The Ball Aerospace Pipeline Damage Prevention Radar (PDPR) project evaluated the use of airborne synthetic aperture radar (SAR) to detect vehicles and equipment located within buried pipeline right-of-way areas but obscured from visual detection. The project included the configuration of a commercial dual-band SAR/EO system for airborne operations, hardware and software modifications to optimize SAR change detection processing, and the execution of multiple flight tests to characterize SAR performance for the detection of equipment obscured by vegetation. Flight tests were conducted in 2016 and 2017 using X-band, Ku-band and ultra-wide band (UWB) SAR in urban and rural environments. Targets in the open showed close to 100% detection performance while covered target results depended on the amount of vegetative canopy. Detection "through" vegetation was generally better using the UWB system, but vegetation gaps frequently allowed higher spatial resolution detections with the Ku-band system. While large equipment was frequently identifiable in the Ku-band SAR images, having coincident EO imagery proved critical for context and automated deep learning based object identification. The detection performance difference between open and covered conditions clearly illustrates how a collection plan that optimizes open viewing conditions increases the overall probability of detection. This research was performed in response to the Damage Prevention topic through the Technology Development in the Pipeline Safety Research and Development Announcement DTPH5615RA00001.
Due to the highly stringent requirements on planet-detecting nulling
interferometers, many approximations made for standard imaging systems are no longer valid. Analyses using scalar electric fields must be modified to employ vector (polarized) electric fields. In this paper, we present definitions of Stokes-related vectors, Mueller matrices, and the responses (scalar and vector) of single- and dual- Bracewell instruments. We study systematic errors due to instrumental polarization, discussing mismatched elliptically polarized arms, misaligned mirror trains, and beam non-uniformities. Also, we consider systematic errors due to interstellar polarization and polarized starspots. Last, we briefly discuss ancillary science projects that are possible with a space-based interferometer and polarimeter.
A maximum likelihood estimation (MLE) method for simultaneously retrieving wind and rain from SeaWinds scatterometer data is introduced and evaluated. The new method incorporates rain backscatter and attenuation into the retrieval process via a simple wind/rain backscatter model. Two retrieval methods are examined: First, when no estimate of the rain rate is available, the new MLE method simultaneously estimates wind speed, wind direction and rain rate. Second, when an estimate of the rain is available, the wind is retrieved by directly correcting the geophysical model function using the rain/wind backscatter model. From simulation, the simultaneous wind/rain retrieval approach demonstrates improved wind vector estimates where the rain is significant. The improvement in retrieval is more pronounced in the “sweet spot” of SeaWinds’ cross track. The rain-corrected wind retrieval approach gives somewhat improved wind speed estimates for rain-contaminated wind vectors over the simultaneous wind/rain retrieval method, especially when the effect of rain is small. Validation of the SeaWinds rain data with co-located Tropical Rainfall Measuring Mission precipitation radar rain rates shows that with some limitations the SeaWinds scatterometer can measure rain.
The latest spaceborne scatterometer, SeaWinds on QuikSCAT, estimates near-surface ocean winds at 25 km resolution over the entire globe. The scatterometer wind retrieval process generates several possible wind vector choices or ambiguities at each resolution cell. Routines for selecting a unique wind vector field are generally ad hoc and error prone. In order to assess SeaWinds ambiguity selection and spatial consistency of retrieved winds, a quality assurance (QA) algorithm is presented based on comparing ambiguity-selected winds to a low-order wind field model fit. Regions exceeding error thresholds are rated according to spatial consistency and flagged as possible ambiguity selection errors. Appropriate error thresholds and additional flagging criteria are set through an analysis of false alarms versus missed detections on a manually-inspected training data set. The QA algorithm correctly identifies 97% of the manually flagged regions with a false alarm rate of less than 2%. Applying the algorithm to 16 months of QuikSCAT wind data, we conclude that SeaWinds ambiguity selection is over 95% effective on regions of rms wind speed greater than 3.5 m/s. The QA algorithm indicates that higher noise occurs at nadir and in areas of low wind speed. additionally, fewer estimated ambiguity selection errors occur at nadir and on the swath edges due to a larger ambiguity set in those regions. The percentage of ambiguity selection errors are found to be highly correlated with the number of cyclonic storms passed by SeaWinds and the percentage of wind vector cells corrupted by rain.