Pattern-theoretic foundations of automatic target recognition in clutter

Issued as final reportAir Force Office of Scientific Research (U.S.

Schwarz, Wallace, and Rissanen: Intertwining Themes in Theories of Model Selection

Investigators interested in model order estimation have tended to divide themselves into widely separated camps; this survey of the contributions of Schwarz, Wallace, Rissanen, and their coworkers attempts to build bridges between the various viewpoints, illuminating connections which may have previously gone unnoticed and clarifying misconceptions which seem to have propagated in the applied literature. Our tour begins with Schwarz's approximation of Bayesian integrals via Laplace's method. We then introduce the concepts underlying Rissanen 's minimum description length principle via a Bayesian scenario with a known prior; this provides the groundwork for understanding his more complex non-Bayesian MDL which employs a "universal" encoding of the integers. Rissanen's method of parameter truncation is contrasted with that employed in various versions of Wallace's minimum message length criteria

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 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..

Statistical Radar Imaging of Diffuse and Specular Targets Using an Expectation-Maximization Algorithm

where K = # # + N 0 I . Enjoys usual properties of EM algorithms ----- Likelihood increases at each iteration ----- Iterates guaranteed to be nonnegative # # What About Specular Imaging? . Specular r is vastly more complicated ----- Not aware of a closed form for the density on r . If the columns of # have a su#cient non-zero entries: ----- r consists of sums of indep. 0-mean random variables ----- By CLT, marginals on r approx. 0-mean Gaussian ----- r "almost Gaussian" in the spirit of Mallows . Motivates trying the di#use EM algorithm on the specular case # # Phantoms for Simulations . Three point scatterers: . Rotating sphere: # # Transmitted Waveform . Specular realization: x 0 50 100 150 200 250 300 350 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 Transmitted waveform Time samples . Autocorrelation: -400-300-200-100 0 100 200 300 400 -100 -50 0 50 100 150 200 250 300 350 Time samples Autocorrelation of transmitted waveform # # Data from T

Minimum Description Length Understanding of Infrared Scenes

Our pattern theoretic approach to automatic target recognition for infrared scenes combines structured and unstructured representations: rigid, 3-D faceted models for known targets of interest and flexible, simply connected shapes to accommodate the unknown "clutterers" that the algorithm may encounter. The radiant intensities of both kinds of targets form nuisance variables which are incorporated into the parameter space. Statistical inference proceeds by simulating hypothesized scenes and comparing them to the collected data via a likelihood function. For a given target pose, we derive closed-form expressions for estimates of the thermodynamic variables via a weighted least-squares approximation. Since the number of objects in the scene, both rigid and flexible, is unknown and must be estimated, the parameter space is a union of subspaces of varying dimension. Without constraints on the model order, scene descriptions may become too complex. We apply Rissanen's minimum description le..