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

Abstract

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 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.

Detection of Moving Target in FM Broadcast-Based Passive Radar

“…According to (2), the targets detection is performed by searching the location of target peak, namely…”

“…Reference [2] [3] investigated the use of television and commercial FM radio signal to track and recognize airborne targets.…”

Cramer-Rao bounds for 2-D target shape estimation in nonlinear inverse scattering problems with application to passive radar

“…The measurements of the scattered wave for the th detector are corrupted by additive noise (5) where are independent zero mean complex Gaussian random variables with covariance matrix (6) where denotes the variance of real and imaginary components of the th complex Gaussian random variable . This noise model applies when the thermal noise of receivers dominates the measurement noise, or when the background noise is spatially homogeneous and Gaussian, 1 and the receivers are sufficiently separated to decorrelate the noise contributions.…”

“…A total of 720 quadrature points are used for the discretization of the integral equation by the Nyström method. 5 The value of is computed at every 1 angle. The total computation time for 360 values of the was about 5 min.…”

“…Passive radar imaging, which is the main motivation for our study, is a nonlinear inverse scattering problem. Unlike traditional radar systems which transmit waveforms and deduce information about targets by measuring and analyzing the reflected signals, passive radar systems use "illuminators of opportunity" such as commercial television and AM/FM radio broadcasts to acquire target information [4], [5]. However, the waveforms used in commercial broadcasts have much lower frequencies compared to traditional active radar systems [5], [6].…”