We develop new algorithms for target detection in multi-sensor environments. These methods are applied to study point vortex motion based on Lagrangian tracer information. First we solve analytically the nonlinear filtering problem for the special case of equal strength vortices. Recently developed methods, the particle filters that are based on importance sampling Monte Carlo simulations, are used for the detection of vortices in the the general case. Unlike the well-known extended Kalman filter, it is applicable to highly nonlinear systems with non-Gaussian uncertainties.