An Algorithm for Automatic Target Recognition Using Passive Radar and an EKF for Estimating Aircraft Orientation

Rather than emitting pulses, passive radar systems rely on illuminators of opportunity, such as TV and FM radio, to illuminate potential targets. These systems are attractive since they allow receivers to operate without emitting energy, rendering them covert. Until recently, most of the research regarding passive radar has focused on detecting and tracking targets. This dissertation focuses on extending the capabilities of passive radar systems to include automatic target recognition. The target recognition algorithm described in this dissertation uses the radar cross section (RCS) of potential targets, collected over a short period of time, as the key information for target recognition. To make the simulated RCS as accurate as possible, the received signal model accounts for aircraft position and orientation, propagation losses, and antenna gain patterns. An extended Kalman filter (EKF) estimates the target's orientation (and uncertainty in the estimate) from velocity measurements obtained from the passive radar tracker. Coupling the aircraft orientation and state with the known antenna locations permits computation of the incident and observed azimuth and elevation angles. The Fast Illinois Solver Code (FISC) simulates the RCS of potential target classes as a function of these angles. Thus, the approximated incident and observed angles allow the appropriate RCS to be extracted from a database of FISC results. Using this process, the RCS of each aircraft in the target class is simulated as though each is executing the same maneuver as the target detected by the system. Two additional scaling processes are required to transform the RCS into a power profile (magnitude only) simulating the signal in the receiver. First, the RCS is scaled by the Advanced Refractive Effects Prediction System (AREPS) code to account for propagation losses that occur as functions of altitude and range. Then, the Numerical Electromagnetic Code (NEC2) computes the antenna gain pattern, further scaling the RCS. A Rician likelihood model compares the scaled RCS of the illuminated aircraft with those of the potential targets. To improve the robustness of the result, the algorithm jointly optimizes over feasible orientation profiles and target types via dynamic programming.Ph.D.Committee Chair: Lanterman, Aaron; Committee Member: McLaughlin, Steve; Committee Member: Richards, Mark; Committee Member: Serban, Nicoleta; Committee Member: Verriest, Eri

Automatic target recognition using passive radar and a coordinated flight model

M.S.Committee Chair: Aaron Lanterma

A Robust Algorithm for Automated Target Recognition

Passive radar is an emerging technology that o#ers a number of unique benefits, including covert operation. Many such systems are already capable of detecting and tracking aircraft. The goal of this work is to develop a robust algorithm for adding automated target recognition (ATR) capabilities to existing passive radar systems. In previous papers, 1, 2 we proposed conducting ATR by comparing the precomputed RCS of known targets to that of detected targets. To make the precomputed RCS as accurate as possible, a coordinated flight model is used to estimate aircraft orientation. Once the aircraft's position and orientation are known, it is possible to determine the incident and observed angles on the aircraft, relative to the transmitter and receiver. This makes it possible to extract the appropriate radar cross section (RCS) from our simulated database. This RCS is then scaled to account for propagation losses and the receiver's antenna gain. A Rician likelihood model compares these expected signals from di#erent targets to the received target profile

Experimental evidence for evaporation/condensation nonuniform flow in a horizontal aerosol generator

The formation of deposition patterns in the cooling zone during operation of a horizontal evaporation/condensation nanoparticle generator was studied to obtain information about flow conditions during particle formation. Quartz reactor tubes were used together with a simple light attenuation measurement to characterize deposition as a function of axial location. Results for the onset and pattern of deposition for four different metals-indium, gallium, silver, and lead-were obtained, and size distributions for indium and gallium particle nanoparticles at different temperatures were measured. Distinct deposition bands could be observed resulting from vapor deposition, nanioparticle deposition, or a combination of both. The location or the bands varied with metal and evaporation temperature. Experimentally observed fluctuations in temperature, bimodal size distributions obtained at the highest furnace temperatures, as well as asymmetric deposition patterns suggested the How in the cooling portion of the generator is nonuniform, possibly as a result of buoyancy. These results are important for the design of nanoparticle generation systems, in that horizontal evaporation/condensation generators are often chosen on the basis of assumed simplicity with respect to flow, and this may not always be the case