportunities to investigate different types of snow and different meteorological conditions. The reflection values tend to decrease during the first couple of hours after a snowfall. The snow structure seems to be more important for the reflection than the snow age. In general the snow reflection at 1.5 mu m is rather low and the reflectivity values can vary between 0.5 and 10% for oblique incidence depending on snow structure which in turn depends on age, air temperature, humidity etc. The snow reflectivity at the 0.9 mu m laser wavelength is much higher, more than 50% for fresh snow. Images of snow covered scenes will be shown together with reflection data including BRDFs.
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