Tracking a non-cooperative target using a Doppler radar wireless sensor network

Tracking or target localization is used in a wide range of important tasks from knowing when your flight will arrive to ensuring your mail is received on time. Tracking provides the location of resources enabling solutions to complex logistical problems. Wireless Sensor Networks (WSN) create new opportunities when applied to tracking, such as more flexible deployment and real-time information. When radar is used as the sensing element in a tracking WSN better results can be obtained; because radar has a comparatively larger range both in distance and angle to other sensors commonly used in WSNs. This allows for less nodes deployed covering larger areas, saving money. In this report I implement a tracking WSN platform similar to what was developed by Lim, Wang, and Terzis. This consists of several sensor nodes each with a radar, a sink node connected to a host PC, and a Matlab© program to fuse sensor data. I have re-implemented their experiment with my WSN platform for tracking a non-cooperative target to verify their results and also run simulations to compare. The results of these tests are discussed and some future improvements are proposed.

GROUND-BASED AND SATELLITE REMOTE SENSING OF PAROXYSMAL ERUPTIONS AT ETNA VOLCANO, 2011-2012.

Mt Etna's activity has increased during the last decade with a tendency towards more explosive eruptions that produce paroxysmal lava fountains. From January 2011 to April 2012, 25 lava fountaining episodes took place at Etna's New South-East Crater (NSEC). Improved understanding of the mechanism driving these explosive basaltic eruptions is needed to reduce volcanic hazards. This type of activity produces high sulfur dioxide (SO2) emissions, associated with lava flows and ash fall-out, but to date the SO2 emissions associated with Etna's lava fountains have been poorly constrained. The Ultraviolet (UV) Ozone Monitoring Instrument (OMI) on NASA's Aura satellite and the Atmospheric Infrared Sounder (AIRS) on Aqua were used to measure the SO2 loadings. Ground-based data from the Observatoire de Physique du Globe de Clermont-Ferrand (OPGC) L-band Doppler radar, VOLDORAD 2B, used in collaboration with the Italian National Institute of Geophysics and Volcanology in Catania (INGV-CT), also detected the associated ash plumes, giving precise timing and duration for the lava fountains. This study resulted in the first detailed analysis of the OMI and AIRS SO2 data for Etna's lava fountains during the 2011-2012 eruptive cycle. The HYSPLIT trajectory model is used to constrain the altitude of the observed SO2 clouds, and results show that the SO2 emission usually coincided with the lava fountain peak intensity as detected by VOLDORAD. The UV OMI and IR AIRS SO2 retrievals permit quantification of the SO2 loss rate in the volcanic SO2 clouds, many of which were tracked for several days after emission. A first attempt to quantitatively validate AIRS SO2 retrievals with OMI data revealed a good correlation for high altitude SO2 clouds. Using estimates of the emitted SO2 at the time each paroxysm, we observe a correlation with the inter-paroxysm repose time. We therefore suggest that our data set supports the collapsing foam (CF) model [1] as driving mechanism for the paroxysmal events at the NSEC. Using VOLDORAD-based estimates of the erupted magma mass, we observe a large excess of SO2 in the eruption clouds. Satellite measurements indicate that SO2 emissions from Etnean lava fountains can reach the lower stratosphere and hence could pose a hazard to aviation. [1] Parfitt E.A (2004). A discussion of the mechanisms of explosive basaltic eruptions. J. Volcanol. Geotherm. Res. 134, 77-107.

Detection of complex point targets with distributed assets in a MIMO radar system

The report explores the problem of detecting complex point target models in a MIMO radar system. A complex point target is a mathematical and statistical model for a radar target that is not resolved in space, but exhibits varying complex reflectivity across the different bistatic view angles. The complex reflectivity can be modeled as a complex stochastic process whose index set is the set of all the bistatic view angles, and the parameters of the stochastic process follow from an analysis of a target model comprising a number of ideal point scatterers randomly located within some radius of the targets center of mass. The proposed complex point targets may be applicable to statistical inference in multistatic or MIMO radar system. Six different target models are summarized here – three 2-dimensional (Gaussian, Uniform Square, and Uniform Circle) and three 3-dimensional (Gaussian, Uniform Cube, and Uniform Sphere). They are assumed to have different distributions on the location of the point scatterers within the target. We develop data models for the received signals from such targets in the MIMO radar system with distributed assets and partially correlated signals, and consider the resulting detection problem which reduces to the familiar Gauss-Gauss detection problem. We illustrate that the target parameter and transmit signal have an influence on the detector performance through target extent and the SNR respectively. A series of the receiver operator characteristic (ROC) curves are generated to notice the impact on the detector for varying SNR. Kullback–Leibler (KL) divergence is applied to obtain the approximate mean difference between density functions the scatterers assume inside the target models to show the change in the performance of the detector with target extent of the point scatterers.

Estimation of scots pine defoliation by the common pine sawfly (Diprion pini L.) using multi-temporal radar data

In 1998-2001 Finland suffered the most severe insect outbreak ever recorded, over 500,000 hectares. The outbreak was caused by the common pine sawfly (Diprion pini L.). The outbreak has continued in the study area, Palokangas, ever since. To find a good method to monitor this type of outbreaks, the purpose of this study was to examine the efficacy of multi-temporal ERS-2 and ENVISAT SAR imagery for estimating Scots pine (Pinus sylvestris L.) defoliation. Three methods were tested: unsupervised k-means clustering, supervised linear discriminant analysis (LDA) and logistic regression. In addition, I assessed if harvested areas could be differentiated from the defoliated forest using the same methods. Two different speckle filters were used to determine the effect of filtering on the SAR imagery and subsequent results. The logistic regression performed best, producing a classification accuracy of 81.6% (kappa 0.62) with two classes (no defoliation, >20% defoliation). LDA accuracy was with two classes at best 77.7% (kappa 0.54) and k-means 72.8 (0.46). In general, the largest speckle filter, 5 x 5 image window, performed best. When additional classes were added the accuracy was usually degraded on a step-by-step basis. The results were good, but because of the restrictions in the study they should be confirmed with independent data, before full conclusions can be made that results are reliable. The restrictions include the small size field data and, thus, the problems with accuracy assessment (no separate testing data) as well as the lack of meteorological data from the imaging dates.