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ABSTRACT: The time-difference of arrival techniques are adapted to locate networked enemy radars using a cooperative team of unmanned aerial vehicles. The team is engaged in deceiving the radars, which limits where they can fly and requires accurate radar positions to be known. Two time-differences of radar pulse arrivals at two vehicle pairs are used to localize one of the radars. An explicit solution for the radar position in polar coordinates is developed. The solution is first used for position estimation given ldquonoisyrdquo measurements, which shows that the vehicle trajectories significantly affect estimation accuracy. Analyzing the explicit solution leads to the angle rule, which gives the optimal vehicle configuration for the angle estimate. Analyzing the fisher Information matrix leads to the coordinate rule, which gives a different optimal configuration for the position estimate. A linearized time-varying model is also formulated and an extended Kalman filter applied. This estimation scheme is compared with the earlier one, with the second showing overall improvement in reducing the variance of the estimate.
IEEE Transactions on Control Systems Technology 10/2008; · 1.77 Impact Factor
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ABSTRACT: The underlying problem is how to cooperatively use unmanned aerial vehicles (UAVs) to generate a coherent phantom track for a radar network to make it track a vehicle that does not exist. The UAVs need to plan a phantom track that is feasible given challenging nonlinear constraints on speed and heading, and optimal in terms of their aggregate costs and some global costs on the phantom. Models are formulated for the UAVs, wind, and the phantom. We develop practical methods for guiding the phantom and UAVs by first using optimal control and then either 1) adding smooth penalty functions to the cost, or 2) using control parametrization. The first method is compared through simulations with the second, which deals with state constraints more efficiently. The results are useful as a benchmark and for comparing more heuristic cooperative control algorithms. Because of its performance and fast solution times, the second method may also be used online.
American Control Conference, 2007. ACC '07; 08/2007
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ABSTRACT: Standard TDOA (time-difference of arrival) estimation techniques are modified and applied to locate networked enemy radars using a cooperative team of unmanned electronic combat air vehicles (ECAVs). The team is engaged in deceiving the radars, which limits where the ECAVs can fly and requires accurate radar positions to be known. Two TDOA measurements of radar pulses taken by two ECAV pairs are used to estimate the position of the middle radar. A nonlinear system model for estimation is formulated and used to perform simulations with "noisy" TDOAs; a linearized time-varying model for straight nominal ECAV trajectories is derived from the nonlinear model. The choice of optimal ECAV trajectories and an observer to minimize the variance of the middle radar position - using the linearized model is addressed. Application of a time-varying Kalman filter to the linearized system shows drastic improvement in reducing the variance of position estimates when compared to the original nonlinear system via simulations.
American Control Conference, 2005. Proceedings of the 2005; 07/2005
