Tracking and recognition of airborne targets via commercial television and FM radio signals

We formulate a Bayesian approach to the joint tracking and recognition of airborne targets via reflected commercial television and FM radio signals measured by an array of sensors. Such passive systems may remain covert, whereas traditional active systems must reveal their presence and location by their transmissions. Since the number of aircraft in the scene is not known a priori, and targets may enter and leave the scene at unknown times, the parameter space is a union of subspaces of varying dimension as well as varying target classes. Targets tracks are parameterized via both positions and orientations, with the orientations naturally represented as elements of the special orthogonal group SO(3). A prior on target tracks is constructed from Ne Newtonian equations of motion. This prior results in a coupling between the position and orientation estimates, yielding a coupling between the tracking and recognition problems. A likelihood function is formulated which incorporates the sensor array geometry, the doppler shift, associated with moving targets, the frequency and angle dependent complex reflectance of the particular target types, and the energy loss predicted by the radar equation. The likelihood and prior are combined to form a posterior distribution. Monte Carlo random sampling algorithms are proposed for exploring the posterior distribution; these methods avoid the need for linearizations associated with extended Kalman filtering or explicit representations of the density required by traditional nonlinear filters.